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Xu HJ, Yang Q, He P, Luo HH, Deng WM, Liu Z, Luo DH. [Value of radiomics models based on MRI diffusion weighted imaging and apparent diffusion coefficient in differentiating benign and malignant thyroid nodules]. Zhonghua Yi Xue Za Zhi 2023; 103:3279-3286. [PMID: 37926572 DOI: 10.3760/cma.j.cn112137-20230913-00453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/07/2023]
Abstract
Objective: To investigate the value of radiomics models based on magnetic resonance imaging (MRI) diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps in distinguishing benign and malignant thyroid nodules. Methods: A cross-sectional study. Clinical data of 148 thyroid nodules (50 benign, 98 malignant) from 140 patients who underwent thyroid MRI examination in Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences between January 2019 and December 2022 were retrospectively analyzed. The nodules were used as the study units, and a leave-one-out method was used to randomly divide the nodules into a training set and a test set at a 7∶3 ratio. Region of interest was segmented and radiomics features were extracted from the DWI and ADC images. In the training set, feature selection was performed using inter-observer agreement analysis, U-test, least absolute shrinkage and selection operator algorithm, and correlation analysis. Four classifiers, including support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN) and logistic regression (LR) were used to build models with the selected features, including the DWI models, ADC models, and combined models. The models were independently tested in the test set. The performance of the radiomics models in distinguishing benign and malignant thyroid nodules was evaluated using the receiver operating characteristic (ROC) curve, with pathological results as the gold standard. Results: Of the 140 patients, there were 40 males and 100 females, with a mean age of (38.4±12.2) years. After feature selection, 11 DWI features and 11 ADC features were used to build the models. In the training set, the AUC values of the combined models were higher than those of the corresponding DWI and ADC models. In the test set, the SVM combined model showed the best predictive performance, with an AUC of 0.873 (95%CI:0.740-0.954), accuracy of 75.6%, sensitivity of 46.7%, specificity of 90.0%, positive predictive value (PPV) of 70.0% and negative predictive value (NPV) of 77.1%, while the RF combined model had an AUC of 0.836 (95%CI:0.695-0.929), accuracy of 77.8%, sensitivity of 40.0%, specificity of 96.7%, PPV of 85.7% and NPV of 76.3%, the KNN combined model had an AUC of 0.832 (95%CI:0.691-0.927), accuracy of 77.8%, sensitivity of 33.3%, specificity of 100%, PPV of 100% and NPV of 75.0%, the LR combined model had an AUC of 0.813 (95%CI:0.669-0.914), accuracy of 77.8%, sensitivity of 60.0%, specificity of 86.7%, PPV of 69.2% and NPV of 81.3%. Conclusions: Radiomics models based on DWI and ADC image features can effectively distinguish benign and malignant thyroid nodules. The SVM combined model had the best prediction performance.
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Affiliation(s)
- H J Xu
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - Q Yang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - P He
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - H H Luo
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - W M Deng
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - Z Liu
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
| | - D H Luo
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China
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52
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Chen ZF, Liu Z. [Radiation-induced intestinal fibrosis: pathological assessment and pharmacological prevention]. Zhonghua Wei Chang Wai Ke Za Zhi 2023; 26:935-939. [PMID: 37849263 DOI: 10.3760/cma.j.cn441530-20230816-00050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Abstract
Although radiotherapy can improve the local control rate of tumors and prolong the survival period of patients, it can also lead to long-term adverse effects such as radiation-induced intestinal fibrosis. Radiation-induced intestinal fibrosis has a high incidence and poses significant challenges to treatment, severely impacting the quality of life of patients. Combining findings from domestic and international research, along with experiences of our center, this article mainly discusses the pathological changes of radiation-induced intestinal fibrosis, as well as the current status and challenges of pathological assessment and pharmacological prevention of this condition. At present, there is no definitive method to reverse the fibrotic pathological changes. Thus, the prevention of fibrosis is a crucial issue to be resolved. In the meantime, there is a lack of ideal assessment methods and effective preventive medications in clinical practice. It is necessary to enhance both basic and clinical research, thoroughly investigate the pathogenesis of the disease, and identify effective intervention targets to promote the diagnosis and treatment of radiation-induced intestinal fibrosis.
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Affiliation(s)
- Z F Chen
- Department of Colorectal Surgery, Union Hospital, Fujian Medical University, Fuzhou 350001, China
| | - Z Liu
- Department of Colorectal Surgery, Union Hospital, Fujian Medical University, Fuzhou 350001, China
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Walmsley T, Unwin J, Allum F, Bari S, Boll R, Borne K, Brouard M, Bucksbaum P, Ekanayake N, Erk B, Forbes R, Howard AJ, Eng-Johnsson P, Lee JWL, Liu Z, Manschwetus B, Mason R, Passow C, Peschel J, Rivas D, Rolles D, Rörig A, Rouzée A, Vallance C, Ziaee F, Burt M. Characterizing the multi-dimensional reaction dynamics of dihalomethanes using XUV-induced Coulomb explosion imaging. J Chem Phys 2023; 159:144302. [PMID: 37823458 DOI: 10.1063/5.0172749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 09/27/2023] [Indexed: 10/13/2023] Open
Abstract
Site-selective probing of iodine 4d orbitals at 13.1 nm was used to characterize the photolysis of CH2I2 and CH2BrI initiated at 202.5 nm. Time-dependent fragment ion momenta were recorded using Coulomb explosion imaging mass spectrometry and used to determine the structural dynamics of the dissociating molecules. Correlations between these fragment momenta, as well as the onset times of electron transfer reactions between them, indicate that each molecule can undergo neutral three-body photolysis. For CH2I2, the structural evolution of the neutral molecule was simultaneously characterized along the C-I and I-C-I coordinates, demonstrating the sensitivity of these measurements to nuclear motion along multiple degrees of freedom.
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Affiliation(s)
- T Walmsley
- Chemistry Research Laboratory, Department of Chemistry, University of Oxford, Oxford OX1 3TA, United Kingdom
| | - J Unwin
- Chemistry Research Laboratory, Department of Chemistry, University of Oxford, Oxford OX1 3TA, United Kingdom
| | - F Allum
- Chemistry Research Laboratory, Department of Chemistry, University of Oxford, Oxford OX1 3TA, United Kingdom
| | - S Bari
- Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany
| | - R Boll
- European XFEL, Holzkoppel 4, 22869 Schenefeld, Germany
| | - K Borne
- J. R. Macdonald Laboratory, Department of Physics, Kansas State University, Manhattan, Kansas 66506, USA
| | - M Brouard
- Chemistry Research Laboratory, Department of Chemistry, University of Oxford, Oxford OX1 3TA, United Kingdom
| | - P Bucksbaum
- Stanford PULSE Institute, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, California 94025, USA
| | - N Ekanayake
- Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany
| | - B Erk
- Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany
| | - R Forbes
- Stanford PULSE Institute, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, California 94025, USA
- Linac Coherent Light Source, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, California 94025, USA
| | - A J Howard
- Stanford PULSE Institute, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, California 94025, USA
| | - P Eng-Johnsson
- Department of Physics, Lund University, 22100 Lund, Sweden
| | - J W L Lee
- Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany
| | - Z Liu
- Chemistry Research Laboratory, Department of Chemistry, University of Oxford, Oxford OX1 3TA, United Kingdom
| | - B Manschwetus
- Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany
| | - R Mason
- Chemistry Research Laboratory, Department of Chemistry, University of Oxford, Oxford OX1 3TA, United Kingdom
| | - C Passow
- Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany
| | - J Peschel
- Department of Physics, Lund University, 22100 Lund, Sweden
| | - D Rivas
- European XFEL, Holzkoppel 4, 22869 Schenefeld, Germany
| | - D Rolles
- J. R. Macdonald Laboratory, Department of Physics, Kansas State University, Manhattan, Kansas 66506, USA
| | - A Rörig
- European XFEL, Holzkoppel 4, 22869 Schenefeld, Germany
| | - A Rouzée
- Max-Born-Institute, Max-Born-Straße 2A, 12489 Berlin, Germany
| | - C Vallance
- Chemistry Research Laboratory, Department of Chemistry, University of Oxford, Oxford OX1 3TA, United Kingdom
| | - F Ziaee
- J. R. Macdonald Laboratory, Department of Physics, Kansas State University, Manhattan, Kansas 66506, USA
| | - M Burt
- Chemistry Research Laboratory, Department of Chemistry, University of Oxford, Oxford OX1 3TA, United Kingdom
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Ma J, Liu N, Liu Z, Liu Q, Wei F, Wang Z. [Epidemiology of pathogenic tick-borne viruses in China: a review]. Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi 2023; 35:325-330. [PMID: 37926466 DOI: 10.16250/j.32.1374.2023128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/07/2023]
Abstract
Ticks are obligate, haematophagous arthropods that are distributed across the world, which may transmit more than 200 pathogens, including viruses, bacteria and parasites. A large number of tick species are widespread in China, and their transmitting tick-borne viral diseases pose a great threat to human health in endemic foci. This review describes the epidemiology of common, emerging and potentially pathogenic tick-borne viruses in China, and recommends the assessment of public health significance and pathogenicity of emerging tick-borne viruses using reverse microbial etiology, so as to provide insights into the management of emerging tick-borne diseases in China.
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Affiliation(s)
- J Ma
- College of Life Science, Jilin Agricultural University, Changchun, Jilin 130021, China
| | - N Liu
- Research Center for Infectious Diseases and Pathogenic Biology, The First Hospital of Jilin University, Changchun, Jilin 130021, China
| | - Z Liu
- Research Center for Infectious Diseases and Pathogenic Biology, The First Hospital of Jilin University, Changchun, Jilin 130021, China
| | - Q Liu
- Research Center for Infectious Diseases and Pathogenic Biology, The First Hospital of Jilin University, Changchun, Jilin 130021, China
| | - F Wei
- College of Life Science, Jilin Agricultural University, Changchun, Jilin 130021, China
| | - Z Wang
- Research Center for Infectious Diseases and Pathogenic Biology, The First Hospital of Jilin University, Changchun, Jilin 130021, China
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Wang G, Liu Z. Relationship between hospital size, remoteness and stroke outcome. QJM 2023; 116:819. [PMID: 37498554 DOI: 10.1093/qjmed/hcad183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Indexed: 07/28/2023] Open
Affiliation(s)
- G Wang
- Department of Rheumatology and Immunology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China
| | - Z Liu
- Department of Rheumatology and Immunology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China
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Wu M, Chen D, Liu Z, Chen M, Liu R, Wang J, Li X, Tao Q, Yu J. Metformin Antagonizes Radiotherapy-Induced Anti-Tumor Effects via Inhibition of cGAS-STING Pathway Mediated Immune Responses. Int J Radiat Oncol Biol Phys 2023; 117:e268. [PMID: 37785015 DOI: 10.1016/j.ijrobp.2023.06.1230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Radiotherapy induced anti-tumor effects depend on both direct tumor cell death caused by radiation and immune activation mediated by cGAS-STING pathway. Metformin (MTF), which could augment the tumoricidal efficiency of radiation, is indicated to be a radiosensitizer by basic research. However, several large prospective clinical trials proved otherwise. In present study, we intend to interrogate the effects of MTF on radiotherapy-induced anti-tumor immune responses and try to explain the inconsistent outcomings of radiotherapy combined with MTF in basic research and clinical practice. MATERIALS/METHODS To explore the effects of MTF on radiotherapy induced anti-tumor effects, tumor models were established using E0771, B16F10 and LLC cell lines in both immunocompetent and immunodeficient mice. To investigate the composition and function of immune cells in tumor microenvironments, single-cell transcriptome sequencing of CD45+ cells sorted from tumor microenvironments were carried out, and flow cytometry and multiple immunofluorescence analysis were then performed for validation. To reveal the possible mechanisms, tumor cells were subjected to radiotherapy in the presence or absence of MTF in vitro, and RNA-sequencing was then employed followed by subsequent validation with western blotting, real-time qPCR and flow cytometry. RESULTS We found that systematic administration of MTF could significantly inhibit radiotherapy-induced anti-tumor effects in immunocompetent mouse models. Single cell sequencing of CD45+ cells sorted from tumor microenvironments and further validation showed that administration of MTF dramatically attenuated the infiltration and cytotoxic capacity of CD8+ T cells after radiotherapy. cGAS-STING pathway in tumor cells was required for maximum efficiency of radiotherapy, while MTF curbed cGAS-STING pathway after radiotherapy in a dose-dependent pattern by enhancing autophagy and reducing cytoplasmic mitochondrial DNA accumulation, which contributed to compromised anti-tumor effects. CONCLUSION Our findings indicated that MTF could antagonize radiotherapy-mediated anti-tumor effects by inhibiting the activation of cGAS-STING pathway and subsequent immune responses, which may partially explain the unsatisfied outcomes of radiotherapy combined with MTF in clinical practices. Since the anti-tumor effects of radiotherapy rely not only on the tumor-killing efficiency of radiation but also on systematic immune responses, our findings suggest that cautions are needed when MTF is administrated with radiotherapy in clinical practice.
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Affiliation(s)
- M Wu
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - D Chen
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Z Liu
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - M Chen
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - R Liu
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - J Wang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - X Li
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Q Tao
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - J Yu
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
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Furuhama A, Kitazawa A, Yao J, Matos Dos Santos CE, Rathman J, Yang C, Ribeiro JV, Cross K, Myatt G, Raitano G, Benfenati E, Jeliazkova N, Saiakhov R, Chakravarti S, Foster RS, Bossa C, Battistelli CL, Benigni R, Sawada T, Wasada H, Hashimoto T, Wu M, Barzilay R, Daga PR, Clark RD, Mestres J, Montero A, Gregori-Puigjané E, Petkov P, Ivanova H, Mekenyan O, Matthews S, Guan D, Spicer J, Lui R, Uesawa Y, Kurosaki K, Matsuzaka Y, Sasaki S, Cronin MTD, Belfield SJ, Firman JW, Spînu N, Qiu M, Keca JM, Gini G, Li T, Tong W, Hong H, Liu Z, Igarashi Y, Yamada H, Sugiyama KI, Honma M. Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project. SAR QSAR Environ Res 2023; 34:983-1001. [PMID: 38047445 DOI: 10.1080/1062936x.2023.2284902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 11/13/2023] [Indexed: 12/05/2023]
Abstract
Quantitative structure-activity relationship (QSAR) models are powerful in silico tools for predicting the mutagenicity of unstable compounds, impurities and metabolites that are difficult to examine using the Ames test. Ideally, Ames/QSAR models for regulatory use should demonstrate high sensitivity, low false-negative rate and wide coverage of chemical space. To promote superior model development, the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan (DGM/NIHS), conducted the Second Ames/QSAR International Challenge Project (2020-2022) as a successor to the First Project (2014-2017), with 21 teams from 11 countries participating. The DGM/NIHS provided a curated training dataset of approximately 12,000 chemicals and a trial dataset of approximately 1,600 chemicals, and each participating team predicted the Ames mutagenicity of each trial chemical using various Ames/QSAR models. The DGM/NIHS then provided the Ames test results for trial chemicals to assist in model improvement. Although overall model performance on the Second Project was not superior to that on the First, models from the eight teams participating in both projects achieved higher sensitivity than models from teams participating in only the Second Project. Thus, these evaluations have facilitated the development of QSAR models.
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Affiliation(s)
- A Furuhama
- Division of Genetics and Mutagenesis (DGM), National Institute of Health Sciences (NIHS), Kawasaki, Japan
| | - A Kitazawa
- Division of Genetics and Mutagenesis (DGM), National Institute of Health Sciences (NIHS), Kawasaki, Japan
| | - J Yao
- Key Laboratory of Fluorine and Nitrogen Chemistry and Advanced Materials (Chinese Academy of Sciences), Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences (SIOC, CAS), Shanghai, China
| | - C E Matos Dos Santos
- Department of Computational Toxicology and In Silico Innovations, Altox Ltd, São Paulo-SP, Brazil
| | - J Rathman
- MN-AM, Nuremberg, Germany/Columbus, OH, USA
| | - C Yang
- MN-AM, Nuremberg, Germany/Columbus, OH, USA
| | | | - K Cross
- In Silico Department, Instem, Conshohocken, PA, USA
| | - G Myatt
- In Silico Department, Instem, Conshohocken, PA, USA
| | - G Raitano
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS (IRFMN), Milano, Italy
| | - E Benfenati
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS (IRFMN), Milano, Italy
| | | | | | | | | | - C Bossa
- Environment and Health Department, Istituto Superiore di Sanità (ISS), Rome, Italy
| | - C Laura Battistelli
- Environment and Health Department, Istituto Superiore di Sanità (ISS), Rome, Italy
| | - R Benigni
- Environment and Health Department, Istituto Superiore di Sanità (ISS), Rome, Italy
- Alpha-PreTox, Rome, Italy
| | - T Sawada
- Faculty of Regional Studies, Gifu University, Gifu, Japan
- xenoBiotic Inc, Gifu, Japan
| | - H Wasada
- Faculty of Regional Studies, Gifu University, Gifu, Japan
| | - T Hashimoto
- Faculty of Regional Studies, Gifu University, Gifu, Japan
| | - M Wu
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - R Barzilay
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - P R Daga
- Simulations Plus, Lancaster, CA, USA
| | - R D Clark
- Simulations Plus, Lancaster, CA, USA
| | | | | | | | - P Petkov
- LMC - Bourgas University, Bourgas, Bulgaria
| | - H Ivanova
- LMC - Bourgas University, Bourgas, Bulgaria
| | - O Mekenyan
- LMC - Bourgas University, Bourgas, Bulgaria
| | - S Matthews
- Computational Pharmacology & Toxicology Laboratory, Discipline of Pharmacology, School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - D Guan
- Computational Pharmacology & Toxicology Laboratory, Discipline of Pharmacology, School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - J Spicer
- Computational Pharmacology & Toxicology Laboratory, Discipline of Pharmacology, School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - R Lui
- Computational Pharmacology & Toxicology Laboratory, Discipline of Pharmacology, School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Y Uesawa
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo, Japan
| | - K Kurosaki
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo, Japan
| | - Y Matsuzaka
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo, Japan
| | - S Sasaki
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo, Japan
| | - M T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
| | - S J Belfield
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
| | - J W Firman
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
| | - N Spînu
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
| | - M Qiu
- Evergreen AI, Inc, Toronto, Canada
| | - J M Keca
- Evergreen AI, Inc, Toronto, Canada
| | - G Gini
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milano, Italy
| | - T Li
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration (NCTR/FDA), Jefferson, AR, USA
| | - W Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration (NCTR/FDA), Jefferson, AR, USA
| | - H Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration (NCTR/FDA), Jefferson, AR, USA
| | - Z Liu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration (NCTR/FDA), Jefferson, AR, USA
- Integrative Toxicology, Nonclinical Drug Safety, Boehringer Ingelheim Pharmaceuticals, Inc, Ridgefield, CT, USA
| | - Y Igarashi
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Osaka, Japan
| | - H Yamada
- Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), Osaka, Japan
| | - K-I Sugiyama
- Division of Genetics and Mutagenesis (DGM), National Institute of Health Sciences (NIHS), Kawasaki, Japan
| | - M Honma
- Division of Genetics and Mutagenesis (DGM), National Institute of Health Sciences (NIHS), Kawasaki, Japan
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Xue J, Shi R, Ma J, Liu Z, Feng G, Chen QQ, Li Y, He Y, Ji S, Shi J, Zhu X, Zhou J. Concurrent Chemoradiotherapy plus Programmed Death-1 (PD-1) Blockade for Locally Advanced Cervical Cancer: Preliminary Results of a Single-Arm, Open-Label, Phase II Trial. Int J Radiat Oncol Biol Phys 2023; 117:e542-e543. [PMID: 37785675 DOI: 10.1016/j.ijrobp.2023.06.1838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) This study aims to assess the anti-tumor activity and safety of concurrent chemoradiotherapy plus PD-1 blockade in patients with locally advanced cervical cancer. MATERIALS/METHODS This is a single-arm, open-label, prospective phase II study. The key inclusion criteria were treatment-naive patients aged 18-75 years with stage II A2-IVA (FIGO 2018) locally advanced cervical cancer. All patients were treated with concurrent chemoradiotherapy including 2 cycle cisplatin (75mg/m2, for three days, every 3 weeks[Q3W]), nedaplatin or carboplatin can be selected for patients who can't tolerate cisplatin. After CCRT, patients achieving complete response (CR), partial responses(PR), stable disease(SD) received adjuvant chemotherapy (docetaxel 75 mg/m2 day 1+ cisplatin DDP 25 mg/m2 day 1-3, Q3W) for 2 cycle. PD-1 blockade Sintilimab and Tislelizumab was administered intravenously at 200 mg every 3 weeks up to 1 year or until disease progression, unacceptable toxicity, or withdrawal of consent. The primary endpoint was objective response rate (ORR) assessed by investigators per Response Evaluation Criteria In Solid Tumours (RECIST) version 1.1. Secondary endpoints were the 12, 24-month overall survival (OS) rates, the 12, 24-month disease free survival (DFS) rates and safety. RESULTS From February 2020 to June 2022, a total of 15 patients was enrolled. Median age was 57 years (range, 36-74 years). Stage IIA1 was documented in 2 patients, stage IIA2 in two patients, stage IIIA in one patient, stage IIIC1 in eight patients, and stage IVA in two patients. And 66.7% (10/15) of patients had Metastatic lymph node. Four patients received adjuvant chemotherapy. The ORR was 100%, with 4 patients achieving CR and 11 PR. The 12 and 24-month OS rates are 93.3% and 84%, the 12 and 24-month DFS rates are 86% and 75.4%, respectively. Treatment-related adverse events (TRAEs) occurred in 86.7% (13/15) of patients. Grade 3 TRAEs are leukocyte (n = 1), thrombocytopenia (n = 1), hepatitis (n = 1), skin reaction (n = 1). No treatment-related deaths occurred. And IFN-γ was significantly elevated after radiotherapy (p = 0.0073). CONCLUSION Concurrent chemoradiotherapy plus PD-1 blockade showed promising antitumor activity and manageable toxicities in patients with locally advanced cervical cancer. Long-term outcomes are still pending to further evaluate their therapeutic effects. (ChiCTR2000032856).
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Affiliation(s)
- J Xue
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215001, China., Suzhou, China
| | - R Shi
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215001, China., Suzhou, China
| | - J Ma
- The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Z Liu
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215001, China., Suzhou, China
| | - G Feng
- Department of Gynecology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215001, China., Suzhou, China
| | - Q Q Chen
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - Y Li
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215001, China., Suzhou, China
| | - Y He
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215001, China., Suzhou, China
| | - S Ji
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
| | - J Shi
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215001, China., Suzhou, China
| | - X Zhu
- Department of Radiation Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, 215001, China., Suzhou, China
| | - J Zhou
- Department of Radiotherapy Oncology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
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Li S, Zhu X, Song M, Xiang Y, Zhang Y, Wang HZ, Geng J, Liu Z, Teng H, Cai Y, Li Y, Wang W. Outcomes and Failure Patterns after Chemoradiotherapy for Locally Advanced Rectal Cancer with Positive Lateral Pelvic Lymph Nodes: A Propensity Score-Matched Analysis. Int J Radiat Oncol Biol Phys 2023; 117:e314. [PMID: 37785131 DOI: 10.1016/j.ijrobp.2023.06.2345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Locally advanced rectal cancer (LARC) combined with positive lateral pelvic lymph nodes (LPLN) tends to present worse prognosis. However, for those patients it remains unclear whether other combination high-risk factors affect the prognosis. This study aimed to use propensity score matching (PSM) to examine long-term outcomes and failure patterns in patients with positive vs. negative LPLN. MATERIALS/METHODS Patients with LARC were retrospectively divided into LPLN-positive and LPLN-negative groups. LPLN-positivity was defined as lymph node short diameter greater than or equal to 7 mm with specific morphological features. Clinical characteristics were compared between the groups using the chi-square test. PSM was applied to balance these differences. Progression-free survival (PFS) and overall survival (OS), and local-regional recurrence (LRR) and distant metastasis (DM) rates were compared between the groups using the Kaplan-Meier method and log-rank tests. RESULTS Prior to PSM, a total of 651 LARC patients were included. The LPLN-positive group had higher rates of lower location (53.1% vs. 43.0%, P = 0.025), mesorectal fascia (MRF)-positive (53.9% vs. 35.4%, P<0.001) and extramural venous invasion (EMVI)-positive (51.2% vs. 27.2%, P<0.001) disease than the LPLN-negative group. After PSM, there were 114 patients for each group along with the balanced clinical factors, and both groups had comparable surgery, pathologic complete response (pCR), and ypN stage rates. The median follow-up time was 45.9 months, 3-year OS (88.3% vs. 92.1%, P = 0.276) and LRR (5.7% vs. 2.8%, P = 0.172) rates were comparable between LPLN-positive and LPLN-negative groups. Meanwhile, despite no statistical difference, 3-year PFS (78.8% vs. 85.9%, P = 0.065) and DM (20.4% vs. 13.3%, P = 0.061) rates slightly differed between the groups. Among 10 patients with LRR, seven (70.0%) had lateral pelvic recurrence, among them, five patients were LPLN-positive, and four (80.0%) of these patients did not receive simultaneous integrated boost intensity-modulated radiotherapy (SIB- IMRT).45 patients were diagnosed with DM, 11 (40.7%) LPLN-positive and 3 (17.6%) LPLN-negative patients were diagnosed with oligometastases (P = 0.109). CONCLUSION Our study shows there is a tendency of worse PFS and DM in LPLN-positive than LPLN-negative patients, for LPLN-positive patients, oligometastases account for a large proportion of all distant metastases.
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Affiliation(s)
- S Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - X Zhu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - M Song
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Y Xiang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Y Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - H Z Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - J Geng
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Z Liu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - H Teng
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Y Cai
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Y Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - W Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
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Wang SX, Yang Y, Xie H, Yang X, Liu Z, Li H, Huang W, Luo WJ, Lei Y, Sun Y, Ma J, Chen Y, Liu LZ, Mao YP. Delta-Radiomics Guides Adaptive De-Intensification after Induction Chemotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma in the IMRT Era. Int J Radiat Oncol Biol Phys 2023; 117:S152-S153. [PMID: 37784386 DOI: 10.1016/j.ijrobp.2023.06.574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) In the setting of intensity-modulated radiotherapy (IMRT) and induction chemotherapy (IC), the benefits from concurrent chemotherapy remained controversial for locoregionally advanced nasopharyngeal carcinoma (LANPC). This study aimed to construct a delta-radiomics model for benefit prediction and patient selection for omitting concurrent chemotherapy. MATERIALS/METHODS Between December 2009 and December 2015, a total of 718 patients with LANPC treated with IC+IMRT or IC+concurrent chemoradiotherapy (CCRT) were retrospectively enrolled and randomly assigned to a training set (n = 503) and a validation set (n = 215). Radiomic features were extracted from magnetic resonance images of pre-IC and post-IC. Interclass correlation coefficients and Pearson correlation coefficients were calculated to select robust radiomic features. After univariate Cox analysis, a delta-radiomics signature was built using the LASSO-Cox regression. A nomogram incorporating the delta-radiomics signature and clinical prognostic factors was then developed and evaluated for calibration and discrimination. Risk stratification by the nomogram was evaluated by Kaplan-Meier methods. The primary outcome was overall survival (OS). RESULTS The delta-radiomics signature, which comprised 19 selected features, was independently associated with prognosis. It yielded an area under the receiver operating characteristic curve (AUC) of 0.77 (95% confidence interval [CI] 0.71 to 0.82) for the training set and 0.71 (95% CI 0.61 to 0.81) for the validation set. The nomogram composed of the delta-radiomic signature, age, T category, N category, pre-treatment Epstein-Barr virus DNA, and treatment showed great calibration and discrimination performance with an AUC of 0.80 (95% CI 0.75 to 0.85) for the training set and 0.75 (95% CI 0.64 to 0.85) for the validation set. Risk stratification by the nomogram excluding the treatment variable resulted in two risk groups with distinct OS. Significant better outcomes were observed in the high-risk patients with IC+CCRT compared to those with IC+IMRT (5-year OS: 73.8% vs. 61.4% in the training set and 85.8% vs. 65.6% in the validation set; all log-rank p < 0.05), while comparable outcomes between IC+CCRT and IC+IMRT were shown for the low-risk patients (95.8% vs. 95.8% in the training set and 92.2% vs. 88.3% in the validation set; all log-rank p > 0.05). CONCLUSION The delta-radiomics signature was identified as an independent indicator of LANPC. Integrating clinical predictors with the delta-radiomics signature, the radiomics-based nomogram could predict individual's survival outcomes and benefits from concurrent chemotherapy after IC for LANPC. Low-risk patients with LANPC determined by the nomogram may be potential candidates for omission of concurrent chemotherapy following IC in the IMRT era.
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Affiliation(s)
- S X Wang
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Y Yang
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - H Xie
- Imaging Diagnosis and Interventional Center, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - X Yang
- Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, Guangzhou, China
| | - Z Liu
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - H Li
- Imaging Diagnosis and Interventional Center, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - W Huang
- Imaging Diagnosis and Interventional Center, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - W J Luo
- Department of Medical Oncology, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Y Lei
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Y Sun
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - J Ma
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Y Chen
- Department of head and neck surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - L Z Liu
- Imaging Diagnosis and Interventional Center, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Y P Mao
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
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Miller JA, Liu Z, Pinsky B, Le QT, Li T, Cao S, Hildesheim A. Local Cost-Effectiveness of Nasopharyngeal Carcinoma Screening Strategies in Southern China: Secondary Analysis of the PRO-NPC-001 Cluster-Randomized Trial. Int J Radiat Oncol Biol Phys 2023; 117:S70. [PMID: 37784557 DOI: 10.1016/j.ijrobp.2023.06.376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Population-based screening for endemic nasopharyngeal carcinoma (NPC) detects most cases at an early stage. In a cluster-randomized trial conducted in Guangdong, a combination of Epstein-Barr Virus (EBV) anti-VCA/EBNA1 IgA serology and endoscopy reduced NPC mortality. We conducted a secondary analysis of this trial in conjunction with local incidence and cost data, hypothesizing that screening would be cost-effective in this region. MATERIALS/METHODS We estimated population-level NPC mortality reduction, resource utilization, and cost-effectiveness of 12 unique screening strategies in six populations in Guangdong/Guangxi using a previously-validated time-inhomogeneous decision-analytic cohort model. These 12 strategies evaluated combinations of serology, nasopharyngeal swab PCR (NP PCR), endoscopy, and head/neck MRI. Incidence data, screening costs, and healthcare costs were obtained from local cancer registries, laboratories conducting ELISA/PCR, and the Guangdong provincial healthcare system. We evaluated variable screening ages, sexes, intervals, and durations to identify optimal screening approaches from the perspective of the healthcare system in southern China. An incremental cost-effectiveness ratio (ICER) willingness-to-pay threshold of 1.50 times the per-capita GDP was considered cost-effective in southern China. RESULTS For the base strategy screening 50-year-old men and women using only serology and endoscopy, the average cost per screened subject for a single round of screening over a five-year cycle was ¥175.69. The addition of MRI improved sensitivity (76% vs. 62%) and approximately doubled screening costs. Triage with NP PCR was cost-neutral when used in conjunction with MRI and reduced endoscopy/MRI utilization by 37% with a 3-4% reduction in screening sensitivity. Among 50-year-old men and women, screening was cost-effective in all populations provided that medium-risk subjects were not referred for endoscopy/MRI (ICER/GDP 0.62-0.83). The use of NP PCR without MRI (ICER/GDP 0.83) was dominated by the base strategy (ICER/GDP 0.62) due to higher costs and NPC mortality. After a single five-year screening cycle, screening reduced population NPC mortality by 14% with serology + endoscopy and 21% with serology + endoscopy + MRI. Introduction of MRI with or without NP PCR could be cost-effective in all populations. For MRI-based strategies, the most efficient use of resources was deferral of endoscopy unless MRI was abnormal (ICER/GDP 0.67). Overall, the best-performing strategies balanced NPC mortality, screening costs, and MRI utilization. CONCLUSION EBV serology-based screening for endemic NPC is likely to be cost-effective among adult men and women in Guangdong and Guangxi. Referring medium-risk subjects for endoscopy/MRI should be avoided, and NP PCR should be used to triage individuals for MRI rather than endoscopy. These data may aid the design of population-based screening programs in this region.
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Affiliation(s)
- J A Miller
- Department of Radiation Oncology, Cleveland Clinic, Cleveland, OH
| | - Z Liu
- Infections and Immunoepidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD
| | - B Pinsky
- Department of Pathology, Stanford University, Stanford, CA
| | | | - T Li
- Department of Cancer Prevention, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - S Cao
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
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Wang HZ, Zheng X, Sun J, Zhu X, Dong D, Du Y, Feng Z, Gong J, Wu H, Geng J, Li S, Song M, Zhang Y, Liu Z, Cai Y, Li Y, Wang W. 4D-MRI Guided Stereotactic Body Radiation Therapy for Unresectable Colorectal Liver Metastases. Int J Radiat Oncol Biol Phys 2023; 117:e359. [PMID: 37785235 DOI: 10.1016/j.ijrobp.2023.06.2445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) This study evaluated the feasibilities and outcomes following four-dimensional magnetic resonance imaging (4D-MRI) guided stereotactic body radiation therapy (SBRT) for unresectable colorectal liver metastases (CRLM). MATERIALS/METHODS From March 2018 to January 2022, we identified 76 unresectable CRLM patients with 123 lesions who received 4D-MRI guided SBRT in our institution. 4D-MRI simulation with or without abdominal compression was conducted for all patients. The prescription dose was 50-65 Gy in 5-12 fractions. The image quality of computed tomography (CT) and MRI were compared using the Clarity Score. Clinical outcomes and toxicity profiles were evaluated. RESULTS The 4D-MRI significantly improved the image quality compared with CT images (mean Clarity Score: 1.67 vs 2.88, P < 0.001). The abdominal compression significantly reduced motions in cranial-caudal direction (P = 0.03) with 2 phase T2 weighted images assessing tumor motion. The median follow-up time was 12.5 months. For 98 lesions assessed for best response, the complete response, partial response and stable disease rate were 57.1 %, 30.6 % and 12.2 %, respectively. The local control (LC) rate at 2 year was 97.3%. 46.1% of patients experienced grade 1-2 toxicities and only 2.6% patients experienced grade 3 hematologic toxicities. CONCLUSION The 4D-MRI technique allowed precise target delineation and motion tracking in unresectable CRLM patients. High LC rate and mild toxicities were achieved. This study provided evidence for using 4D-MRI guided SBRT as an alternative treatment in unresectable CRLM.
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Affiliation(s)
- H Z Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - X Zheng
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, Beijing, China
| | - J Sun
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, Beijing, China
| | - X Zhu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - D Dong
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, Beijing, China
| | - Y Du
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, Beijing, China
| | - Z Feng
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, Beijing, China
| | - J Gong
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, Beijing, China
| | - H Wu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, Beijing, China
| | - J Geng
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - S Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - M Song
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Y Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Z Liu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Y Cai
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Y Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - W Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
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Zhang MQ, Subinuer M, Chen ZP, Cai J, Liu C, Li XQ, Liu Z, Qiao T. [Clinical analysis of surgical treatment of infection after interventional operation for major iliac artery disease in 6 cases]. Zhonghua Wai Ke Za Zhi 2023; 61:1007-1013. [PMID: 37767668 DOI: 10.3760/cma.j.cn112139-20230228-00087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
Objective: To explore the surgical treatment strategy of stent graft infection after interventional treatment of major iliac artery related diseases. Methods: Retrospective analysis was performed on the clinical data of 6 patients with secondary stent graft infection after interventional treatment for major iliac artery related diseases admitted to the Department of Vascular Surgery,Affiliated Drum Tower Hospital,Medical School of Nanjing University from November 2021 to August 2022.There were 5 males and 1 female,with a mean age of 64 years (range:49 to 79 years).The infection time was 53 to 3 165 days.All the 6 patients received surgical treatment,including 3 patients who underwent anatomic bypass grafting (axillary arterial-femoral artery bypass,femoral arterial-femoral artery bypass) using artificial vessels,and 3 patients who underwent in situ abdominal aorta reconstruction using bovine pericardium.The perioperative situation,postoperative infection and the occurrence of serious adverse events were recorded,and the safety of different treatment methods and materials was evaluated. Results: All patients successfully completed the operation and no death occurred during hospitalization.Intraoperative blood loss was 2 000~5 000 ml,and intraoperative blood transfusion was 1 600 to 5 350 ml.All the patients were followed up for 81 to 395 days after surgery,and the incision healed well,and no reinfection occurred.Postoperative gastrointestinal bleeding occurred in 1 patient,secondary surgery (retroperitoneal hematoma removal) was performed in 1 patient due to postoperative bleeding at the vascular anastomosis,both lower limb amputations were performed in 1 patient due to postoperative lower limb ischemia,and intermittent claudication occurred in 2 patients.All patients were alive at the last follow-up. Conclusion: For patients with aortic stent graft infection,when the infection is not serious and there is enough space to block the proximal and distal aorta,in situ aortic reconstruction is an effective treatment,and different materials can achieve satisfactory results in a short period of time.
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Affiliation(s)
- M Q Zhang
- Department of Vascular Surgery,Affiliated Drum Tower Hospital,Medical School of Nanjing University,Nanjing 210008,China
| | - Maimaitiaili Subinuer
- Department of Vascular Surgery,Affiliated Drum Tower Hospital,Medical School of Nanjing University,Nanjing 210008,China
| | - Z P Chen
- Department of Vascular Surgery,Affiliated Drum Tower Hospital,Medical School of Nanjing University,Nanjing 210008,China
| | - J Cai
- Department of Vascular Surgery,Affiliated Drum Tower Hospital,Medical School of Nanjing University,Nanjing 210008,China
| | - C Liu
- Department of Vascular Surgery,Affiliated Drum Tower Hospital,Medical School of Nanjing University,Nanjing 210008,China
| | - X Q Li
- Department of Vascular Surgery,Affiliated Drum Tower Hospital,Medical School of Nanjing University,Nanjing 210008,China
| | - Z Liu
- Department of Vascular Surgery,Affiliated Drum Tower Hospital,Medical School of Nanjing University,Nanjing 210008,China
| | - T Qiao
- Department of Vascular Surgery,Affiliated Drum Tower Hospital,Medical School of Nanjing University,Nanjing 210008,China
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Bao C, Wei M, Pan H, Wen M, Liu Z, Xu Y, Jiang H. A preliminary study for the clinical effect of one combinational physiotherapy and its potential influence on gut microbial composition in children with Tourette syndrome. Front Nutr 2023; 10:1184311. [PMID: 37781119 PMCID: PMC10541309 DOI: 10.3389/fnut.2023.1184311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 08/28/2023] [Indexed: 10/03/2023] Open
Abstract
Introduction Tourette syndrome (TS) is a chronic neuropsychiatric disorder with unknown causes and inadequate therapies. Inspired by the important roles of gut microbiota in some mental illnesses, the interactions between gut microbiota and TS via the gut-brain axis have gained more and more attention. This study aimed to characterize the gut microbial profiles in children with TS, and explore the clinical effects of one combinational physiotherapy and its potential influence on gut microbial composition. Methods The gut microbial profiles were depicted based on the sequence data of 32 patients and 29 matched health children by 16S rDNA amplicon pyrosequencing. Thirty of thirty-two patients underwent uninterrupted two 10-day courses of combinational physiotherapy, which included a 60-minute cranial electrotherapy stimulation (CES) training followed by a 30-minute biofeedback training per session, 2 sessions a day. Results Our results indicated that the gut microbial composition in children with TS was different from that in healthy controls. Multiple GBM neurotransmitter modules obtained through Picrust2 functional predictive analysis were significantly increased in patients, including Histamine degradation, Dopamine degradation, and DOPAC synthesis. Moreover, this combinational physiotherapy could significantly diminish tic activity, whose positive effects were first reported in children with TS. Lastly, different gut microbial compositions and predictive metabolic pathways were also observed between patients before and after this treatment, with lower abundances of the genera (e.g., Dorea) and significant decreases of GBM neurotransmitter modules (e.g. dopamine degradation) in patients after this treatment, indicating that improved clinical symptoms might be accompanied by an improvement of intestinal microenvironment. Discussion Children with TS showed a cognizable gut microbial profile, and certain enriched bacteria with pro-inflammatory potential might induce neuroinflammatory responses. This combinational physiotherapy could significantly diminish tic activity, and the gut microbial compositions in patients after this treatment were different from those without any treatment, indicating the existence of bidirectional communication of the gut-brain axis in TS. But studies on the gut microbial characteristics in TS patients, the influences of gut microbiota on tic severity, the efficacy and safety of this treatment, and the bidirectional regulatory mechanism between brain signals and gut microbiota in TS still need to be explored.
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Affiliation(s)
- Chun Bao
- Department of Child Healthcare, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Meng Wei
- Department of Child Healthcare, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Hongguo Pan
- Department of Child Healthcare, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Ming Wen
- Zhangjiang Center for Translational Medicine, Shanghai Biotecan Pharmaceuticals Co., Ltd., Shanghai, China
| | - Ziming Liu
- Zhangjiang Center for Translational Medicine, Shanghai Biotecan Pharmaceuticals Co., Ltd., Shanghai, China
| | - Yue Xu
- Zhangjiang Center for Translational Medicine, Shanghai Biotecan Pharmaceuticals Co., Ltd., Shanghai, China
| | - Huihui Jiang
- Zhangjiang Center for Translational Medicine, Shanghai Biotecan Pharmaceuticals Co., Ltd., Shanghai, China
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Zeng M, Yao Y, Liu Z. [Advances in the study of monitors and predictors of efficacy in allergen-specific immunotherapy for allergic rhinitis]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2023; 58:901-905. [PMID: 37675530 DOI: 10.3760/cma.j.cn115330-20230320-00127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Affiliation(s)
- M Zeng
- Department of Otorhinolaryngology Head and Neck Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan 430030, China
| | - Y Yao
- Department of Otorhinolaryngology Head and Neck Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan 430030, China
| | - Z Liu
- Department of Otorhinolaryngology Head and Neck Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan 430030, China
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Wang H, Fu T, Du Y, Gao W, Huang K, Liu Z, Chandak P, Liu S, Van Katwyk P, Deac A, Anandkumar A, Bergen K, Gomes CP, Ho S, Kohli P, Lasenby J, Leskovec J, Liu TY, Manrai A, Marks D, Ramsundar B, Song L, Sun J, Tang J, Veličković P, Welling M, Zhang L, Coley CW, Bengio Y, Zitnik M. Publisher Correction: Scientific discovery in the age of artificial intelligence. Nature 2023; 621:E33. [PMID: 37648871 DOI: 10.1038/s41586-023-06559-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Affiliation(s)
- Hanchen Wang
- Department of Engineering, University of Cambridge, Cambridge, UK
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
- Department of Research and Early Development, Genentech Inc, South San Francisco, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Tianfan Fu
- Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Yuanqi Du
- Department of Computer Science, Cornell University, Ithaca, NY, USA
| | - Wenhao Gao
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kexin Huang
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Ziming Liu
- Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Payal Chandak
- Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA, USA
| | - Shengchao Liu
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- Université de Montréal, Montreal, Quebec, Canada
| | - Peter Van Katwyk
- Department of Earth, Environmental and Planetary Sciences, Brown University, Providence, RI, USA
- Data Science Institute, Brown University, Providence, RI, USA
| | - Andreea Deac
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- Université de Montréal, Montreal, Quebec, Canada
| | - Anima Anandkumar
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
- NVIDIA, Santa Clara, CA, USA
| | - Karianne Bergen
- Department of Earth, Environmental and Planetary Sciences, Brown University, Providence, RI, USA
- Data Science Institute, Brown University, Providence, RI, USA
| | - Carla P Gomes
- Department of Computer Science, Cornell University, Ithaca, NY, USA
| | - Shirley Ho
- Center for Computational Astrophysics, Flatiron Institute, New York, NY, USA
- Department of Astrophysical Sciences, Princeton University, Princeton, NJ, USA
- Department of Physics, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Physics and Center for Data Science, New York University, New York, NY, USA
| | | | - Joan Lasenby
- Department of Engineering, University of Cambridge, Cambridge, UK
| | - Jure Leskovec
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | | | - Arjun Manrai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Debora Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Le Song
- BioMap, Beijing, China
- Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates
| | - Jimeng Sun
- University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Jian Tang
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- HEC Montréal, Montreal, Quebec, Canada
- CIFAR AI Chair, Toronto, Ontario, Canada
| | - Petar Veličković
- Google DeepMind, London, UK
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Max Welling
- University of Amsterdam, Amsterdam, Netherlands
- Microsoft Research Amsterdam, Amsterdam, Netherlands
| | - Linfeng Zhang
- DP Technology, Beijing, China
- AI for Science Institute, Beijing, China
| | - Connor W Coley
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yoshua Bengio
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- Université de Montréal, Montreal, Quebec, Canada
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Harvard Data Science Initiative, Cambridge, MA, USA.
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA, USA.
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Li M, Gao N, Wang S, Ding Y, Guo YF, Liu Z. A bibliometric analysis of Barrett's esophagus. Eur Rev Med Pharmacol Sci 2023; 27:8055-8073. [PMID: 37750634 DOI: 10.26355/eurrev_202309_33566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
OBJECTIVE Esophageal adenocarcinoma is known to have a high incidence and poor prognosis in the population and is a serious threat to public health. As a precancerous lesion of esophageal adenocarcinoma, early intervention of Barrett's esophagus is key to the prevention and treatment of esophageal adenocarcinoma. MATERIALS AND METHODS Research publications on Barrett's esophagus (BE) were searched in the Web of Science Core Collection, and the extracted publications were screened to obtain relevant data. The included articles were analyzed bibliometrically using Microsoft Excel 2019, Citespace V, and VOSviewer 1.6.18. The keywords used for the search can be categorized into 4 clusters: endoscopic therapy, clinical screening, risk factors, and drug therapy. RESULTS A total of 3,497 publications from 83 countries and 3,319 research institutions were retrieved. Since 1983, there has been a rapid increase in publications in this field. The United States (n = 1,941) and Mayo Clinic (n = 218) were the most productive countries and institutions, respectively, and the most prominent author was Kenneth K. Wang, who published 89 papers. CONCLUSIONS In this study, we were able to perform a comprehensive and systematic analysis of literature related to BE. Endoscopic resection and radiofrequency ablation may emerge as research hotspots for BE in the future. Our findings provide insight into the current trends in the management of BE and facilitate the choice of appropriate measures to improve the prognosis of patients.
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Affiliation(s)
- M Li
- Department of Gastroenterology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
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Chen XX, Zeng MX, Cai D, Zhou HH, Wang YJ, Liu Z. Correlation between APOE4 gene and gut microbiota in Alzheimer's disease. Benef Microbes 2023; 14:349-360. [PMID: 38661357 DOI: 10.1163/18762891-20220116] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 06/07/2023] [Indexed: 04/26/2024]
Abstract
Gut microbiota (GM) dysbiosis has been increasingly associated with Alzheimer's disease (AD). However, the association between APOE4, the most common genetic risk factor for sporadic AD, and GM in AD remains unclear. In this study, we conducted a comparative analysis of the GM of participants from China and the USA, with and without APOE4 genes and with or without AD (67 AD cases, 67 control cases). Our results revealed that the GM alpha diversity was not different between groups (AD_APOE4, Control_APOE4, AD_non-APOE4, and Control_non-APOE4) (419.031 ± 143.631 vs 391.091 ± 126.081, 351.086 ± 169.174 and 386.089 ± 177.200, respectively. P > 0.05). Interestingly, individuals in the AD_APOE4 group had different bacterial compositions and bacterial biomarkers. The Kruskal-Wallis rank sum test indicated that the abundances of many bacterial species in the AD_APOE4 patients differed from those in control individuals, including decreases in unclassified_g__Escherichia-Shigella (1.763 ± 6.73, 4.429 ± 11.13, 8.245 ± 16.55, and 5.69 ± 13.91 in four groups, respectively; P < 0.05), and unclassified_g_Clostridium_sensu_stricto_1 (0.1519 ± 0.348, 2.502 ± 5.913, 0.5146 ± 0.9487, 1.063 ± 3.428 in four groups, respectively; P < 0.05), and increases in gut_metagenome_g_Faecalibacterium (2.885 ± 4.47, 2.174 ± 3.957, 0.5765 ± 1.784, 1.582 ± 2.92 in four groups, respectively. P < 0.01) and unclassified_g_Bacteroides (3.875 ± 3.738, 2.47 ± 2.748, 2.046 ± 3.674, 3.206 ± 3.446 in four groups, respectively; P < 0.05). In the KEGG pathway level 2 analysis, we identified three significant differences in relative abundances of predicted functions between AD_APOE4 and AD_non-APOE4_carrier groups: neurodegenerative diseases (0.0007 ± 0.0005 vs 0.0009 ± 0.0004; P < 0.01), metabolism (0.0240 ± 0.0003 vs 0.0250 ± 0.0003; P < 0.05), and biosynthesis of other secondary metabolites (0.0094 ± 0.0002 vs 0.0090 ± 0.0002; P < 0.05). Receiver operating characteristic curves further demonstrated an area under the curve (AUC) of 0.74 for the discrimination of AD_APOE4_carrier and AD_non-APOE4_carrier individuals.
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Affiliation(s)
- X-X Chen
- Department of Neurology, Guangdong Key Laboratory of Age-Related Cardiac and Cerebral Diseases, Institute of Neurology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong Province, China P.R
| | - M-X Zeng
- Department of Neurology, Guangdong Key Laboratory of Age-Related Cardiac and Cerebral Diseases, Institute of Neurology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong Province, China P.R
| | - D Cai
- Clinical Skills Training Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong Province, China P.R
| | - H-H Zhou
- Department of Neurology, Guangdong Key Laboratory of Age-Related Cardiac and Cerebral Diseases, Institute of Neurology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong Province, China P.R
| | - Y-J Wang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China P.R
| | - Z Liu
- Department of Neurology, Guangdong Key Laboratory of Age-Related Cardiac and Cerebral Diseases, Institute of Neurology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong Province, China P.R
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Gong X, Liu Z, Qian G, Liu Z. Interlocking Evaluation of Mesoscopic Skeleton with the Compaction Degree of Hot-Mix Asphalt. Materials (Basel) 2023; 16:5879. [PMID: 37687572 PMCID: PMC10488679 DOI: 10.3390/ma16175879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/24/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023]
Abstract
Asphalt mixtures are multi-phase composites composed of aggregates, bitumen, mineral powders, and voids, and various structures are intertwined during the compaction process. Most of the traditional research focuses on the macro-scale domain, and it is difficult to obtain the internal structure of asphalt mixture in different compaction processes. With the continuous development of digital image technology, the influence of the meso-structure of the asphalt mixture on the compaction quality of the asphalt mixture has become a new means to evaluate the performance of the asphalt mixture. In this paper, different numbers of compactions are selected to represent different stages in the compaction process, the digital images of specimens in different compaction stages are obtained by industrial CT scanning technology. Then, the images are processed and reconstructed in three dimensions using improved image segmentation methods, and the position characteristics and geometric information of coarse aggregate are obtained by combining the Oriented Bounding Box (OBB). The meso-response characteristics of the skeleton structure of the asphalt mixture during compaction were studied. The influence of the internal structure of the mixture on the compaction quality of the mixture was obtained, which is of great significance for the study of improving the durability of the pavement. The results show that the "effective coordination number" (the number of aggregate particles that can transmit force in the skeleton structure) is greatly related to the aggregate size. With the compaction process, the centroid of coarse aggregate in the upper layer of the specimen reflects the overall downward movement trend. The inclination angle of the aggregate spindle tends to be in the range of 80°~100°; the anisotropic amplitude of the xy plane increases, and the direction of the aggregate spindle becomes more and more consistent. With the increase in the number of rotational compactions, these four parameters showed obvious rules, indicating that this meso-characteristic index could well characterize the compaction quality of the asphalt mixture in the compaction process.
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Affiliation(s)
- Xiangbing Gong
- School of Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, China
| | - Ziming Liu
- School of Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, China
| | - Guoping Qian
- School of Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, China
| | - Zhiyang Liu
- College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China
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Adam J, Adamczyk L, Adams JR, Adkins JK, Agakishiev G, Aggarwal MM, Ahammed Z, Alekseev I, Anderson DM, Aparin A, Aschenauer EC, Ashraf MU, Atetalla FG, Attri A, Averichev GS, Bairathi V, Barish K, Behera A, Bellwied R, Bhasin A, Bielcik J, Bielcikova J, Bland LC, Bordyuzhin IG, Brandenburg JD, Brandin AV, Butterworth J, Caines H, Calderón de la Barca Sánchez M, Cebra D, Chakaberia I, Chaloupka P, Chan BK, Chang FH, Chang Z, Chankova-Bunzarova N, Chatterjee A, Chen D, Chen J, Chen JH, Chen X, Chen Z, Cheng J, Cherney M, Chevalier M, Choudhury S, Christie W, Chu X, Crawford HJ, Csanád M, Daugherity M, Dedovich TG, Deppner IM, Derevschikov AA, Didenko L, Dong X, Drachenberg JL, Dunlop JC, Edmonds T, Elsey N, Engelage J, Eppley G, Esumi S, Evdokimov O, Ewigleben A, Eyser O, Fatemi R, Fazio S, Federic P, Fedorisin J, Feng CJ, Feng Y, Filip P, Finch E, Fisyak Y, Francisco A, Fulek L, Gagliardi CA, Galatyuk T, Geurts F, Ghimire N, Gibson A, Gopal K, Gou X, Grosnick D, Guryn W, Hamad AI, Hamed A, Harabasz S, Harris JW, He S, He W, He XH, He Y, Heppelmann S, Heppelmann S, Herrmann N, Hoffman E, Holub L, Hong Y, Horvat S, Hu Y, Huang HZ, Huang SL, Huang T, Huang X, Humanic TJ, Huo P, Igo G, Isenhower D, Jacobs WW, Jena C, Jentsch A, Ji Y, Jia J, Jiang K, Jowzaee S, Ju X, Judd EG, Kabana S, Kabir ML, Kagamaster S, Kalinkin D, Kang K, Kapukchyan D, Kauder K, Ke HW, Keane D, Kechechyan A, Kelsey M, Khyzhniak YV, Kikoła DP, Kim C, Kimelman B, Kincses D, Kinghorn TA, Kisel I, Kiselev A, Kocan M, Kochenda L, Kosarzewski LK, Kramarik L, Kravtsov P, Krueger K, Kulathunga Mudiyanselage N, Kumar L, Kumar S, Kunnawalkam Elayavalli R, Kwasizur JH, Lacey R, Lan S, Landgraf JM, Lauret J, Lebedev A, Lednicky R, Lee JH, Leung YH, Li C, Li C, Li W, Li W, Li X, Li Y, Liang Y, Licenik R, Lin T, Lin Y, Lisa MA, Liu F, Liu H, Liu P, Liu P, Liu T, Liu X, Liu Y, Liu Z, Ljubicic T, Llope WJ, Longacre RS, Lukow NS, Luo S, Luo X, Ma GL, Ma L, Ma R, Ma YG, Magdy N, Majka R, Mallick D, Margetis S, Markert C, Matis HS, Mazer JA, Minaev NG, Mioduszewski S, Mohanty B, Mooney I, Moravcova Z, Morozov DA, Nagy M, Nam JD, Nasim M, Nayak K, Neff D, Nelson JM, Nemes DB, Nie M, Nigmatkulov G, Niida T, Nogach LV, Nonaka T, Nunes AS, Odyniec G, Ogawa A, Oh S, Okorokov VA, Page BS, Pak R, Pandav A, Panebratsev Y, Pawlik B, Pawlowska D, Pei H, Perkins C, Pinsky L, Pintér RL, Pluta J, Pokhrel BR, Porter J, Posik M, Pruthi NK, Przybycien M, Putschke J, Qiu H, Quintero A, Radhakrishnan SK, Ramachandran S, Ray RL, Reed R, Ritter HG, Rogachevskiy OV, Romero JL, Ruan L, Rusnak J, Sahoo NR, Sako H, Salur S, Sandweiss J, Sato S, Schmidke WB, Schmitz N, Schweid BR, Seck F, Seger J, Sergeeva M, Seto R, Seyboth P, Shah N, Shahaliev E, Shanmuganathan PV, Shao M, Sheikh AI, Shen WQ, Shi SS, Shi Y, Shou QY, Sichtermann EP, Sikora R, Simko M, Singh J, Singha S, Smirnov N, Solyst W, Sorensen P, Spinka HM, Srivastava B, Stanislaus TDS, Stefaniak M, Stewart DJ, Strikhanov M, Stringfellow B, Suaide AAP, Sumbera M, Summa B, Sun XM, Sun X, Sun Y, Sun Y, Surrow B, Svirida DN, Szymanski P, Tang AH, Tang Z, Taranenko A, Tarnowsky T, Thomas JH, Timmins AR, Tlusty D, Tokarev M, Tomkiel CA, Trentalange S, Tribble RE, Tribedy P, Tripathy SK, Tsai OD, Tu Z, Ullrich T, Underwood DG, Upsal I, Van Buren G, Vanek J, Vasiliev AN, Vassiliev I, Videbæk F, Vokal S, Voloshin SA, Wang F, Wang G, Wang JS, Wang P, Wang Y, Wang Y, Wang Z, Webb JC, Weidenkaff PC, Wen L, Westfall GD, Wieman H, Wissink SW, Witt R, Wu Y, Xiao ZG, Xie G, Xie W, Xu H, Xu N, Xu QH, Xu YF, Xu Y, Xu Z, Xu Z, Yang C, Yang Q, Yang S, Yang Y, Yang Z, Ye Z, Ye Z, Yi L, Yip K, Yu Y, Zbroszczyk H, Zha W, Zhang C, Zhang D, Zhang S, Zhang S, Zhang XP, Zhang Y, Zhang Y, Zhang ZJ, Zhang Z, Zhang Z, Zhao J, Zhong C, Zhou C, Zhu X, Zhu Z, Zurek M, Zyzak M. Erratum: Global Polarization of Ξ and Ω Hyperons in Au+Au Collisions at sqrt[s_{NN}]=200 GeV [Phys. Rev. Lett. 126, 162301 (2021)]. Phys Rev Lett 2023; 131:089901. [PMID: 37683178 DOI: 10.1103/physrevlett.131.089901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Indexed: 09/10/2023]
Abstract
This corrects the article DOI: 10.1103/PhysRevLett.126.162301.
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Xu Y, Luan G, Liu F, Zhang Y, Li Z, Liu Z, Yang T. Exosomal miR-200b-3p induce macrophage polarization by regulating transcriptional repressor ZEB1 in hepatocellular carcinoma. Hepatol Int 2023; 17:889-903. [PMID: 36930410 DOI: 10.1007/s12072-023-10507-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 02/23/2023] [Indexed: 03/18/2023]
Abstract
PURPOSE Accumulating evidence has elucidated that the interaction between cancer cells and M2 macrophages plays an important role in the tumorigenesis of hepatocellular carcinoma (HCC). However, the mechanism connecting tumor-derived exosomes, M2 polarization of macrophages, and liver metastasis remain unclear. Therefore, it is necessary to explore their influence on the tumor microenvironment of HCC. METHODS Transmission electron microscopy, nanometer particle testing, and special biomarker analysis were utilized to characterize exosomes, while the differential expression of microRNAs was evaluated using high-throughput sequencing technology. The functions of miR-200b-3p exosomes were confirmed using in vitro and in vivo assays. The interactions between microRNAs and ZEB1 as well as cancer cells and macrophages were measured using RNA pull-down and luciferase gene reporter assays. RESULTS Using in silico analysis, we identified high levels of miR-200b-3p exosome expression in patients with HCC, particularly with relapsed HCC. We demonstrated that HCC cell-derived miR-200b-3p exosomes were internalized by M0 macrophages and induced M2 polarization by downregulating ZEB1 and upregulating interleukin-4. As a result, the JAK/STAT signaling pathway was activated in M2 macrophages, leading to increased PIM1 and VEGFα expression. These cell factors accelerated the proliferation and metastasis of HCC, resulting in a feedback loop between HCC cells and M2 macrophages. CONCLUSION The study illustrates that HCC cell-derived miR-200b-3p exosomes facilitate the proliferation and polarization of macrophages by modulating cytokine secretion and the JAK/STAT signaling pathway, leading to the metastasis of HCC. These findings demonstrate the existence of a novel feedback loop between cancer cells and immune cells in the tumor microenvironment, presenting a new concept in cancer research.
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Affiliation(s)
- Ying Xu
- Shandong Cancer Hospital and Institute, Shandong Fist Medical University and Shandong Academy of Medical Science, No 440, Jiyan Road, Ji'nan, Shandong, China.
| | | | - Feng Liu
- The First Affiliated Hospital of Shandong First Medical University, Shandong, China
| | - Yuhua Zhang
- Shandong University of Traditional Chinese Medicine, Shandong, China
| | - Zhongchao Li
- Shandong Cancer Hospital and Institute, Shandong Fist Medical University and Shandong Academy of Medical Science, No 440, Jiyan Road, Ji'nan, Shandong, China
| | - Ziming Liu
- Shandong Fist Medical University and Shandong Academy of Medical Science, Shandong, China
| | - Tao Yang
- Binzhou Medical University Hospital, Shandong, China
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72
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Wang H, Fu T, Du Y, Gao W, Huang K, Liu Z, Chandak P, Liu S, Van Katwyk P, Deac A, Anandkumar A, Bergen K, Gomes CP, Ho S, Kohli P, Lasenby J, Leskovec J, Liu TY, Manrai A, Marks D, Ramsundar B, Song L, Sun J, Tang J, Veličković P, Welling M, Zhang L, Coley CW, Bengio Y, Zitnik M. Scientific discovery in the age of artificial intelligence. Nature 2023; 620:47-60. [PMID: 37532811 DOI: 10.1038/s41586-023-06221-2] [Citation(s) in RCA: 44] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 05/16/2023] [Indexed: 08/04/2023]
Abstract
Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI toolsneed a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.
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Affiliation(s)
- Hanchen Wang
- Department of Engineering, University of Cambridge, Cambridge, UK
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
- Department of Research and Early Development, Genentech Inc, South San Francisco, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Tianfan Fu
- Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Yuanqi Du
- Department of Computer Science, Cornell University, Ithaca, NY, USA
| | - Wenhao Gao
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kexin Huang
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Ziming Liu
- Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Payal Chandak
- Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA, USA
| | - Shengchao Liu
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- Université de Montréal, Montreal, Quebec, Canada
| | - Peter Van Katwyk
- Department of Earth, Environmental and Planetary Sciences, Brown University, Providence, RI, USA
- Data Science Institute, Brown University, Providence, RI, USA
| | - Andreea Deac
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- Université de Montréal, Montreal, Quebec, Canada
| | - Anima Anandkumar
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
- NVIDIA, Santa Clara, CA, USA
| | - Karianne Bergen
- Department of Earth, Environmental and Planetary Sciences, Brown University, Providence, RI, USA
- Data Science Institute, Brown University, Providence, RI, USA
| | - Carla P Gomes
- Department of Computer Science, Cornell University, Ithaca, NY, USA
| | - Shirley Ho
- Center for Computational Astrophysics, Flatiron Institute, New York, NY, USA
- Department of Astrophysical Sciences, Princeton University, Princeton, NJ, USA
- Department of Physics, Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Physics and Center for Data Science, New York University, New York, NY, USA
| | | | - Joan Lasenby
- Department of Engineering, University of Cambridge, Cambridge, UK
| | - Jure Leskovec
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | | | - Arjun Manrai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Debora Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Le Song
- BioMap, Beijing, China
- Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates
| | - Jimeng Sun
- University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Jian Tang
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- HEC Montréal, Montreal, Quebec, Canada
- CIFAR AI Chair, Toronto, Ontario, Canada
| | - Petar Veličković
- Google DeepMind, London, UK
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - Max Welling
- University of Amsterdam, Amsterdam, Netherlands
- Microsoft Research Amsterdam, Amsterdam, Netherlands
| | - Linfeng Zhang
- DP Technology, Beijing, China
- AI for Science Institute, Beijing, China
| | - Connor W Coley
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yoshua Bengio
- Mila - Quebec AI Institute, Montreal, Quebec, Canada
- Université de Montréal, Montreal, Quebec, Canada
| | - Marinka Zitnik
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Harvard Data Science Initiative, Cambridge, MA, USA.
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA, USA.
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Bian B, Liu Z, Feng D, Li W, Wang L, Li Y, Li D. Glutaric Aciduria Type 1: Comparison between Diffusional Kurtosis Imaging and Conventional MR Imaging. AJNR Am J Neuroradiol 2023; 44:967-973. [PMID: 37474264 PMCID: PMC10411849 DOI: 10.3174/ajnr.a7928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 06/07/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND AND PURPOSE Routine MR imaging has limited use in evaluating the severity of glutaric aciduria type 1. To better understand the mechanisms of brain injury in glutaric aciduria type 1, we explored the value of diffusional kurtosis imaging in detecting microstructural injury of the gray and white matter. MATERIALS AND METHODS This study included 17 patients with glutaric aciduria type 1 and 17 healthy controls who underwent conventional MR imaging and diffusional kurtosis imaging. The diffusional kurtosis imaging metrics of the gray and white matter were measured. Then, the MR imaging scores and diffusional kurtosis imaging metrics of all ROIs were further correlated with the morbidity scores and Barry-Albright dystonia scores. RESULTS The MR imaging scores showed no significant relation to the morbidity and Barry-Albright dystonia scores. Compared with healthy controls, patients with glutaric aciduria type 1 showed higher kurtosis values in the basal ganglia, corona radiata, centrum semiovale, and temporal lobe (P < .05). The DTI metrics of the basal ganglia were higher than those of healthy controls (P < .05). The fractional anisotropy value of the temporal lobe and the mean diffusivity values of basal ganglia in glutaric aciduria type 1 were lower than those in the control group (P < .05). The diffusional kurtosis imaging metrics of the temporal lobe and basal ganglia were significantly correlated with the Barry-Albright dystonia scores. The mean kurtosis values of the anterior and posterior putamen and Barry-Albright dystonia scores were most relevant (r = 0.721, 0.730, respectively). The mean kurtosis values of the basal ganglia had the best diagnostic efficiency with area under the curve values of 0.837 for the temporal lobe, and the mean diffusivity values of the basal ganglia in glutaric aciduria type 1 were lower than those in the control group (P < .05). The diffusional kurtosis imaging metrics of the temporal lobe and basal ganglia were significantly correlated with the Barry-Albright dystonia scores. The mean kurtosis values of the anterior and posterior putamen and Barry-Albright dystonia scores were most relevant (r = 0.721, 0.730, respectively). The mean kurtosis values of the basal ganglia had the best diagnostic efficiency with area under the curve values of 0.837. CONCLUSIONS Diffusional kurtosis imaging provides more comprehensive quantitative information regarding the gray and white matter micropathologic damage in glutaric aciduria type 1 than routine MR imaging scores.
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Affiliation(s)
- B Bian
- From the Departments of Radiology (B.B., Z.L., D.L.)
| | - Z Liu
- From the Departments of Radiology (B.B., Z.L., D.L.)
| | - D Feng
- Outpatient Pediatrics (D.F.)
| | - W Li
- State Key Laboratory of Stem Cell and Reproductive Biology (W.L., L.W.), Chinese Academy of Sciences and University, Beijing, China
| | - L Wang
- State Key Laboratory of Stem Cell and Reproductive Biology (W.L., L.W.), Chinese Academy of Sciences and University, Beijing, China
| | - Y Li
- Gene Therapy Laboratory (Y.L.), The First Hospital of Jilin University, Changchun, Jilin, China
| | - D Li
- From the Departments of Radiology (B.B., Z.L., D.L.)
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Liu Z, Qiu X, Yang H, Wu X, Ye W. [Inhibitor of growth protein-2 silencing alleviates angiotensin Ⅱ-induced cardiac remodeling in mice by reducing p53 acetylation]. Nan Fang Yi Ke Da Xue Xue Bao 2023; 43:1127-1135. [PMID: 37488795 PMCID: PMC10366506 DOI: 10.12122/j.issn.1673-4254.2023.07.09] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 07/26/2023]
Abstract
OBJECTIVE To investigate the effect of inhibitor of growth protein-2 (Ing2) silencing on angiotensin Ⅱ (AngⅡ)-induced cardiac remodeling in mice and explore the underlying mechanism. METHODS An adenoviral vector carrying Ing2 shRNA or empty adenoviral vector was injected into the tail vein of mice, followed 48 h later by infusion of 1000 ng · kg-1 · min-1 Ang Ⅱ or saline using a mini-osmotic pump for 42 consecutive days. Transthoracic echocardiography was used to assess cardiac geometry and function and the level of cardiac hypertrophy in the mice. Masson and WGA staining were used to detect myocardial fibrosis and cross-sectional area of cardiomyocytes, and myocardial cell apoptosis was detected with TUNEL assay. Western blotting was performed to detect myocardial expressions of cleaved caspase 3, ING2, collagen Ⅰ, Ac-p53(Lys382) and p-p53 (Ser15); Ing2 mRNA expression was detected using real-time PCR. Mitochondrial biogenesis, as measured by mitochondrial ROS content, ATP content, citrate synthase activity and calcium storage, was determined using commercial assay kits. RESULTS The expression levels of Ing2 mRNA and protein were significantly higher in the mice with chronic Ang Ⅱ infusion than in saline-infused mice. Chronic infusion of AngⅡ significantly increased the left ventricular end-systolic diameter (LVESD) and left ventricular end-diastolic diameter (LVEDD) and reduced left ventricular ejection fraction (LVEF) and left ventricular fractional shortening (LVFS) in the mice. Ing2 silencing obviously alleviated AngⅡ-induced cardiac function decline, as shown by decreased LVEDD and LVESD and increased LVEF and LVFS, improved myocardial mitochondrial damage and myocardial hypertrophy and fibrosis, and inhibited cardiomyocyte apoptosis. Chronic AngⅡ infusion significantly increased myocardial expression levels of Ac-p53(Lys382) and p-p53(Ser15) in the mice, and Ing2 silencing prior to AngⅡ infusion lessened AngⅡ- induced increase of Ac-p53(Lys382) without affecting p53 (ser15) expression. CONCLUSION Ing2 silencing can inhibit AngⅡ-induced cardiac remodeling and dysfunction in mice by reducing p53 acetylation.
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Affiliation(s)
- Z Liu
- Department of Cardiovascular Medicine, Chinese Traditional Medicine Hospital of Hainan Province, Haikou 570203, China
| | - X Qiu
- Department of Endocrinology, Chinese Traditional Medicine Hospital of Hainan Province, Haikou 570203, China
| | - H Yang
- Department of Cardiovascular Medicine, Chinese Traditional Medicine Hospital of Hainan Province, Haikou 570203, China
| | - X Wu
- Department of Endocrinology, Chinese Traditional Medicine Hospital of Hainan Province, Haikou 570203, China
| | - W Ye
- Guangzhou University of Chinese Medicine, Guangzhou 510006, China
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Xiang Z, Huang X, Chen H, Liu B, Liu Z, Dong W, Wang H. Insights into thermal hydrolysis pretreatment temperature for enhancing volatile fatty acids production from sludge fermentation: Performance and mechanism. Bioresour Technol 2023; 379:129032. [PMID: 37031805 DOI: 10.1016/j.biortech.2023.129032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 05/03/2023]
Abstract
To reveal the impact of thermal hydrolysis pretreatment (THP) temperature on the unclear mechanisms of volatile fatty acids (VFAs) production, four groups were established with different temperatures (100, 120, 140 and 160 °C), and high throughput sequencing technology was utilized. The results indicated that the optimal VFAs production occurred at 140 °C. Moreover, as the THP temperature increased, the proportion of acetic acid also increased, accounting for 10.8% to 26.7% of the VFAs, compared to only 4.9% in the control group. Mechanism investigations revealed that THP facilitated the hydrolysis and release of biodegradable organic matter. Moreover, the abundance of VFAs production and hydrolytic microorganisms and related metabolic functional genes expression were evidently improved by THP. Overall, this study deepens the understanding of the mechanisms through which different THP temperatures stimulate the production of VFAs through acidogenic fermentation, providing technical support for future THP application in sludge treatment.
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Affiliation(s)
- Zhuangzhuang Xiang
- College of Environmental Science and Engineering, Ocean University of China, Qingdao 266100, China
| | - Xiao Huang
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China; State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China.
| | - Hanfeng Chen
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Biming Liu
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
| | - Ziming Liu
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Wenyi Dong
- School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen Key Laboratory of Water Resource Utilization and Environmental Pollution Control, Shenzhen 518055, China
| | - Hongjie Wang
- School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen Key Laboratory of Water Resource Utilization and Environmental Pollution Control, Shenzhen 518055, China
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Romanenko A, Harnik R, Grassellino A, Pilipenko R, Pischalnikov Y, Liu Z, Melnychuk OS, Giaccone B, Pronitchev O, Khabiboulline T, Frolov D, Posen S, Belomestnykh S, Berlin A, Hook A. Search for Dark Photons with Superconducting Radio Frequency Cavities. Phys Rev Lett 2023; 130:261801. [PMID: 37450797 DOI: 10.1103/physrevlett.130.261801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 05/23/2023] [Indexed: 07/18/2023]
Abstract
We conduct the first "light-shining-through-wall" (LSW) search for dark photons using two state-of-the-art high-quality-factor superconducting radio frequency (SRF) cavities -Dark SRF-and report the results of its pathfinder run. Our new experimental setup enables improvements in sensitivity over previous searches and covers new dark photon parameter space. We design delicate calibration and measurement protocols to utilize the high-Q setup at Dark SRF. Using cavities operating at 1.3 GHz, we establish a new exclusion limit for kinetic mixing as small as ε=1.6×10^{-9} and provide the world's best constraints on dark photons in the 2.1×10^{-7}-5.7×10^{-6} eV mass range. Our result is the first proof of concept for the enabling role of SRF cavities in LSW setups, with ample opportunities for further improvements. In addition, our data set a competitive lab-based limit on the standard model photon mass by searching for longitudinal photon polarization.
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Affiliation(s)
- A Romanenko
- Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
| | - R Harnik
- Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
| | - A Grassellino
- Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
| | - R Pilipenko
- Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
| | - Y Pischalnikov
- Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
| | - Z Liu
- School of Physics and Astronomy, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - O S Melnychuk
- Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
| | - B Giaccone
- Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
| | - O Pronitchev
- Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
| | - T Khabiboulline
- Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
| | - D Frolov
- Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
| | - S Posen
- Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
| | - S Belomestnykh
- Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
| | - A Berlin
- Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
| | - A Hook
- Maryland Center for Fundamental Physics, University of Maryland, College Park, Maryland 20742, USA
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Wang Y, Liu Z, Ma G, Xu Y, Li Y. Mouth breathing induces condylar remodelling and chondrocyte apoptosis via both the extrinsic and mitochondrial pathways in male adolescent rats. Tissue Cell 2023; 83:102146. [PMID: 37399641 DOI: 10.1016/j.tice.2023.102146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/29/2023] [Accepted: 06/16/2023] [Indexed: 07/05/2023]
Abstract
The prevalence of mouth breathing is high in children and adolescents. It causes various changes to the respiratory tract and, consequently, craniofacial growth deformities. However, the underlying mechanisms contributing to these effects are obscure. Herein, we aimed to study the effects of mouth breathing on chondrocyte proliferation and death in the condylar cartilage and morphological changes in the mandible and condyle. Additionally, we aimed to elucidate the mechanisms underlying chondrocyte apoptosis and investigate any variations in the related pathways. Subchondral bone resorption and decreased condylar cartilage thickness were observed in mouth-breathing rats; further, mRNA expression levels of Collagen II, Aggrecan, and Sox 9 were lower in the mouth breathing group, while those of matrix metalloproteinase 9 increased. TdT-mediated dUTP nick end labelling staining and immunohistochemistry analyses showed that apoptosis occurred in the proliferative and hypertrophic layers of cartilage in the mouth breathing group. TNF, BAX, cytochrome c, and cleaved-caspase-3 were highly expressed in the condylar cartilage of the mouth-breathing rats. These results suggest that mouth breathing leads to subchondral bone resorption, cartilage layer thinning, and cartilage matrix destruction, inducing chondrocyte apoptosis via both the extrinsic and mitochondrial apoptosis pathways.
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Affiliation(s)
- Y Wang
- Shanghai Engineering Research Center of Tooth Restoration and Regeneration, The Affiliated Stomatology Hospital of Tongji University, Department of Orthodontics, No. 399, Yanchang Middle Road, Jing'an District, Shanghai, CN 200072, China
| | - Z Liu
- Shanghai Engineering Research Center of Tooth Restoration and Regeneration, The Affiliated Stomatology Hospital of Tongji University, Department of Orthodontics, No. 399, Yanchang Middle Road, Jing'an District, Shanghai, CN 200072, China
| | - G Ma
- Shanghai Engineering Research Center of Tooth Restoration and Regeneration, The Affiliated Stomatology Hospital of Tongji University, Department of Orthodontics, No. 399, Yanchang Middle Road, Jing'an District, Shanghai, CN 200072, China
| | - Y Xu
- Shanghai Engineering Research Center of Tooth Restoration and Regeneration, The Affiliated Stomatology Hospital of Tongji University, Department of Orthodontics, No. 399, Yanchang Middle Road, Jing'an District, Shanghai, CN 200072, China
| | - Y Li
- The Affiliated Stomatology Hospital of Tongji University, Department of Orthodontics, No. 399, Yanchang Middle Road, Jing'an District, Shanghai CN 200072, China.
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Li YK, Qiu JY, Shi BL, Liu Z, Mao SH, Qiao J, Zhu ZZ, Qiu Y. [Comparison of intraoperative neurophysiological monitoring between patients with arthrogryposis multiplex congenita and adolescent idiopathic scoliosis]. Zhonghua Yi Xue Za Zhi 2023; 103:1774-1780. [PMID: 37305937 DOI: 10.3760/cma.j.cn112137-20221215-02661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Objective: To compare the intraoperative neurophysiological monitoring (IONM) results between patients with arthrogryposis multiplex congenita (AMC) and adolescent idiopathic scoliosis (AIS) and to analyze the influence of congenital spinal deformity on IONM in AMC patients, thus to evaluate the efficiency of IONM in AMC patients. Methods: A cross-sectional study. The clinical data of 19 AMC patients underwent correction surgery from July 2013 to January 2022 in Nanjing Drum Tower Hospital were retrospectively reviewed. There were 13 males and 6 females with a mean age of (15.2±5.6) years, and the average Cobb angle of main curve was 60.8°±27.7°. And 57 female AIS patients of similar age and curve type with the AMC patients during the same period were selected as the control group, with an average age of (14.6±4.4) years and a mean Cobb angle of 55.2°±14.2°. The latency and amplitude of samatosensory evoked potentials (SSEPs) and transcranial electric motor evoked potentials (TCeMEPs) were compared between the two groups. The difference in IONM data between AMC patients with and without congenital spinal deformity was also evaluated. Results: The success rates of SSEPs and TCeMEPs were 100% and 14/19 for AMC patients, 100% and 100% for AIS patients. The SSEPs-P40 latency, SSEPs-N50 latency, SSEPs-amplitude, TCeMEPs-latency, TCeMEPs-amplitude showed no significant difference between AMC patients and AIS patients (P>0.05 for all). The side-difference of TCeMEPs-amplitude showed an increasing trend in AMC patients when compared with that in AIS patients, but there was no statistical difference between the two groups [(147.0±185.6) μV vs (68.1±311.4) μV, P=0.198]. The SSEPs-amplitude value was (1.4±1.1) μV on concave side in AMC patients with congenital spinal deformity, and it was (2.6±1.2) μV on concave side in AMC patients without congenital spinal deformity (P=0.041). The SSEPs-amplitude value was (1.4±0.8) μV on convex side in AMC patients with congenital spinal deformity, and it was (2.6±1.3) μV on convex side in AMC patients without congenital spinal deformity (P=0.028). Conclusions: The values of SSEPs-P40 latency, SSEPs-N50 latency, SSEPs-amplitude, TCeMEPs-latency and TCeMEPs-amplitude are similar in AMC and AIS patients. The SSEPs-amplitude of AMC patients with congenital spinal deformity is lower than that of AMC patients without congenital spinal deformity.
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Affiliation(s)
- Y K Li
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - J Y Qiu
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - B L Shi
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - Z Liu
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - S H Mao
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - J Qiao
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - Z Z Zhu
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - Y Qiu
- Division of Spine Surgery, Department of Orthopedic Surgery, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
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Chen J, Xiao WC, Shan R, Song JY, Liu Z. [Influence of rs2587552 polymorphism of DRD2 gene on the effect of a childhood obesity intervention: A prospective, parallel-group controlled trial]. Beijing Da Xue Xue Bao Yi Xue Ban 2023; 55:436-441. [PMID: 37291918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
OBJECTIVE To explore the association between rs2587552 polymorphism (has a strong lin-kage disequilibrium with rs1800497 which had been found in many studies to be related to obesity, r2=0.85) of DRD2 gene and the effect of a childhood obesity intervention in Chinese population, and provide a scientific basis for future personalized childhood obesity intervention based on genetic background. METHODS From a multi-center cluster randomized controlled trial studying the effect of a childhood obesity intervention, we enrolled 382 children from 8 primary schools (192 and 190 children from intervention and control groups, respectively) in Beijing as study subjects. Saliva was collected and DNA was extracted to detect the rs2587552 polymorphism of DRD2 gene, and the interactions between the gene and study arms on childhood obesity indicators [including body weight, body mass index (BMI), BMI Z-score, waist circumference, hip circumference, waist-to-hip ratio, waist-to-height ratio, and body fat percentage] were analyzed. RESULTS No association was found between rs2587552 polymorphism and the changes in hip circumference or body fat percentage in the intervention group (P>0.05). However, in the control group, children carrying the A allele at DRD2 rs2587552 locus showed a greater increase in hip circumference and body fat percentage compared with those not carrying A allele (P < 0.001). There were interactions between rs2587552 polymorphism of DRD2 gene and study arms on the changes in hip circumference and body fat percentage (P=0.007 and 0.015, respectively). Compared with the control group, children in the intervention group carrying the A allele at DRD2 rs2587552 locus showed decrease in hip circumference by (-1.30 cm, 95%CI: -2.25 to -0.35, P=0.007) and decrease in body fat percentage by (-1.34%, 95%CI: -2.42 to -0.27, P=0.015) compared with those not carrying A allele. The results were consistent between the dominant model and the additive model (hip circumfe-rence: -0.66 cm, 95%CI: -1.28 to -0.03, P=0.041; body fat percentage: -0.69%, 95%CI: -1.40 to 0.02, P=0.056). No interaction was found between rs2587552 polymorphism and study arms on the changes in other childhood obesity-related indicators (P>0.05). CONCLUSION Children carrying the A allele at rs2587552 polymorphism of DRD2 gene are more sensitive to intervention and showed more improvement in hip circumference and body fat percentage after the intervention, suggesting that future personalized childhood obesity lifestyle intervention can be carried out based on the rs2587552 polymorphism of DRD2 gene.
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Affiliation(s)
- J Chen
- Department of Maternal and Child Health, Peking University School of Public Health, Beijing 100191, China
| | - W C Xiao
- Department of Maternal and Child Health, Peking University School of Public Health, Beijing 100191, China
| | - R Shan
- Department of Maternal and Child Health, Peking University School of Public Health, Beijing 100191, China
| | - J Y Song
- Institute of Child and Adolescent Health, Peking University School of Public Health, Beijing 100191, China
| | - Z Liu
- Department of Maternal and Child Health, Peking University School of Public Health, Beijing 100191, China
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Abdulhamid MI, Aboona BE, Adam J, Adams JR, Agakishiev G, Aggarwal I, Aggarwal MM, Ahammed Z, Aitbaev A, Alekseev I, Anderson DM, Aparin A, Aslam S, Atchison J, Averichev GS, Bairathi V, Baker W, Ball Cap JG, Barish K, Bhagat P, Bhasin A, Bhatta S, Bordyuzhin IG, Brandenburg JD, Brandin AV, Cai XZ, Caines H, Calderón de la Barca Sánchez M, Cebra D, Ceska J, Chakaberia I, Chan BK, Chang Z, Chatterjee A, Chen D, Chen J, Chen JH, Chen Z, Cheng J, Cheng Y, Choudhury S, Christie W, Chu X, Crawford HJ, Dale-Gau G, Das A, Daugherity M, Dedovich TG, Deppner IM, Derevschikov AA, Dhamija A, Di Carlo L, Didenko L, Dixit P, Dong X, Drachenberg JL, Duckworth E, Dunlop JC, Engelage J, Eppley G, Esumi S, Evdokimov O, Ewigleben A, Eyser O, Fatemi R, Fazio S, Feng CJ, Feng Y, Finch E, Fisyak Y, Flor FA, Fu C, Geurts F, Ghimire N, Gibson A, Gopal K, Gou X, Grosnick D, Gupta A, Hamed A, Han Y, Harasty MD, Harris JW, Harrison-Smith H, He W, He XH, He Y, Hu C, Hu Q, Hu Y, Huang H, Huang HZ, Huang SL, Huang T, Huang X, Huang Y, Huang Y, Humanic TJ, Isenhower D, Isshiki M, Jacobs WW, Jalotra A, Jena C, Ji Y, Jia J, Jin C, Ju X, Judd EG, Kabana S, Kabir ML, Kalinkin D, Kang K, Kapukchyan D, Kauder K, Ke HW, Keane D, Kechechyan A, Kelsey M, Kimelman B, Kiselev A, Knospe AG, Ko HS, Kochenda L, Korobitsin AA, Kravtsov P, Kumar L, Kumar S, Kunnawalkam Elayavalli R, Lacey R, Landgraf JM, Lebedev A, Lednicky R, Lee JH, Leung YH, Lewis N, Li C, Li W, Li X, Li Y, Li Y, Li Z, Liang X, Liang Y, Lin T, Liu C, Liu F, Liu G, Liu H, Liu H, Liu L, Liu T, Liu X, Liu Y, Liu Z, Ljubicic T, Llope WJ, Lomicky O, Longacre RS, Loyd EM, Lu T, Lukow NS, Luo XF, Luong VB, Ma L, Ma R, Ma YG, Magdy N, Mallick D, Margetis S, Matis HS, Mazer JA, McNamara G, Mi K, Minaev NG, Mohanty B, Mondal MM, Mooney I, Morozov DA, Mudrokh A, Nagy MI, Nain AS, Nam JD, Nasim M, Neff D, Nelson JM, Nemes DB, Nie M, Nigmatkulov G, Niida T, Nishitani R, Nogach LV, Nonaka T, Odyniec G, Ogawa A, Oh S, Okorokov VA, Okubo K, Page BS, Pak R, Pan J, Pandav A, Pandey AK, Panebratsev Y, Pani T, Parfenov P, Paul A, Perkins C, Pokhrel BR, Posik M, Protzman T, Pruthi NK, Putschke J, Qin Z, Qiu H, Quintero A, Racz C, Radhakrishnan SK, Raha N, Ray RL, Ritter HG, Robertson CW, Rogachevsky OV, Rosales Aguilar MA, Roy D, Ruan L, Sahoo AK, Sahoo NR, Sako H, Salur S, Samigullin E, Sato S, Schmidke WB, Schmitz N, Seger J, Seto R, Seyboth P, Shah N, Shahaliev E, Shanmuganathan PV, Shao T, Sharma M, Sharma N, Sharma R, Sharma SR, Sheikh AI, Shen DY, Shen K, Shi SS, Shi Y, Shou QY, Si F, Singh J, Singha S, Sinha P, Skoby MJ, Söhngen Y, Song Y, Srivastava B, Stanislaus TDS, Stewart DJ, Strikhanov M, Stringfellow B, Su Y, Sun C, Sun X, Sun Y, Sun Y, Surrow B, Svirida DN, Sweger ZW, Tamis A, Tang AH, Tang Z, Taranenko A, Tarnowsky T, Thomas JH, Tlusty D, Todoroki T, Tokarev MV, Tomkiel CA, Trentalange S, Tribble RE, Tribedy P, Tsai OD, Tsang CY, Tu Z, Ullrich T, Underwood DG, Upsal I, Van Buren G, Vasiliev AN, Verkest V, Videbæk F, Vokal S, Voloshin SA, Wang F, Wang G, Wang JS, Wang X, Wang Y, Wang Y, Wang Y, Wang Z, Webb JC, Weidenkaff PC, Westfall GD, Wieman H, Wilks G, Wissink SW, Wu J, Wu J, Wu X, Wu Y, Xi B, Xiao ZG, Xie G, Xie W, Xu H, Xu N, Xu QH, Xu Y, Xu Y, Xu Z, Xu Z, Yan G, Yan Z, Yang C, Yang Q, Yang S, Yang Y, Ye Z, Ye Z, Yi L, Yip K, Yu Y, Zha W, Zhang C, Zhang D, Zhang J, Zhang S, Zhang W, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhang ZJ, Zhang Z, Zhang Z, Zhao F, Zhao J, Zhao M, Zhou C, Zhou J, Zhou S, Zhou Y, Zhu X, Zurek M, Zyzak M. Measurements of the Elliptic and Triangular Azimuthal Anisotropies in Central ^{3}He+Au, d+Au and p+Au Collisions at sqrt[s_{NN}]=200 GeV. Phys Rev Lett 2023; 130:242301. [PMID: 37390421 DOI: 10.1103/physrevlett.130.242301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 02/27/2023] [Accepted: 05/15/2023] [Indexed: 07/02/2023]
Abstract
The elliptic (v_{2}) and triangular (v_{3}) azimuthal anisotropy coefficients in central ^{3}He+Au, d+Au, and p+Au collisions at sqrt[s_{NN}]=200 GeV are measured as a function of transverse momentum (p_{T}) at midrapidity (|η|<0.9), via the azimuthal angular correlation between two particles both at |η|<0.9. While the v_{2}(p_{T}) values depend on the colliding systems, the v_{3}(p_{T}) values are system independent within the uncertainties, suggesting an influence on eccentricity from subnucleonic fluctuations in these small-sized systems. These results also provide stringent constraints for the hydrodynamic modeling of these systems.
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Affiliation(s)
- M I Abdulhamid
- American University of Cairo, New Cairo 11835, New Cairo, Egypt
| | - B E Aboona
- Texas A&M University, College Station, Texas 77843
| | - J Adam
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - J R Adams
- The Ohio State University, Columbus, Ohio 43210
| | - G Agakishiev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - I Aggarwal
- Panjab University, Chandigarh 160014, India
| | | | - Z Ahammed
- Variable Energy Cyclotron Centre, Kolkata 700064, India
| | - A Aitbaev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - I Alekseev
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute", Moscow 117218
- National Research Nuclear University MEPhI, Moscow 115409
| | - D M Anderson
- Texas A&M University, College Station, Texas 77843
| | - A Aparin
- Joint Institute for Nuclear Research, Dubna 141 980
| | - S Aslam
- Indian Institute Technology, Patna, Bihar 801106, India
| | - J Atchison
- Abilene Christian University, Abilene, Texas 79699
| | | | - V Bairathi
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
| | - W Baker
- University of California, Riverside, California 92521
| | | | - K Barish
- University of California, Riverside, California 92521
| | - P Bhagat
- University of Jammu, Jammu 180001, India
| | - A Bhasin
- University of Jammu, Jammu 180001, India
| | - S Bhatta
- State University of New York, Stony Brook, New York 11794
| | - I G Bordyuzhin
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute", Moscow 117218
| | | | - A V Brandin
- National Research Nuclear University MEPhI, Moscow 115409
| | - X Z Cai
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800
| | - H Caines
- Yale University, New Haven, Connecticut 06520
| | | | - D Cebra
- University of California, Davis, California 95616
| | - J Ceska
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - I Chakaberia
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - B K Chan
- University of California, Los Angeles, California 90095
| | - Z Chang
- Indiana University, Bloomington, Indiana 47408
| | - A Chatterjee
- National Institute of Technology Durgapur, Durgapur - 713209, India
| | - D Chen
- University of California, Riverside, California 92521
| | - J Chen
- Shandong University, Qingdao, Shandong 266237
| | - J H Chen
- Fudan University, Shanghai, 200433
| | - Z Chen
- Shandong University, Qingdao, Shandong 266237
| | - J Cheng
- Tsinghua University, Beijing 100084
| | - Y Cheng
- University of California, Los Angeles, California 90095
| | | | - W Christie
- Brookhaven National Laboratory, Upton, New York 11973
| | - X Chu
- Brookhaven National Laboratory, Upton, New York 11973
| | - H J Crawford
- University of California, Berkeley, California 94720
| | - G Dale-Gau
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - A Das
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - M Daugherity
- Abilene Christian University, Abilene, Texas 79699
| | - T G Dedovich
- Joint Institute for Nuclear Research, Dubna 141 980
| | - I M Deppner
- University of Heidelberg, Heidelberg 69120, Germany
| | - A A Derevschikov
- NRC "Kurchatov Institute", Institute of High Energy Physics, Protvino 142281
| | - A Dhamija
- Panjab University, Chandigarh 160014, India
| | - L Di Carlo
- Wayne State University, Detroit, Michigan 48201
| | - L Didenko
- Brookhaven National Laboratory, Upton, New York 11973
| | - P Dixit
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - X Dong
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | | | - J C Dunlop
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Engelage
- University of California, Berkeley, California 94720
| | - G Eppley
- Rice University, Houston, Texas 77251
| | - S Esumi
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - O Evdokimov
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - A Ewigleben
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - O Eyser
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Fatemi
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - S Fazio
- University of Calabria & INFN-Cosenza, Rende 87036, Italy
| | - C J Feng
- National Cheng Kung University, Tainan 70101
| | - Y Feng
- Purdue University, West Lafayette, Indiana 47907
| | - E Finch
- Southern Connecticut State University, New Haven, Connecticut 06515
| | - Y Fisyak
- Brookhaven National Laboratory, Upton, New York 11973
| | - F A Flor
- Yale University, New Haven, Connecticut 06520
| | - C Fu
- Central China Normal University, Wuhan, Hubei 430079
| | - F Geurts
- Rice University, Houston, Texas 77251
| | - N Ghimire
- Temple University, Philadelphia, Pennsylvania 19122
| | - A Gibson
- Valparaiso University, Valparaiso, Indiana 46383
| | - K Gopal
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - X Gou
- Shandong University, Qingdao, Shandong 266237
| | - D Grosnick
- Valparaiso University, Valparaiso, Indiana 46383
| | - A Gupta
- University of Jammu, Jammu 180001, India
| | - A Hamed
- American University of Cairo, New Cairo 11835, New Cairo, Egypt
| | - Y Han
- Rice University, Houston, Texas 77251
| | - M D Harasty
- University of California, Davis, California 95616
| | - J W Harris
- Yale University, New Haven, Connecticut 06520
| | | | - W He
- Fudan University, Shanghai, 200433
| | - X H He
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y He
- Shandong University, Qingdao, Shandong 266237
| | - C Hu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Q Hu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Hu
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - H Huang
- National Cheng Kung University, Tainan 70101
| | - H Z Huang
- University of California, Los Angeles, California 90095
| | - S L Huang
- State University of New York, Stony Brook, New York 11794
| | - T Huang
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - X Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Central China Normal University, Wuhan, Hubei 430079
| | - T J Humanic
- The Ohio State University, Columbus, Ohio 43210
| | - D Isenhower
- Abilene Christian University, Abilene, Texas 79699
| | - M Isshiki
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - W W Jacobs
- Indiana University, Bloomington, Indiana 47408
| | - A Jalotra
- University of Jammu, Jammu 180001, India
| | - C Jena
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - Y Ji
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J Jia
- Brookhaven National Laboratory, Upton, New York 11973
- State University of New York, Stony Brook, New York 11794
| | - C Jin
- Rice University, Houston, Texas 77251
| | - X Ju
- University of Science and Technology of China, Hefei, Anhui 230026
| | - E G Judd
- University of California, Berkeley, California 94720
| | - S Kabana
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
| | - M L Kabir
- University of California, Riverside, California 92521
| | - D Kalinkin
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - K Kang
- Tsinghua University, Beijing 100084
| | - D Kapukchyan
- University of California, Riverside, California 92521
| | - K Kauder
- Brookhaven National Laboratory, Upton, New York 11973
| | - H W Ke
- Brookhaven National Laboratory, Upton, New York 11973
| | - D Keane
- Kent State University, Kent, Ohio 44242
| | - A Kechechyan
- Joint Institute for Nuclear Research, Dubna 141 980
| | - M Kelsey
- Wayne State University, Detroit, Michigan 48201
| | - B Kimelman
- University of California, Davis, California 95616
| | - A Kiselev
- Brookhaven National Laboratory, Upton, New York 11973
| | - A G Knospe
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - H S Ko
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - L Kochenda
- National Research Nuclear University MEPhI, Moscow 115409
| | | | - P Kravtsov
- National Research Nuclear University MEPhI, Moscow 115409
| | - L Kumar
- Panjab University, Chandigarh 160014, India
| | - S Kumar
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | | | - R Lacey
- State University of New York, Stony Brook, New York 11794
| | - J M Landgraf
- Brookhaven National Laboratory, Upton, New York 11973
| | - A Lebedev
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Lednicky
- Joint Institute for Nuclear Research, Dubna 141 980
| | - J H Lee
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y H Leung
- University of Heidelberg, Heidelberg 69120, Germany
| | - N Lewis
- Brookhaven National Laboratory, Upton, New York 11973
| | - C Li
- Shandong University, Qingdao, Shandong 266237
| | - W Li
- Rice University, Houston, Texas 77251
| | - X Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Li
- Tsinghua University, Beijing 100084
| | - Z Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - X Liang
- University of California, Riverside, California 92521
| | - Y Liang
- Kent State University, Kent, Ohio 44242
| | - T Lin
- Shandong University, Qingdao, Shandong 266237
| | - C Liu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - F Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - G Liu
- South China Normal University, Guangzhou, Guangdong 510631
| | - H Liu
- Indiana University, Bloomington, Indiana 47408
| | - H Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - L Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - T Liu
- Yale University, New Haven, Connecticut 06520
| | - X Liu
- The Ohio State University, Columbus, Ohio 43210
| | - Y Liu
- Texas A&M University, College Station, Texas 77843
| | - Z Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - T Ljubicic
- Brookhaven National Laboratory, Upton, New York 11973
| | - W J Llope
- Wayne State University, Detroit, Michigan 48201
| | - O Lomicky
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - R S Longacre
- Brookhaven National Laboratory, Upton, New York 11973
| | - E M Loyd
- University of California, Riverside, California 92521
| | - T Lu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - N S Lukow
- Temple University, Philadelphia, Pennsylvania 19122
| | - X F Luo
- Central China Normal University, Wuhan, Hubei 430079
| | - V B Luong
- Joint Institute for Nuclear Research, Dubna 141 980
| | - L Ma
- Fudan University, Shanghai, 200433
| | - R Ma
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y G Ma
- Fudan University, Shanghai, 200433
| | - N Magdy
- State University of New York, Stony Brook, New York 11794
| | - D Mallick
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | | | - H S Matis
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J A Mazer
- Rutgers University, Piscataway, New Jersey 08854
| | - G McNamara
- Wayne State University, Detroit, Michigan 48201
| | - K Mi
- Central China Normal University, Wuhan, Hubei 430079
| | - N G Minaev
- NRC "Kurchatov Institute", Institute of High Energy Physics, Protvino 142281
| | - B Mohanty
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - M M Mondal
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - I Mooney
- Yale University, New Haven, Connecticut 06520
| | - D A Morozov
- NRC "Kurchatov Institute", Institute of High Energy Physics, Protvino 142281
| | - A Mudrokh
- Joint Institute for Nuclear Research, Dubna 141 980
| | - M I Nagy
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - A S Nain
- Panjab University, Chandigarh 160014, India
| | - J D Nam
- Temple University, Philadelphia, Pennsylvania 19122
| | - Md Nasim
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - D Neff
- University of California, Los Angeles, California 90095
| | - J M Nelson
- University of California, Berkeley, California 94720
| | - D B Nemes
- Yale University, New Haven, Connecticut 06520
| | - M Nie
- Shandong University, Qingdao, Shandong 266237
| | - G Nigmatkulov
- National Research Nuclear University MEPhI, Moscow 115409
| | - T Niida
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - R Nishitani
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - L V Nogach
- NRC "Kurchatov Institute", Institute of High Energy Physics, Protvino 142281
| | - T Nonaka
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - G Odyniec
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - A Ogawa
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Oh
- Sejong University, Seoul, 05006, South Korea
| | - V A Okorokov
- National Research Nuclear University MEPhI, Moscow 115409
| | - K Okubo
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - B S Page
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Pak
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Pan
- Texas A&M University, College Station, Texas 77843
| | - A Pandav
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - A K Pandey
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | | | - T Pani
- Rutgers University, Piscataway, New Jersey 08854
| | - P Parfenov
- National Research Nuclear University MEPhI, Moscow 115409
| | - A Paul
- University of California, Riverside, California 92521
| | - C Perkins
- University of California, Berkeley, California 94720
| | - B R Pokhrel
- Temple University, Philadelphia, Pennsylvania 19122
| | - M Posik
- Temple University, Philadelphia, Pennsylvania 19122
| | - T Protzman
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - N K Pruthi
- Panjab University, Chandigarh 160014, India
| | - J Putschke
- Wayne State University, Detroit, Michigan 48201
| | - Z Qin
- Tsinghua University, Beijing 100084
| | - H Qiu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - A Quintero
- Temple University, Philadelphia, Pennsylvania 19122
| | - C Racz
- University of California, Riverside, California 92521
| | | | - N Raha
- Wayne State University, Detroit, Michigan 48201
| | - R L Ray
- University of Texas, Austin, Texas 78712
| | - H G Ritter
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | | | | | - D Roy
- Rutgers University, Piscataway, New Jersey 08854
| | - L Ruan
- Brookhaven National Laboratory, Upton, New York 11973
| | - A K Sahoo
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - N R Sahoo
- Shandong University, Qingdao, Shandong 266237
| | - H Sako
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - S Salur
- Rutgers University, Piscataway, New Jersey 08854
| | - E Samigullin
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute", Moscow 117218
| | - S Sato
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - W B Schmidke
- Brookhaven National Laboratory, Upton, New York 11973
| | - N Schmitz
- Max-Planck-Institut für Physik, Munich 80805, Germany
| | - J Seger
- Creighton University, Omaha, Nebraska 68178
| | - R Seto
- University of California, Riverside, California 92521
| | - P Seyboth
- Max-Planck-Institut für Physik, Munich 80805, Germany
| | - N Shah
- Indian Institute Technology, Patna, Bihar 801106, India
| | - E Shahaliev
- Joint Institute for Nuclear Research, Dubna 141 980
| | | | - T Shao
- Fudan University, Shanghai, 200433
| | - M Sharma
- University of Jammu, Jammu 180001, India
| | - N Sharma
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - R Sharma
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - S R Sharma
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | | | - D Y Shen
- Fudan University, Shanghai, 200433
| | - K Shen
- University of Science and Technology of China, Hefei, Anhui 230026
| | - S S Shi
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Shi
- Shandong University, Qingdao, Shandong 266237
| | - Q Y Shou
- Fudan University, Shanghai, 200433
| | - F Si
- University of Science and Technology of China, Hefei, Anhui 230026
| | - J Singh
- Panjab University, Chandigarh 160014, India
| | - S Singha
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - P Sinha
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - M J Skoby
- Ball State University, Muncie, Indiana, 47306
- Purdue University, West Lafayette, Indiana 47907
| | - Y Söhngen
- University of Heidelberg, Heidelberg 69120, Germany
| | - Y Song
- Yale University, New Haven, Connecticut 06520
| | - B Srivastava
- Purdue University, West Lafayette, Indiana 47907
| | | | - D J Stewart
- Wayne State University, Detroit, Michigan 48201
| | - M Strikhanov
- National Research Nuclear University MEPhI, Moscow 115409
| | | | - Y Su
- University of Science and Technology of China, Hefei, Anhui 230026
| | - C Sun
- State University of New York, Stony Brook, New York 11794
| | - X Sun
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Sun
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Sun
- Huzhou University, Huzhou, Zhejiang 313000
| | - B Surrow
- Temple University, Philadelphia, Pennsylvania 19122
| | - D N Svirida
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute", Moscow 117218
| | - Z W Sweger
- University of California, Davis, California 95616
| | - A Tamis
- Yale University, New Haven, Connecticut 06520
| | - A H Tang
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Tang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - A Taranenko
- National Research Nuclear University MEPhI, Moscow 115409
| | - T Tarnowsky
- Michigan State University, East Lansing, Michigan 48824
| | - J H Thomas
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - D Tlusty
- Creighton University, Omaha, Nebraska 68178
| | - T Todoroki
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - M V Tokarev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - C A Tomkiel
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - S Trentalange
- University of California, Los Angeles, California 90095
| | - R E Tribble
- Texas A&M University, College Station, Texas 77843
| | - P Tribedy
- Brookhaven National Laboratory, Upton, New York 11973
| | - O D Tsai
- Brookhaven National Laboratory, Upton, New York 11973
- University of California, Los Angeles, California 90095
| | - C Y Tsang
- Brookhaven National Laboratory, Upton, New York 11973
- Kent State University, Kent, Ohio 44242
| | - Z Tu
- Brookhaven National Laboratory, Upton, New York 11973
| | - T Ullrich
- Brookhaven National Laboratory, Upton, New York 11973
| | - D G Underwood
- Argonne National Laboratory, Argonne, Illinois 60439
- Valparaiso University, Valparaiso, Indiana 46383
| | - I Upsal
- Rice University, Houston, Texas 77251
| | - G Van Buren
- Brookhaven National Laboratory, Upton, New York 11973
| | - A N Vasiliev
- National Research Nuclear University MEPhI, Moscow 115409
- NRC "Kurchatov Institute", Institute of High Energy Physics, Protvino 142281
| | - V Verkest
- Wayne State University, Detroit, Michigan 48201
| | - F Videbæk
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Vokal
- Joint Institute for Nuclear Research, Dubna 141 980
| | | | - F Wang
- Purdue University, West Lafayette, Indiana 47907
| | - G Wang
- University of California, Los Angeles, California 90095
| | - J S Wang
- Huzhou University, Huzhou, Zhejiang 313000
| | - X Wang
- Shandong University, Qingdao, Shandong 266237
| | - Y Wang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Wang
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Wang
- Tsinghua University, Beijing 100084
| | - Z Wang
- Shandong University, Qingdao, Shandong 266237
| | - J C Webb
- Brookhaven National Laboratory, Upton, New York 11973
| | | | - G D Westfall
- Michigan State University, East Lansing, Michigan 48824
| | - H Wieman
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - G Wilks
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - S W Wissink
- Indiana University, Bloomington, Indiana 47408
| | - J Wu
- Central China Normal University, Wuhan, Hubei 430079
| | - J Wu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - X Wu
- University of California, Los Angeles, California 90095
| | - Y Wu
- University of California, Riverside, California 92521
| | - B Xi
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800
| | - Z G Xiao
- Tsinghua University, Beijing 100084
| | - G Xie
- University of Chinese Academy of Sciences, Beijing, 101408
| | - W Xie
- Purdue University, West Lafayette, Indiana 47907
| | - H Xu
- Huzhou University, Huzhou, Zhejiang 313000
| | - N Xu
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - Q H Xu
- Shandong University, Qingdao, Shandong 266237
| | - Y Xu
- Shandong University, Qingdao, Shandong 266237
| | - Y Xu
- Central China Normal University, Wuhan, Hubei 430079
| | - Z Xu
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Xu
- University of California, Los Angeles, California 90095
| | - G Yan
- Shandong University, Qingdao, Shandong 266237
| | - Z Yan
- State University of New York, Stony Brook, New York 11794
| | - C Yang
- Shandong University, Qingdao, Shandong 266237
| | - Q Yang
- Shandong University, Qingdao, Shandong 266237
| | - S Yang
- South China Normal University, Guangzhou, Guangdong 510631
| | - Y Yang
- National Cheng Kung University, Tainan 70101
| | - Z Ye
- Rice University, Houston, Texas 77251
| | - Z Ye
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - L Yi
- Shandong University, Qingdao, Shandong 266237
| | - K Yip
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y Yu
- Shandong University, Qingdao, Shandong 266237
| | - W Zha
- University of Science and Technology of China, Hefei, Anhui 230026
| | - C Zhang
- State University of New York, Stony Brook, New York 11794
| | - D Zhang
- Central China Normal University, Wuhan, Hubei 430079
| | - J Zhang
- Shandong University, Qingdao, Shandong 266237
| | - S Zhang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - W Zhang
- South China Normal University, Guangzhou, Guangdong 510631
| | - X Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Zhang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Zhang
- Central China Normal University, Wuhan, Hubei 430079
| | - Z J Zhang
- National Cheng Kung University, Tainan 70101
| | - Z Zhang
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Zhang
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - F Zhao
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - J Zhao
- Fudan University, Shanghai, 200433
| | - M Zhao
- Brookhaven National Laboratory, Upton, New York 11973
| | - C Zhou
- Fudan University, Shanghai, 200433
| | - J Zhou
- University of Science and Technology of China, Hefei, Anhui 230026
| | - S Zhou
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Zhou
- Central China Normal University, Wuhan, Hubei 430079
| | - X Zhu
- Tsinghua University, Beijing 100084
| | - M Zurek
- Argonne National Laboratory, Argonne, Illinois 60439
- Brookhaven National Laboratory, Upton, New York 11973
| | - M Zyzak
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
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Hao JN, Kong Z, Liu Z, Wang YH, Pan ZB, Wang J. [Analysis of safety and factors influencing the surgical efficacy of benign biliary stenosis treated with autologous gastric flap repair with the vascular tip]. Zhonghua Yi Xue Za Zhi 2023; 103:1707-1713. [PMID: 37302861 DOI: 10.3760/cma.j.cn112137-20230209-00182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Objective: To investigate the complication rate and risk factors associated with using autologous gastric flap tissue with a vascular tip to treat benign biliary strictures. Methods: A retrospective analysis was conducted on clinical data of 92 patients with benign biliary stenosis who applied autologous gastric flap tissue to repair the stenosis at the PLA General Hospital from January 2006 to May 2022. Among them, there were 40 males and 52 females, aged from 25 to 79 (50.5±12.9) years. The perioperative clinical data of the patients were recorded(Body Mass Index、preoperative platelets et.), and a multivariate logistic regression model was used to analyze the factors influencing postoperative complications. Long-term follow-up was conducted to evaluate the long-term efficacy of autologous gastric flap tissue with vascular tissues for benign biliary stenosis surgery. Results: The incidence of recent postoperative complications in patients was 26.1%, and univariate analysis showed that preoperative bile-intestinal anastomosis, positive intraoperative bile bacterial culture, low preoperative hemoglobin, and low preoperative platelet count were significantly associated with the occurrence of postoperative complications after biliary stenosis repair with a vascularized gastric flap (P<0.05). Multifactorial analysis showed that low preoperative platelets (OR=0.990, 95%CI: 0.982-0.998, P=0.015), low preoperative hemoglobin (OR=4.953, 95%CI: 1.405-15.010, P=0.012) and positive intraoperative bile bacterial culture (OR=19.338, 95%CI: 3.618-103.360, P<0.001) were independent risk factors for the development of postoperative complications. The excellent long-term follow-up rate of patients was 92.0%. Conclusions: The procedure of repairing benign biliary stenosis with a vascularized gastric flap preserves the function of the sphincter of Oddi and reconstructs the normal physiological passage of the bile duct. This procedure is safe and feasible and provides a reliable option for the surgical treatment of bile duct injury and bile duct stenosis.
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Affiliation(s)
- J N Hao
- Medical School of Chinese PLA, Beijing 100853,China Faculty of Hepato-Pancreato-Biliary Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853,China
| | - Z Kong
- Faculty of Hepato-Pancreato-Biliary Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853,China
| | - Z Liu
- Faculty of Hepato-Pancreato-Biliary Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853,China
| | - Y H Wang
- The fourth center of the PLA General Hospital, Beijing 100037,China
| | - Z B Pan
- 68207 Unit PLA, Jiayuguan 735100,China
| | - J Wang
- Faculty of Hepato-Pancreato-Biliary Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853,China
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Wu H, Manglike A, Chen Y, Liu Z, Fritzsche K, Lu Z. Scoping review update on somatic symptom disorder that includes additional Chinese data. Gen Psychiatr 2023; 36:e100942. [PMID: 37337547 PMCID: PMC10277133 DOI: 10.1136/gpsych-2022-100942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 04/23/2023] [Indexed: 06/21/2023] Open
Abstract
Somatic symptom disorder (SSD) is a new diagnosis introduced into the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), which is expected to solve the diagnostic difficulties of patients with medically unexplained symptoms. Based on the previous work, this review aims to comprehensively synthesise updated evidence related to SSD from recent years in English publications and, more extensively, from data published in Chinese language journals. The scoping review update was based on an earlier scoping review and included Chinese language publication data from China National Knowledge Internet (CNKI), WANFANG and WEIPU between January 2013 and May 2022 and data from PubMed, PsycINFO, and Cochrane Library between June 2020 and May 2022. Initially, 2 984 articles were identified, of which 63 full texts were included for analysis. In China, SSD is mainly applied in scientific research, but it also shows good predictive validity and clinical application potential. The mean frequency of SSD was 4.5% in the general population, 25.2% in the primary care population and 33.5% in diverse specialised care settings. Biological factors, such as brain region changes and heart rate variability, are associated with the onset of SSD. Psychological impairment related to somatic symptoms is the best predictor of prognosis. While adolescent SSD was significantly associated with family function, SSD overall is associated with an increased dysfunction of cognition and emotion, decreased quality of life, and high comorbidity with anxiety and depressive disorders. Further research is needed on suicide risk and cultural and gender-related issues. Updating the data of Chinese language studies, our research enriches the evidence-based findings related to the topics addressed in the text sections of the SSD chapter of DSM-5. However, research gaps remain about SSD reliability, population-based prevalence, suicide risk, and cultural and gender-related issues.
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Affiliation(s)
- Heng Wu
- Department of Psychosomatic Medicine, Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| | - Ayinuer Manglike
- Department of Psychosomatic Medicine, Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yixiao Chen
- Department of Psychosomatic Medicine, Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| | - Ziming Liu
- Department of Psychosomatic Medicine, Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China
| | - Kurt Fritzsche
- Department of Psychosomatic Medicine and Psychotherapy, Medical Center, University of Freiburg, Freiburg, Germany
| | - Zheng Lu
- Department of Psychosomatic Medicine, Shanghai Tongji Hospital, Tongji University School of Medicine, Shanghai, China
- Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Liu MF, Ma RX, Cao XB, Zhang H, Zhou SH, Jiang WH, Jiang Y, Sun JW, Yang QT, Li XZ, Sun YN, Shi L, Wang M, Song XC, Chen FQ, Zhang XS, Wei HQ, Yu SQ, Zhu DD, Ba L, Cao ZW, Xiao XP, Wei X, Lin ZH, Chen FH, Shan CG, Wang GK, Ye J, Qu SH, Zhao CQ, Wang ZL, Li HB, Liu F, Cui XB, Ye SN, Liu Z, Xu Y, Cai X, Hang W, Zhang RX, Zhao YL, Yu GD, Shi GG, Lu MP, Shen Y, Zhao YT, Pei JH, Xie SB, Yu LG, Liu YH, Gu SS, Yang YC, Cheng L, Liu JF. [Incidence and prognosis of olfactory and gustatory dysfunctions related to infection of SARS-CoV-2 Omicron strain: a national multi-center survey of 35 566 population]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2023; 58:579-588. [PMID: 37339898 DOI: 10.3760/cma.j.cn115330-20230316-00117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/22/2023]
Abstract
Objective: This cross-sectional investigation aimed to determine the incidence, clinical characteristics, prognosis, and related risk factors of olfactory and gustatory dysfunctions related to infection with the SARS-CoV-2 Omicron strain in mainland China. Methods: Data of patients with SARS-CoV-2 from December 28, 2022, to February 21, 2023, were collected through online and offline questionnaires from 45 tertiary hospitals and one center for disease control and prevention in mainland China. The questionnaire included demographic information, previous health history, smoking and alcohol drinking, SARS-CoV-2 vaccination, olfactory and gustatory function before and after infection, other symptoms after infection, as well as the duration and improvement of olfactory and gustatory dysfunction. The self-reported olfactory and gustatory functions of patients were evaluated using the Olfactory VAS scale and Gustatory VAS scale. Results: A total of 35 566 valid questionnaires were obtained, revealing a high incidence of olfactory and taste dysfunctions related to infection with the SARS-CoV-2 Omicron strain (67.75%). Females(χ2=367.013, P<0.001) and young people(χ2=120.210, P<0.001) were more likely to develop these dysfunctions. Gender(OR=1.564, 95%CI: 1.487-1.645), SARS-CoV-2 vaccination status (OR=1.334, 95%CI: 1.164-1.530), oral health status (OR=0.881, 95%CI: 0.839-0.926), smoking history (OR=1.152, 95%CI=1.080-1.229), and drinking history (OR=0.854, 95%CI: 0.785-0.928) were correlated with the occurrence of olfactory and taste dysfunctions related to SARS-CoV-2(above P<0.001). 44.62% (4 391/9 840) of the patients who had not recovered their sense of smell and taste also suffered from nasal congestion, runny nose, and 32.62% (3 210/9 840) suffered from dry mouth and sore throat. The improvement of olfactory and taste functions was correlated with the persistence of accompanying symptoms(χ2=10.873, P=0.001). The average score of olfactory and taste VAS scale was 8.41 and 8.51 respectively before SARS-CoV-2 infection, but decreased to3.69 and 4.29 respectively after SARS-CoV-2 infection, and recovered to 5.83and 6.55 respectively at the time of the survey. The median duration of olfactory and gustatory dysfunctions was 15 days and 12 days, respectively, with 0.5% (121/24 096) of patients experiencing these dysfunctions for more than 28 days. The overall self-reported improvement rate of smell and taste dysfunctions was 59.16% (14 256/24 096). Gender(OR=0.893, 95%CI: 0.839-0.951), SARS-CoV-2 vaccination status (OR=1.334, 95%CI: 1.164-1.530), history of head and facial trauma(OR=1.180, 95%CI: 1.036-1.344, P=0.013), nose (OR=1.104, 95%CI: 1.042-1.171, P=0.001) and oral (OR=1.162, 95%CI: 1.096-1.233) health status, smoking history(OR=0.765, 95%CI: 0.709-0.825), and the persistence of accompanying symptoms (OR=0.359, 95%CI: 0.332-0.388) were correlated with the recovery of olfactory and taste dysfunctions related to SARS-CoV-2 (above P<0.001 except for the indicated values). Conclusion: The incidence of olfactory and taste dysfunctions related to infection with the SARS-CoV-2 Omicron strain is high in mainland China, with females and young people more likely to develop these dysfunctions. Active and effective intervention measures may be required for cases that persist for a long time. The recovery of olfactory and taste functions is influenced by several factors, including gender, SARS-CoV-2 vaccination status, history of head and facial trauma, nasal and oral health status, smoking history, and persistence of accompanying symptoms.
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Affiliation(s)
- M F Liu
- Graduate School of Beijing University of Chinese Medicine, Beijing 100029, China Department of Otorhinolaryngology Head and Neck Surgery, China-Japan Friendship Hospital, Beijing 100029, China
| | - R X Ma
- Department of Otorhinolaryngology Head and Neck Surgery, the First People's Hospital of Yinchuan, Yinchuan 750001, China
| | - X B Cao
- Department of Otorhinolaryngology, the First People's Hospital of Yunnan Province, Kunming 650100, China
| | - H Zhang
- Department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
| | - S H Zhou
- Department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou 310003, China
| | - W H Jiang
- Department of Otorhinolaryngology Head and Neck Surgery, Xiangya Hospital Central South University, Changsha 410008, China
| | - Y Jiang
- Department of Otorhinolaryngology Head and Neck Surgery, the Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - J W Sun
- Department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital of USTC, Hefei 230001, China
| | - Q T Yang
- Department of Otorhinolaryngology Head and Neck Surgery, the Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
| | - X Z Li
- Department of Otorhinolaryngology Head and Neck Surgery, Qilu Hospital of Shandong University, Jinan 250012, China
| | - Y N Sun
- Department of Otorhinolaryngology Head and Neck Surgery, the Second Affiliated Hospital of Harbin Medical University, Harbin 150001, China
| | - L Shi
- Department of Rhinology and Allergy, Shandong Provincial ENT Hospital, Shandong University, Jinan 250299, China
| | - M Wang
- Department of Otorhinolaryngology Head and Neck Surgery, Peking University People's Hospital, Beijing 100032, China
| | - X C Song
- Department of Otorhinolaryngology Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai 264000, China
| | - F Q Chen
- Department of Otorhinolaryngology Head and Neck Surgery, Xijing Hospital, the Fourth Military Medical University, Xi'an 710032, China
| | - X S Zhang
- Gansu Provincial Center for Disease Control and Prevention, Lanzhou 730000, China
| | - H Q Wei
- Department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital of China Medical University, Shenyang 110001, China
| | - S Q Yu
- Department of Otorhinolaryngology Head and Neck Surgery, Tongji Hospital, Tongji Medical University, Shanghai 200065, China
| | - D D Zhu
- Department of Otorhinolaryngology Head and Neck Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
| | - L Ba
- Department of Otorhinolaryngology Head and Neck Surgery, Xizang Autonomous Region People's Hospital, Lasa 850000, China
| | - Z W Cao
- Department of Otorhinolaryngology Head and Neck Surgery, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - X P Xiao
- Department of Otorhinolaryngology Head and Neck Surgery, Hunan Provincial People's Hospital, Changsha 410005, China
| | - X Wei
- Department of Otorhinolaryngology Head and Neck Surgery, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou 570311, China
| | - Z H Lin
- Department of Otorhinolaryngology Head and Neck Surgery, the Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310009, China
| | - F H Chen
- Department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
| | - C G Shan
- Department of Otorhinolaryngology Head and Neck Surgery, the Second Hospital of Hebei Medical University, Shijiazhuang 050000, China
| | - G K Wang
- Department of Otorhinolaryngology Head and Neck Surgery, Henan Provincial People's Hospital, Zhengzhou 450003, China
| | - J Ye
- Department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital of Nanchang University, Nanchang 330006, China
| | - S H Qu
- Department of Otorhinolaryngology Head and Neck Surgery, Guangxi Zhuang Autonomous Region People's Hospital, Nanning 530021, China
| | - C Q Zhao
- Department of Otorhinolaryngology Head and Neck Surgery, Shanxi Medical University Affiliated Second Hospital, Taiyuan 030001, China
| | - Z L Wang
- Department of Otorhinolaryngology Head and Neck Surgery, XuanWu Hospital, Capital Medical University, Beijing 100053, China
| | - H B Li
- Department of Otorhinolaryngology Head and Neck Surgery, Eye, Ear, Nose and Throat Hospital, Shanghai Medical College, Fudan University, Shanghai 200031, China
| | - F Liu
- Department of Otorhinolaryngology Head and Neck Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - X B Cui
- Department of Otorhinolaryngology Head and Neck Surgery, Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010059, China
| | - S N Ye
- Department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China
| | - Z Liu
- Department of Otorhinolaryngology Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Y Xu
- Department of Otorhinolaryngology Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - X Cai
- Department of Otorhinolaryngology Head and Neck Surgery, Qinghai Provincial People's Hospital, Xining 810000, China
| | - W Hang
- Department of Otorhinolaryngology Head and Neck Surgery, Tianjin Huanhu Hospital, Tianjin 300350, China
| | - R X Zhang
- Department of Otorhinolaryngology Head and Neck Surgery, Huadong Hospital Affiliated to Fudan University, Shanghai 200040, China
| | - Y L Zhao
- Department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - G D Yu
- Department of Otorhinolaryngology Head and Neck Surgery, Affiliated Hospital of Guizhou Medical University, Guiyang 550004, China
| | - G G Shi
- Department of Otorhinolaryngology Head and Neck Surgery, Shandong Provincial Hospital, Affiliated to Shandong First Medical University, Jinan 250021, China
| | - M P Lu
- Department of Otorhinolaryngology, the First Affiliated Hospital, Nanjing Medical University, Nanjing 210029, China
| | - Y Shen
- Department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Y T Zhao
- Department of Otorhinolaryngology Head and Neck Surgery, the First People's Hospital of Yinchuan, Yinchuan 750001, China
| | - J H Pei
- Department of Otorhinolaryngology, the First People's Hospital of Yunnan Province, Kunming 650100, China
| | - S B Xie
- Department of Otorhinolaryngology Head and Neck Surgery, Xiangya Hospital Central South University, Changsha 410008, China
| | - L G Yu
- Department of Otorhinolaryngology Head and Neck Surgery, the Affiliated Hospital of Qingdao University, Qingdao 266000, China
| | - Y H Liu
- Department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - S S Gu
- Department of Otorhinolaryngology Head and Neck Surgery, Qilu Hospital of Shandong University, Jinan 250012, China
| | - Y C Yang
- Department of Otorhinolaryngology Head and Neck Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - L Cheng
- Department of Otorhinolaryngology, the First Affiliated Hospital, Nanjing Medical University, Nanjing 210029, China
| | - J F Liu
- Department of Otorhinolaryngology Head and Neck Surgery, China-Japan Friendship Hospital, Beijing 100029, China
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Acciarri R, Adams C, Baller B, Basque V, Cavanna F, Co RT, Fitzpatrick RS, Fleming B, Green P, Harnik R, Kelly KJ, Kumar S, Lang K, Lepetic I, Liu Z, Luo X, Lyu KF, Palamara O, Scanavini G, Soderberg M, Spitz J, Szelc AM, Wu W, Yang T. First Constraints on Heavy QCD Axions with a Liquid Argon Time Projection Chamber Using the ArgoNeuT Experiment. Phys Rev Lett 2023; 130:221802. [PMID: 37327426 DOI: 10.1103/physrevlett.130.221802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 04/21/2023] [Indexed: 06/18/2023]
Abstract
We present the results of a search for heavy QCD axions performed by the ArgoNeuT experiment at Fermilab. We search for heavy axions produced in the NuMI neutrino beam target and absorber decaying into dimuon pairs, which can be identified using the unique capabilities of ArgoNeuT and the MINOS near detector. This decay channel is motivated by a broad class of heavy QCD axion models that address the strong CP and axion quality problems with axion masses above the dimuon threshold. We obtain new constraints at a 95% confidence level for heavy axions in the previously unexplored mass range of 0.2-0.9 GeV, for axion decay constants around tens of TeV.
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Affiliation(s)
- R Acciarri
- Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
| | - C Adams
- Argonne National Laboratory, Lemont, Illinois 60439, USA
| | - B Baller
- Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
| | - V Basque
- Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
| | - F Cavanna
- Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
| | - R T Co
- School of Physics and Astronomy, University of Minnesota, Minneapolis, Minnesota 55455, USA
- William I. Fine Theoretical Physics Institute, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | | | - B Fleming
- Yale University, New Haven, Connecticut 06520, USA
| | - P Green
- University of Manchester, Manchester M13 9PL, United Kingdom
- University of Oxford, Oxford OX1 3RH, United Kingdom
| | - R Harnik
- Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
| | - K J Kelly
- CERN, Esplande des Particules, 1211 Geneva 23, Switzerland
| | - S Kumar
- University of California, Berkeley, California 94720, USA
- Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - K Lang
- University of Texas at Austin, Austin, Texas 78712, USA
| | - I Lepetic
- Rutgers University, Piscataway, New Jersey 08854, USA
| | - Z Liu
- School of Physics and Astronomy, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - X Luo
- University of California, Santa Barbara, California, 93106, USA
| | - K F Lyu
- School of Physics and Astronomy, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - O Palamara
- Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
| | - G Scanavini
- Yale University, New Haven, Connecticut 06520, USA
| | - M Soderberg
- Syracuse University, Syracuse, New York 13244, USA
| | - J Spitz
- University of Michigan, Ann Arbor, Michigan 48109, USA
| | - A M Szelc
- University of Edinburgh, Edinburgh EH9 3FD, United Kingdom
| | - W Wu
- Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
| | - T Yang
- Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA
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85
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Yang J, Liu Z, Guo H, Reheman Z, Ye J, Song S, Wang N, Nie W, Nie J. Prevalence and influencing factors of anaemia among pregnant women in rural areas of Northwestern China. Public Health 2023; 220:50-56. [PMID: 37269588 DOI: 10.1016/j.puhe.2023.04.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 03/22/2023] [Accepted: 04/26/2023] [Indexed: 06/05/2023]
Abstract
OBJECTIVES Anaemia during pregnancy is a significant public health problem that adversely impacts both the mother and foetus. However, the factors influencing maternal anaemia in deprived areas of Northwestern China have not yet been thoroughly investigated. This study aimed to describe the prevalence and potential influencing factors of anaemia among expectant mothers in rural areas of Northwestern China. STUDY DESIGN This was a cross-sectional survey. METHODS A cross-sectional survey of 586 expectant mothers was conducted to investigate the prevalence of anaemia, prenatal healthcare coverage, dietary diversity and nutrient supplementation intake. The study population was selected from the sample areas using a random sampling method. Data were collected through a questionnaire, and haemoglobin concentrations were measured by a capillary blood test. RESULTS The results show that 34.8% of the study population were anaemic, with 13% having moderate-to-severe anaemia. The results of the regression analysis showed that diet was not significantly associated with haemoglobin concentrations or the prevalence of anaemia. However, regular prenatal healthcare attendance was found to be an important influencing factor for both haemoglobin concentration (β = 3.67, P = 0.002) and the prevalence of anaemia (odds ratio = 0.59, P = 0.011). CONCLUSIONS Pregnant women receiving regular prenatal care were less likely to be anaemic; thus, it is essential to implement strategies to improve attendance at maternal public health services to reduce the prevalence of maternal anaemia.
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Affiliation(s)
- J Yang
- Center for Experimental Economics in Educational, Shaanxi Normal University, Xi'an, China.
| | - Z Liu
- Center for Experimental Economics in Educational, Shaanxi Normal University, Xi'an, China.
| | - H Guo
- School of Philosophy and Government, Shaanxi Normal University, Xian, China.
| | - Z Reheman
- Center for Experimental Economics in Educational, Shaanxi Normal University, Xi'an, China.
| | - J Ye
- Center for Experimental Economics in Educational, Shaanxi Normal University, Xi'an, China.
| | - S Song
- Center for Experimental Economics in Educational, Shaanxi Normal University, Xi'an, China.
| | - N Wang
- Center for Experimental Economics in Educational, Shaanxi Normal University, Xi'an, China.
| | - W Nie
- Center for Experimental Economics in Educational, Shaanxi Normal University, Xi'an, China.
| | - J Nie
- Center for Experimental Economics in Educational, Shaanxi Normal University, Xi'an, China.
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86
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Aboona BE, Adam J, Adams JR, Agakishiev G, Aggarwal I, Aggarwal MM, Ahammed Z, Aitbaev A, Alekseev I, Anderson DM, Aparin A, Atchison J, Averichev GS, Bairathi V, Baker W, Ball Cap JG, Barish K, Bhagat P, Bhasin A, Bhatta S, Bordyuzhin IG, Brandenburg JD, Brandin AV, Cai XZ, Caines H, Calderón de la Barca Sánchez M, Cebra D, Ceska J, Chakaberia I, Chan BK, Chang Z, Chen D, Chen J, Chen JH, Chen Z, Cheng J, Cheng Y, Choudhury S, Christie W, Chu X, Crawford HJ, Dale-Gau G, Das A, Daugherity M, Dedovich TG, Deppner IM, Derevschikov AA, Dhamija A, Di Carlo L, Didenko L, Dixit P, Dong X, Drachenberg JL, Duckworth E, Dunlop JC, Engelage J, Eppley G, Esumi S, Evdokimov O, Ewigleben A, Eyser O, Fatemi R, Fazio S, Feng CJ, Feng Y, Finch E, Fisyak Y, Flor FA, Fu C, Geurts F, Ghimire N, Gibson A, Gopal K, Gou X, Grosnick D, Gupta A, Hamed A, Han Y, Harasty MD, Harris JW, Harrison H, He W, He XH, He Y, Hu C, Hu Q, Hu Y, Huang H, Huang HZ, Huang SL, Huang T, Huang X, Huang Y, Huang Y, Humanic TJ, Isenhower D, Isshiki M, Jacobs WW, Jalotra A, Jena C, Ji Y, Jia J, Jin C, Ju X, Judd EG, Kabana S, Kabir ML, Kalinkin D, Kang K, Kapukchyan D, Kauder K, Ke HW, Keane D, Kechechyan A, Kelsey M, Kimelman B, Kiselev A, Knospe AG, Ko HS, Kochenda L, Korobitsin AA, Kravtsov P, Kumar L, Kumar S, Kunnawalkam Elayavalli R, Lacey R, Landgraf JM, Lebedev A, Lednicky R, Lee JH, Leung YH, Lewis N, Li C, Li C, Li W, Li X, Li Y, Li Y, Li Z, Liang X, Liang Y, Lin T, Liu C, Liu F, Liu H, Liu H, Liu L, Liu T, Liu X, Liu Y, Liu Z, Ljubicic T, Llope WJ, Lomicky O, Longacre RS, Loyd E, Lu T, Lukow NS, Luo XF, Luong VB, Ma L, Ma R, Ma YG, Magdy N, Mallick D, Margetis S, Matis HS, Mazer JA, McNamara G, Mi K, Minaev NG, Mohanty B, Mooney I, Morozov DA, Mudrokh A, Nagy MI, Nain AS, Nam JD, Nasim M, Neff D, Nelson JM, Nemes DB, Nie M, Nigmatkulov G, Niida T, Nishitani R, Nogach LV, Nonaka T, Nunes AS, Odyniec G, Ogawa A, Oh S, Okorokov VA, Okubo K, Page BS, Pak R, Pan J, Pandav A, Pandey AK, Panebratsev Y, Pani T, Parfenov P, Paul A, Perkins C, Pokhrel BR, Posik M, Protzman T, Pruthi NK, Putschke J, Qin Z, Qiu H, Quintero A, Racz C, Radhakrishnan SK, Raha N, Ray RL, Ritter HG, Robertson CW, Rogachevsky OV, Rosales Aguilar MA, Roy D, Ruan L, Sahoo AK, Sahoo NR, Sako H, Salur S, Samigullin E, Sato S, Schmidke WB, Schmitz N, Seger J, Seto R, Seyboth P, Shah N, Shahaliev E, Shanmuganathan PV, Shao M, Shao T, Sharma M, Sharma N, Sharma R, Sharma SR, Sheikh AI, Shen DY, Shen K, Shi SS, Shi Y, Shou QY, Si F, Singh J, Singha S, Sinha P, Skoby MJ, Söhngen Y, Song Y, Srivastava B, Stanislaus TDS, Stewart DJ, Strikhanov M, Stringfellow B, Su Y, Sun C, Sun X, Sun Y, Sun Y, Surrow B, Svirida DN, Sweger ZW, Tamis A, Tang AH, Tang Z, Taranenko A, Tarnowsky T, Thomas JH, Tlusty D, Todoroki T, Tokarev MV, Tomkiel CA, Trentalange S, Tribble RE, Tribedy P, Tsai OD, Tsang CY, Tu Z, Ullrich T, Underwood DG, Upsal I, Van Buren G, Vasiliev AN, Verkest V, Videbæk F, Vokal S, Voloshin SA, Wang F, Wang G, Wang JS, Wang X, Wang Y, Wang Y, Wang Y, Wang Z, Webb JC, Weidenkaff PC, Westfall GD, Wieman H, Wilks G, Wissink SW, Wu J, Wu J, Wu X, Wu Y, Xi B, Xiao ZG, Xie W, Xu H, Xu N, Xu QH, Xu Y, Xu Y, Xu Z, Xu Z, Yan G, Yan Z, Yang C, Yang Q, Yang S, Yang Y, Ye Z, Ye Z, Yi L, Yip K, Yu Y, Zha W, Zhang C, Zhang D, Zhang J, Zhang S, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhang ZJ, Zhang Z, Zhang Z, Zhao F, Zhao J, Zhao M, Zhou C, Zhou J, Zhou S, Zhou Y, Zhu X, Zurek M, Zyzak M. Observation of Directed Flow of Hypernuclei _{Λ}^{3}H and _{Λ}^{4}H in sqrt[s_{NN}]=3 GeV Au+Au Collisions at RHIC. Phys Rev Lett 2023; 130:212301. [PMID: 37295104 DOI: 10.1103/physrevlett.130.212301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/24/2023] [Accepted: 03/02/2023] [Indexed: 06/12/2023]
Abstract
We report here the first observation of directed flow (v_{1}) of the hypernuclei _{Λ}^{3}H and _{Λ}^{4}H in mid-central Au+Au collisions at sqrt[s_{NN}]=3 GeV at RHIC. These data are taken as part of the beam energy scan program carried out by the STAR experiment. From 165×10^{6} events in 5%-40% centrality, about 8400 _{Λ}^{3}H and 5200 _{Λ}^{4}H candidates are reconstructed through two- and three-body decay channels. We observe that these hypernuclei exhibit significant directed flow. Comparing to that of light nuclei, it is found that the midrapidity v_{1} slopes of _{Λ}^{3}H and _{Λ}^{4}H follow baryon number scaling, implying that the coalescence is the dominant mechanism for these hypernuclei production in the 3 GeV Au+Au collisions.
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Affiliation(s)
- B E Aboona
- Texas A&M University, College Station, Texas 77843
| | - J Adam
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - J R Adams
- Ohio State University, Columbus, Ohio 43210
| | - G Agakishiev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - I Aggarwal
- Panjab University, Chandigarh 160014, India
| | | | - Z Ahammed
- Variable Energy Cyclotron Centre, Kolkata 700064, India
| | - A Aitbaev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - I Alekseev
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute," Moscow 117218
- National Research Nuclear University MEPhI, Moscow 115409
| | - D M Anderson
- Texas A&M University, College Station, Texas 77843
| | - A Aparin
- Joint Institute for Nuclear Research, Dubna 141 980
| | - J Atchison
- Abilene Christian University, Abilene, Texas 79699
| | | | - V Bairathi
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
| | - W Baker
- University of California, Riverside, California 92521
| | | | - K Barish
- University of California, Riverside, California 92521
| | - P Bhagat
- University of Jammu, Jammu 180001, India
| | - A Bhasin
- University of Jammu, Jammu 180001, India
| | - S Bhatta
- State University of New York, Stony Brook, New York 11794
| | - I G Bordyuzhin
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute," Moscow 117218
| | | | - A V Brandin
- National Research Nuclear University MEPhI, Moscow 115409
| | - X Z Cai
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800
| | - H Caines
- Yale University, New Haven, Connecticut 06520
| | | | - D Cebra
- University of California, Davis, California 95616
| | - J Ceska
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - I Chakaberia
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - B K Chan
- University of California, Los Angeles, California 90095
| | - Z Chang
- Indiana University, Bloomington, Indiana 47408
| | - D Chen
- University of California, Riverside, California 92521
| | - J Chen
- Shandong University, Qingdao, Shandong 266237
| | - J H Chen
- Fudan University, Shanghai, 200433
| | - Z Chen
- Shandong University, Qingdao, Shandong 266237
| | - J Cheng
- Tsinghua University, Beijing 100084
| | - Y Cheng
- University of California, Los Angeles, California 90095
| | | | - W Christie
- Brookhaven National Laboratory, Upton, New York 11973
| | - X Chu
- Brookhaven National Laboratory, Upton, New York 11973
| | - H J Crawford
- University of California, Berkeley, California 94720
| | - G Dale-Gau
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - A Das
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - M Daugherity
- Abilene Christian University, Abilene, Texas 79699
| | - T G Dedovich
- Joint Institute for Nuclear Research, Dubna 141 980
| | - I M Deppner
- University of Heidelberg, Heidelberg 69120, Germany
| | - A A Derevschikov
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - A Dhamija
- Panjab University, Chandigarh 160014, India
| | - L Di Carlo
- Wayne State University, Detroit, Michigan 48201
| | - L Didenko
- Brookhaven National Laboratory, Upton, New York 11973
| | - P Dixit
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - X Dong
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | | | - J C Dunlop
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Engelage
- University of California, Berkeley, California 94720
| | - G Eppley
- Rice University, Houston, Texas 77251
| | - S Esumi
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - O Evdokimov
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - A Ewigleben
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - O Eyser
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Fatemi
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - S Fazio
- University of Calabria & INFN-Cosenza, Italy
| | - C J Feng
- National Cheng Kung University, Tainan 70101
| | - Y Feng
- Purdue University, West Lafayette, Indiana 47907
| | - E Finch
- Southern Connecticut State University, New Haven, Connecticut 06515
| | - Y Fisyak
- Brookhaven National Laboratory, Upton, New York 11973
| | - F A Flor
- Yale University, New Haven, Connecticut 06520
| | - C Fu
- Central China Normal University, Wuhan, Hubei 430079
| | - F Geurts
- Rice University, Houston, Texas 77251
| | - N Ghimire
- Temple University, Philadelphia, Pennsylvania 19122
| | - A Gibson
- Valparaiso University, Valparaiso, Indiana 46383
| | - K Gopal
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - X Gou
- Shandong University, Qingdao, Shandong 266237
| | - D Grosnick
- Valparaiso University, Valparaiso, Indiana 46383
| | - A Gupta
- University of Jammu, Jammu 180001, India
| | - A Hamed
- American University of Cairo, New Cairo 11835, New Cairo, Egypt
| | - Y Han
- Rice University, Houston, Texas 77251
| | - M D Harasty
- University of California, Davis, California 95616
| | - J W Harris
- Yale University, New Haven, Connecticut 06520
| | - H Harrison
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - W He
- Fudan University, Shanghai, 200433
| | - X H He
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y He
- Shandong University, Qingdao, Shandong 266237
| | - C Hu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Q Hu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Hu
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - H Huang
- National Cheng Kung University, Tainan 70101
| | - H Z Huang
- University of California, Los Angeles, California 90095
| | - S L Huang
- State University of New York, Stony Brook, New York 11794
| | - T Huang
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - X Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Central China Normal University, Wuhan, Hubei 430079
| | | | - D Isenhower
- Abilene Christian University, Abilene, Texas 79699
| | - M Isshiki
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - W W Jacobs
- Indiana University, Bloomington, Indiana 47408
| | - A Jalotra
- University of Jammu, Jammu 180001, India
| | - C Jena
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - Y Ji
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J Jia
- Brookhaven National Laboratory, Upton, New York 11973
- State University of New York, Stony Brook, New York 11794
| | - C Jin
- Rice University, Houston, Texas 77251
| | - X Ju
- University of Science and Technology of China, Hefei, Anhui 230026
| | - E G Judd
- University of California, Berkeley, California 94720
| | - S Kabana
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
| | - M L Kabir
- University of California, Riverside, California 92521
| | - D Kalinkin
- Brookhaven National Laboratory, Upton, New York 11973
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - K Kang
- Tsinghua University, Beijing 100084
| | - D Kapukchyan
- University of California, Riverside, California 92521
| | - K Kauder
- Brookhaven National Laboratory, Upton, New York 11973
| | - H W Ke
- Brookhaven National Laboratory, Upton, New York 11973
| | - D Keane
- Kent State University, Kent, Ohio 44242
| | - A Kechechyan
- Joint Institute for Nuclear Research, Dubna 141 980
| | - M Kelsey
- Wayne State University, Detroit, Michigan 48201
| | - B Kimelman
- University of California, Davis, California 95616
| | - A Kiselev
- Brookhaven National Laboratory, Upton, New York 11973
| | - A G Knospe
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - H S Ko
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - L Kochenda
- National Research Nuclear University MEPhI, Moscow 115409
| | | | - P Kravtsov
- National Research Nuclear University MEPhI, Moscow 115409
| | - L Kumar
- Panjab University, Chandigarh 160014, India
| | - S Kumar
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | | | - R Lacey
- State University of New York, Stony Brook, New York 11794
| | - J M Landgraf
- Brookhaven National Laboratory, Upton, New York 11973
| | - A Lebedev
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Lednicky
- Joint Institute for Nuclear Research, Dubna 141 980
| | - J H Lee
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y H Leung
- University of Heidelberg, Heidelberg 69120, Germany
| | - N Lewis
- Brookhaven National Laboratory, Upton, New York 11973
| | - C Li
- Shandong University, Qingdao, Shandong 266237
| | - C Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - W Li
- Rice University, Houston, Texas 77251
| | - X Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Li
- Tsinghua University, Beijing 100084
| | - Z Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - X Liang
- University of California, Riverside, California 92521
| | - Y Liang
- Kent State University, Kent, Ohio 44242
| | - T Lin
- Shandong University, Qingdao, Shandong 266237
| | - C Liu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - F Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - H Liu
- Indiana University, Bloomington, Indiana 47408
| | - H Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - L Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - T Liu
- Yale University, New Haven, Connecticut 06520
| | - X Liu
- Ohio State University, Columbus, Ohio 43210
| | - Y Liu
- Texas A&M University, College Station, Texas 77843
| | - Z Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - T Ljubicic
- Brookhaven National Laboratory, Upton, New York 11973
| | - W J Llope
- Wayne State University, Detroit, Michigan 48201
| | - O Lomicky
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - R S Longacre
- Brookhaven National Laboratory, Upton, New York 11973
| | - E Loyd
- University of California, Riverside, California 92521
| | - T Lu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - N S Lukow
- Temple University, Philadelphia, Pennsylvania 19122
| | - X F Luo
- Central China Normal University, Wuhan, Hubei 430079
| | - V B Luong
- Joint Institute for Nuclear Research, Dubna 141 980
| | - L Ma
- Fudan University, Shanghai, 200433
| | - R Ma
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y G Ma
- Fudan University, Shanghai, 200433
| | - N Magdy
- State University of New York, Stony Brook, New York 11794
| | - D Mallick
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | | | - H S Matis
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J A Mazer
- Rutgers University, Piscataway, New Jersey 08854
| | - G McNamara
- Wayne State University, Detroit, Michigan 48201
| | - K Mi
- Central China Normal University, Wuhan, Hubei 430079
| | - N G Minaev
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - B Mohanty
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - I Mooney
- Yale University, New Haven, Connecticut 06520
| | - D A Morozov
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - A Mudrokh
- Joint Institute for Nuclear Research, Dubna 141 980
| | - M I Nagy
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - A S Nain
- Panjab University, Chandigarh 160014, India
| | - J D Nam
- Temple University, Philadelphia, Pennsylvania 19122
| | - Md Nasim
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - D Neff
- University of California, Los Angeles, California 90095
| | - J M Nelson
- University of California, Berkeley, California 94720
| | - D B Nemes
- Yale University, New Haven, Connecticut 06520
| | - M Nie
- Shandong University, Qingdao, Shandong 266237
| | - G Nigmatkulov
- National Research Nuclear University MEPhI, Moscow 115409
| | - T Niida
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - R Nishitani
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - L V Nogach
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - T Nonaka
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - A S Nunes
- Brookhaven National Laboratory, Upton, New York 11973
| | - G Odyniec
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - A Ogawa
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Oh
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - V A Okorokov
- National Research Nuclear University MEPhI, Moscow 115409
| | - K Okubo
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - B S Page
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Pak
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Pan
- Texas A&M University, College Station, Texas 77843
| | - A Pandav
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - A K Pandey
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | | | - T Pani
- Rutgers University, Piscataway, New Jersey 08854
| | - P Parfenov
- National Research Nuclear University MEPhI, Moscow 115409
| | - A Paul
- University of California, Riverside, California 92521
| | - C Perkins
- University of California, Berkeley, California 94720
| | - B R Pokhrel
- Temple University, Philadelphia, Pennsylvania 19122
| | - M Posik
- Temple University, Philadelphia, Pennsylvania 19122
| | - T Protzman
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - N K Pruthi
- Panjab University, Chandigarh 160014, India
| | - J Putschke
- Wayne State University, Detroit, Michigan 48201
| | - Z Qin
- Tsinghua University, Beijing 100084
| | - H Qiu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - A Quintero
- Temple University, Philadelphia, Pennsylvania 19122
| | - C Racz
- University of California, Riverside, California 92521
| | | | - N Raha
- Wayne State University, Detroit, Michigan 48201
| | - R L Ray
- University of Texas, Austin, Texas 78712
| | - H G Ritter
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | | | | | - D Roy
- Rutgers University, Piscataway, New Jersey 08854
| | - L Ruan
- Brookhaven National Laboratory, Upton, New York 11973
| | - A K Sahoo
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - N R Sahoo
- Shandong University, Qingdao, Shandong 266237
| | - H Sako
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - S Salur
- Rutgers University, Piscataway, New Jersey 08854
| | - E Samigullin
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute," Moscow 117218
| | - S Sato
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - W B Schmidke
- Brookhaven National Laboratory, Upton, New York 11973
| | - N Schmitz
- Max-Planck-Institut für Physik, Munich 80805, Germany
| | - J Seger
- Creighton University, Omaha, Nebraska 68178
| | - R Seto
- University of California, Riverside, California 92521
| | - P Seyboth
- Max-Planck-Institut für Physik, Munich 80805, Germany
| | - N Shah
- Indian Institute Technology, Patna, Bihar 801106, India
| | - E Shahaliev
- Joint Institute for Nuclear Research, Dubna 141 980
| | | | - M Shao
- University of Science and Technology of China, Hefei, Anhui 230026
| | - T Shao
- Fudan University, Shanghai, 200433
| | - M Sharma
- University of Jammu, Jammu 180001, India
| | - N Sharma
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - R Sharma
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - S R Sharma
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | | | - D Y Shen
- Fudan University, Shanghai, 200433
| | - K Shen
- University of Science and Technology of China, Hefei, Anhui 230026
| | - S S Shi
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Shi
- Shandong University, Qingdao, Shandong 266237
| | - Q Y Shou
- Fudan University, Shanghai, 200433
| | - F Si
- University of Science and Technology of China, Hefei, Anhui 230026
| | - J Singh
- Panjab University, Chandigarh 160014, India
| | - S Singha
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - P Sinha
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - M J Skoby
- Ball State University, Muncie, Indiana, 47306
- Purdue University, West Lafayette, Indiana 47907
| | - Y Söhngen
- University of Heidelberg, Heidelberg 69120, Germany
| | - Y Song
- Yale University, New Haven, Connecticut 06520
| | - B Srivastava
- Purdue University, West Lafayette, Indiana 47907
| | | | - D J Stewart
- Wayne State University, Detroit, Michigan 48201
| | - M Strikhanov
- National Research Nuclear University MEPhI, Moscow 115409
| | | | - Y Su
- University of Science and Technology of China, Hefei, Anhui 230026
| | - C Sun
- State University of New York, Stony Brook, New York 11794
| | - X Sun
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Sun
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Sun
- Huzhou University, Huzhou, Zhejiang 313000
| | - B Surrow
- Temple University, Philadelphia, Pennsylvania 19122
| | - D N Svirida
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute," Moscow 117218
| | - Z W Sweger
- University of California, Davis, California 95616
| | - A Tamis
- Yale University, New Haven, Connecticut 06520
| | - A H Tang
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Tang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - A Taranenko
- National Research Nuclear University MEPhI, Moscow 115409
| | - T Tarnowsky
- Michigan State University, East Lansing, Michigan 48824
| | - J H Thomas
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - D Tlusty
- Creighton University, Omaha, Nebraska 68178
| | - T Todoroki
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - M V Tokarev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - C A Tomkiel
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - S Trentalange
- University of California, Los Angeles, California 90095
| | - R E Tribble
- Texas A&M University, College Station, Texas 77843
| | - P Tribedy
- Brookhaven National Laboratory, Upton, New York 11973
| | - O D Tsai
- Brookhaven National Laboratory, Upton, New York 11973
- University of California, Los Angeles, California 90095
| | - C Y Tsang
- Brookhaven National Laboratory, Upton, New York 11973
- Kent State University, Kent, Ohio 44242
| | - Z Tu
- Brookhaven National Laboratory, Upton, New York 11973
| | - T Ullrich
- Brookhaven National Laboratory, Upton, New York 11973
| | - D G Underwood
- Argonne National Laboratory, Argonne, Illinois 60439
- Valparaiso University, Valparaiso, Indiana 46383
| | - I Upsal
- Rice University, Houston, Texas 77251
| | - G Van Buren
- Brookhaven National Laboratory, Upton, New York 11973
| | - A N Vasiliev
- National Research Nuclear University MEPhI, Moscow 115409
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - V Verkest
- Wayne State University, Detroit, Michigan 48201
| | - F Videbæk
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Vokal
- Joint Institute for Nuclear Research, Dubna 141 980
| | | | - F Wang
- Purdue University, West Lafayette, Indiana 47907
| | - G Wang
- University of California, Los Angeles, California 90095
| | - J S Wang
- Huzhou University, Huzhou, Zhejiang 313000
| | - X Wang
- Shandong University, Qingdao, Shandong 266237
| | - Y Wang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Wang
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Wang
- Tsinghua University, Beijing 100084
| | - Z Wang
- Shandong University, Qingdao, Shandong 266237
| | - J C Webb
- Brookhaven National Laboratory, Upton, New York 11973
| | | | - G D Westfall
- Michigan State University, East Lansing, Michigan 48824
| | - H Wieman
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - G Wilks
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - S W Wissink
- Indiana University, Bloomington, Indiana 47408
| | - J Wu
- Central China Normal University, Wuhan, Hubei 430079
| | - J Wu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - X Wu
- University of California, Los Angeles, California 90095
| | - Y Wu
- University of California, Riverside, California 92521
| | - B Xi
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800
| | - Z G Xiao
- Tsinghua University, Beijing 100084
| | - W Xie
- Purdue University, West Lafayette, Indiana 47907
| | - H Xu
- Huzhou University, Huzhou, Zhejiang 313000
| | - N Xu
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - Q H Xu
- Shandong University, Qingdao, Shandong 266237
| | - Y Xu
- Shandong University, Qingdao, Shandong 266237
| | - Y Xu
- Central China Normal University, Wuhan, Hubei 430079
| | - Z Xu
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Xu
- University of California, Los Angeles, California 90095
| | - G Yan
- Shandong University, Qingdao, Shandong 266237
| | - Z Yan
- State University of New York, Stony Brook, New York 11794
| | - C Yang
- Shandong University, Qingdao, Shandong 266237
| | - Q Yang
- Shandong University, Qingdao, Shandong 266237
| | - S Yang
- South China Normal University, Guangzhou, Guangdong 510631
| | - Y Yang
- National Cheng Kung University, Tainan 70101
| | - Z Ye
- Rice University, Houston, Texas 77251
| | - Z Ye
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - L Yi
- Shandong University, Qingdao, Shandong 266237
| | - K Yip
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y Yu
- Shandong University, Qingdao, Shandong 266237
| | - W Zha
- University of Science and Technology of China, Hefei, Anhui 230026
| | - C Zhang
- State University of New York, Stony Brook, New York 11794
| | - D Zhang
- Central China Normal University, Wuhan, Hubei 430079
| | - J Zhang
- Shandong University, Qingdao, Shandong 266237
| | - S Zhang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - X Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Zhang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Zhang
- Central China Normal University, Wuhan, Hubei 430079
| | - Z J Zhang
- National Cheng Kung University, Tainan 70101
| | - Z Zhang
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Zhang
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - F Zhao
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - J Zhao
- Fudan University, Shanghai, 200433
| | - M Zhao
- Brookhaven National Laboratory, Upton, New York 11973
| | - C Zhou
- Fudan University, Shanghai, 200433
| | - J Zhou
- University of Science and Technology of China, Hefei, Anhui 230026
| | - S Zhou
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Zhou
- Central China Normal University, Wuhan, Hubei 430079
| | - X Zhu
- Tsinghua University, Beijing 100084
| | - M Zurek
- Argonne National Laboratory, Argonne, Illinois 60439
| | - M Zyzak
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
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Li S, Liu Z. [Clinical characteristics of cardiac defects fetuses and the impact of multi-disciplinary team cooperation approach on the pregnancy decision making]. Zhonghua Fu Chan Ke Za Zhi 2023; 58:326-333. [PMID: 37217339 DOI: 10.3760/cma.j.cn112141-20221205-00740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Objective: To analysis the clinical characteristics of 400 fetuses with heart defects and the impactors of pregnancy decision making, and explore the influence of a multi-disciplinary team (MDT) cooperation approach on it. Methods: Clinical data of 400 fetuses with abnormal cardiac structure diagnosed at Peking University First Hospital from January 2012 to June 2021 were collected, which were divided into 4 groups according to the characteristics of fetal heart defects and the presence of extracardiac abnormalities or not: single cardiac defects without extracardiac abnormalities (122 cases), multiple cardiac defects without extracardiac abnormalities (100 cases), single cardiac defects with extracardiac abnormalities (115 cases), and multiple cardiac defects with extracardiac abnormalities (63 cases). The types of fetal cardiac structural abnormalities and genetic test results, and the detection rate of pathogenic genetic abnormalities, MDT consultation and management situation, and pregnancy decision of fetuses in each group were retrospectively analyzed. A logistics regression was used to analyze the influencing factors of fetal heart defects pregnancy decision. Results: (1) Among the 400 fetal heart defects, the four most common major types were ventricular septal defect 96 (24.0%, 96/400), tetralogy of Fallot 52 (13.0%, 52/400), coarctation of the aorta 34 (8.5%, 34/400), and atrioventricular septal defect 26 (6.5%, 26/400). (2) Among the 204 fetuses undergoing genetic examination, 44 (21.6%, 44/204) pathogenic genetic abnormalities were detected. (3) Detection rate of pathogenic genetic abnormalities (39.3%, 24/61) and pregnancy termination rate (86.1%, 99/115) in the single cardiac defects with extracardiac abnormalities group were significantly higher than those in the single cardiac defects without extracardiac abnormalities group [15.1% (8/53), 44.3% (54/122), respectively] and the multiple cardiac defects without extracardiac abnormalities group [6.1% (3/49), 70.0% (70/100), respectively, both P<0.05], and the pregnancy termination rate in the multiple cardiac defects without extracardiac abnormalities group and the multiple cardiac defects with extracardiac abnormalities group (82.5%,52/63) were significantly higher than that of the single cardiac abnormalities without extracardiac abnormalities group (both P<0.05). (4) After adjusting for age, gravity, parity and performed prenatal diagnosis, maternal age, the diagnosis of gestational age, prognosis grades, co-existence of extracardiac abnormalities, presence of pathogenic genetic abnormalities, and receiving MDT consultation and management were still independent influencing factors of termination of pregnancy of fetuses with cardiac defects (all P<0.05). A total of 29 (7.2%, 29/400) fetal cardiac defects received MDT consultation and management, and compared with those without MDT management, the pregnancy termination rate in the multiple cardiac defects without extracardiac abnormalities group [74.2%(66/89) vs 4/11] and the multiple cardiac defects with extracardiac abnormalities group [87.9%(51/58) vs 1/5] were lower, the differences were statistically significant respectively (all P<0.05). Conclusions: Maternal age, diagnosed gestational age, severity of cardiac defects, extracardiac abnormalities, pathogenic genetic abnormalities and MDT counseling and management are the influencing factors of fetal heart defects pregnancy decision. MDT cooperation approach influences pregnancy decision-making and should be recommended for the management of fetal cardiac defect to reduce unnecessary termination of pregnancy and improve pregnancy outcomes.
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Affiliation(s)
- S Li
- Department of Obstetrics and Gynecology, Peking University First Hospital, Beijing 100034, China
| | - Z Liu
- Department of Obstetrics and Gynecology, Peking University First Hospital, Beijing 100034, China
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Gong G, Cao S, Xiao H, Fang W, Que Y, Liu Z, Chen C. [Prediction of microvascular invasion in hepatocellular carcinoma with magnetic resonance imaging using models combining deep attention mechanism with clinical features]. Nan Fang Yi Ke Da Xue Xue Bao 2023; 43:839-851. [PMID: 37313827 DOI: 10.12122/j.issn.1673-4254.2023.05.21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
OBJECTIVE To investigate the consistency and diagnostic performance of magnetic resonance imaging (MRI) for detecting microvascular invasion (MVI) of hepatocellular carcinoma (HCC) and the validity of deep learning attention mechanisms and clinical features for MVI grade prediction. METHODS This retrospective study was conducted among 158 patients with HCC treated in Shunde Hospital Affiliated to Southern Medical University between January, 2017 and February, 2020. The imaging data and clinical data of the patients were collected to establish single sequence deep learning models and fusion models based on the EfficientNetB0 and attention modules. The imaging data included conventional MRI sequences (T1WI, T2WI, and DWI), enhanced MRI sequences (AP, PP, EP, and HBP) and synthesized MRI sequences (T1mapping-pre and T1mapping-20 min), and the high-risk areas of MVI were visualized using deep learning visualization techniques. RESULTS The fusion model based on T1mapping-20min sequence and clinical features outperformed other fusion models with an accuracy of 0.8376, a sensitivity of 0.8378, a specificity of 0.8702, and an AUC of 0.8501 for detecting MVI. The deep fusion models were also capable of displaying the high-risk areas of MVI. CONCLUSION The fusion models based on multiple MRI sequences can effectively detect MVI in patients with HCC, demonstrating the validity of deep learning algorithm that combines attention mechanism and clinical features for MVI grade prediction.
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Affiliation(s)
- G Gong
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - S Cao
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - H Xiao
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - W Fang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Y Que
- First Affiliated Hospital of Nanchang University, Nanchang 330006, China
| | - Z Liu
- Shunde Hospital Affiliated to Southern Medical University, Foshan 528308, China
| | - C Chen
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
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89
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Abdulhamid MI, Aboona BE, Adam J, Adams JR, Agakishiev G, Aggarwal I, Aggarwal MM, Ahammed Z, Aitbaev A, Alekseev I, Anderson DM, Aparin A, Aslam S, Atchison J, Averichev GS, Bairathi V, Baker W, Ball Cap JG, Barish K, Bhagat P, Bhasin A, Bhatta S, Bordyuzhin IG, Brandenburg JD, Brandin AV, Cai XZ, Caines H, Calderón de la Barca Sánchez M, Cebra D, Ceska J, Chakaberia I, Chan BK, Chang Z, Chatterjee A, Chen D, Chen J, Chen JH, Chen Z, Cheng J, Cheng Y, Choudhury S, Christie W, Chu X, Crawford HJ, Dale-Gau G, Das A, Daugherity M, Dedovich TG, Deppner IM, Derevschikov AA, Dhamija A, Di Carlo L, Didenko L, Dixit P, Dong X, Drachenberg JL, Duckworth E, Dunlop JC, Engelage J, Eppley G, Esumi S, Evdokimov O, Ewigleben A, Eyser O, Fatemi R, Fazio S, Feng CJ, Feng Y, Finch E, Fisyak Y, Flor FA, Fu C, Geurts F, Ghimire N, Gibson A, Gopal K, Gou X, Grosnick D, Gupta A, Hamed A, Han Y, Harasty MD, Harris JW, Harrison-Smith H, He W, He XH, He Y, Hu C, Hu Q, Hu Y, Huang H, Huang HZ, Huang SL, Huang T, Huang X, Huang Y, Huang Y, Humanic TJ, Isenhower D, Isshiki M, Jacobs WW, Jalotra A, Jena C, Ji Y, Jia J, Jin C, Ju X, Judd EG, Kabana S, Kabir ML, Kalinkin D, Kang K, Kapukchyan D, Kauder K, Ke HW, Keane D, Kechechyan A, Kelsey M, Kimelman B, Kiselev A, Knospe AG, Ko HS, Kochenda L, Korobitsin AA, Kravtsov P, Kumar L, Kumar S, Kunnawalkam Elayavalli R, Lacey R, Landgraf JM, Lebedev A, Lednicky R, Lee JH, Leung YH, Lewis N, Li C, Li W, Li X, Li Y, Li Y, Li Z, Liang X, Liang Y, Lin T, Liu C, Liu F, Liu H, Liu H, Liu L, Liu T, Liu X, Liu Y, Liu Z, Ljubicic T, Llope WJ, Lomicky O, Longacre RS, Loyd EM, Lu T, Lukow NS, Luo XF, Luong VB, Ma L, Ma R, Ma YG, Magdy N, Mallick D, Margetis S, Matis HS, Mazer JA, McNamara G, Mi K, Minaev NG, Mohanty B, Mondal MM, Mooney I, Morozov DA, Mudrokh A, Nagy MI, Nain AS, Nam JD, Nasim M, Neff D, Nelson JM, Nemes DB, Nie M, Nigmatkulov G, Niida T, Nishitani R, Nogach LV, Nonaka T, Odyniec G, Ogawa A, Oh S, Okorokov VA, Okubo K, Page BS, Pak R, Pan J, Pandav A, Pandey AK, Panebratsev Y, Pani T, Parfenov P, Paul A, Perkins C, Pokhrel BR, Posik M, Protzman T, Pruthi NK, Putschke J, Qin Z, Qiu H, Quintero A, Racz C, Radhakrishnan SK, Raha N, Ray RL, Ritter HG, Robertson CW, Rogachevsky OV, Rosales Aguilar MA, Roy D, Ruan L, Sahoo AK, Sahoo NR, Sako H, Salur S, Samigullin E, Sato S, Schmidke WB, Schmitz N, Seger J, Seto R, Seyboth P, Shah N, Shahaliev E, Shanmuganathan PV, Shao T, Sharma M, Sharma N, Sharma R, Sharma SR, Sheikh AI, Shen DY, Shen K, Shi SS, Shi Y, Shou QY, Si F, Singh J, Singha S, Sinha P, Skoby MJ, Söhngen Y, Song Y, Srivastava B, Stanislaus TDS, Stewart DJ, Strikhanov M, Stringfellow B, Su Y, Sun C, Sun X, Sun Y, Sun Y, Surrow B, Svirida DN, Sweger ZW, Tamis A, Tang AH, Tang Z, Taranenko A, Tarnowsky T, Thomas JH, Tlusty D, Todoroki T, Tokarev MV, Tomkiel CA, Trentalange S, Tribble RE, Tribedy P, Tsai OD, Tsang CY, Tu Z, Ullrich T, Underwood DG, Upsal I, Van Buren G, Vasiliev AN, Verkest V, Videbæk F, Vokal S, Voloshin SA, Wang F, Wang G, Wang JS, Wang X, Wang Y, Wang Y, Wang Y, Wang Z, Webb JC, Weidenkaff PC, Westfall GD, Wieman H, Wilks G, Wissink SW, Wu J, Wu J, Wu X, Wu Y, Xi B, Xiao ZG, Xie G, Xie W, Xu H, Xu N, Xu QH, Xu Y, Xu Y, Xu Z, Xu Z, Yan G, Yan Z, Yang C, Yang Q, Yang S, Yang Y, Ye Z, Ye Z, Yi L, Yip K, Yu N, Yu Y, Zha W, Zhang C, Zhang D, Zhang J, Zhang S, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhang ZJ, Zhang Z, Zhang Z, Zhao F, Zhao J, Zhao M, Zhou C, Zhou J, Zhou S, Zhou Y, Zhu X, Zurek M, Zyzak M. Beam Energy Dependence of Triton Production and Yield Ratio (N_{t}×N_{p}/N_{d}^{2}) in Au+Au Collisions at RHIC. Phys Rev Lett 2023; 130:202301. [PMID: 37267557 DOI: 10.1103/physrevlett.130.202301] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 02/21/2023] [Accepted: 03/30/2023] [Indexed: 06/04/2023]
Abstract
We report the triton (t) production in midrapidity (|y|<0.5) Au+Au collisions at sqrt[s_{NN}]=7.7-200 GeV measured by the STAR experiment from the first phase of the beam energy scan at the Relativistic Heavy Ion Collider. The nuclear compound yield ratio (N_{t}×N_{p}/N_{d}^{2}), which is predicted to be sensitive to the fluctuation of local neutron density, is observed to decrease monotonically with increasing charged-particle multiplicity (dN_{ch}/dη) and follows a scaling behavior. The dN_{ch}/dη dependence of the yield ratio is compared to calculations from coalescence and thermal models. Enhancements in the yield ratios relative to the coalescence baseline are observed in the 0%-10% most central collisions at 19.6 and 27 GeV, with a significance of 2.3σ and 3.4σ, respectively, giving a combined significance of 4.1σ. The enhancements are not observed in peripheral collisions or model calculations without critical fluctuation, and decreases with a smaller p_{T} acceptance. The physics implications of these results on the QCD phase structure and the production mechanism of light nuclei in heavy-ion collisions are discussed.
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Affiliation(s)
- M I Abdulhamid
- American University of Cairo, New Cairo 11835, New Cairo, Egypt
| | - B E Aboona
- Texas A&M University, College Station, Texas 77843
| | - J Adam
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - J R Adams
- The Ohio State University, Columbus, Ohio 43210
| | - G Agakishiev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - I Aggarwal
- Panjab University, Chandigarh 160014, India
| | | | - Z Ahammed
- Variable Energy Cyclotron Centre, Kolkata 700064, India
| | - A Aitbaev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - I Alekseev
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute," Moscow 117218
- National Research Nuclear University MEPhI, Moscow 115409
| | - D M Anderson
- Texas A&M University, College Station, Texas 77843
| | - A Aparin
- Joint Institute for Nuclear Research, Dubna 141 980
| | - S Aslam
- Indian Institute Technology, Patna, Bihar 801106, India
| | - J Atchison
- Abilene Christian University, Abilene, Texas 79699
| | | | - V Bairathi
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
| | - W Baker
- University of California, Riverside, California 92521
| | | | - K Barish
- University of California, Riverside, California 92521
| | - P Bhagat
- University of Jammu, Jammu 180001, India
| | - A Bhasin
- University of Jammu, Jammu 180001, India
| | - S Bhatta
- State University of New York, Stony Brook, New York 11794
| | - I G Bordyuzhin
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute," Moscow 117218
| | | | - A V Brandin
- National Research Nuclear University MEPhI, Moscow 115409
| | - X Z Cai
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800
| | - H Caines
- Yale University, New Haven, Connecticut 06520
| | | | - D Cebra
- University of California, Davis, California 95616
| | - J Ceska
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - I Chakaberia
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - B K Chan
- University of California, Los Angeles, California 90095
| | - Z Chang
- Indiana University, Bloomington, Indiana 47408
| | - A Chatterjee
- National Institute of Technology Durgapur, Durgapur - 713209, India
| | - D Chen
- University of California, Riverside, California 92521
| | - J Chen
- Shandong University, Qingdao, Shandong 266237
| | - J H Chen
- Fudan University, Shanghai, 200433
| | - Z Chen
- Shandong University, Qingdao, Shandong 266237
| | - J Cheng
- Tsinghua University, Beijing 100084
| | - Y Cheng
- University of California, Los Angeles, California 90095
| | | | - W Christie
- Brookhaven National Laboratory, Upton, New York 11973
| | - X Chu
- Brookhaven National Laboratory, Upton, New York 11973
| | - H J Crawford
- University of California, Berkeley, California 94720
| | - G Dale-Gau
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - A Das
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - M Daugherity
- Abilene Christian University, Abilene, Texas 79699
| | - T G Dedovich
- Joint Institute for Nuclear Research, Dubna 141 980
| | - I M Deppner
- University of Heidelberg, Heidelberg 69120, Germany
| | - A A Derevschikov
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - A Dhamija
- Panjab University, Chandigarh 160014, India
| | - L Di Carlo
- Wayne State University, Detroit, Michigan 48201
| | - L Didenko
- Brookhaven National Laboratory, Upton, New York 11973
| | - P Dixit
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - X Dong
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | | | - J C Dunlop
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Engelage
- University of California, Berkeley, California 94720
| | - G Eppley
- Rice University, Houston, Texas 77251
| | - S Esumi
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - O Evdokimov
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - A Ewigleben
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - O Eyser
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Fatemi
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - S Fazio
- University of Calabria & INFN-Cosenza, Rende 87036, Italy
| | - C J Feng
- National Cheng Kung University, Tainan 70101
| | - Y Feng
- Purdue University, West Lafayette, Indiana 47907
| | - E Finch
- Southern Connecticut State University, New Haven, Connecticut 06515
| | - Y Fisyak
- Brookhaven National Laboratory, Upton, New York 11973
| | - F A Flor
- Yale University, New Haven, Connecticut 06520
| | - C Fu
- Central China Normal University, Wuhan, Hubei 430079
| | - F Geurts
- Rice University, Houston, Texas 77251
| | - N Ghimire
- Temple University, Philadelphia, Pennsylvania 19122
| | - A Gibson
- Valparaiso University, Valparaiso, Indiana 46383
| | - K Gopal
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - X Gou
- Shandong University, Qingdao, Shandong 266237
| | - D Grosnick
- Valparaiso University, Valparaiso, Indiana 46383
| | - A Gupta
- University of Jammu, Jammu 180001, India
| | - A Hamed
- American University of Cairo, New Cairo 11835, New Cairo, Egypt
| | - Y Han
- Rice University, Houston, Texas 77251
| | - M D Harasty
- University of California, Davis, California 95616
| | - J W Harris
- Yale University, New Haven, Connecticut 06520
| | | | - W He
- Fudan University, Shanghai, 200433
| | - X H He
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y He
- Shandong University, Qingdao, Shandong 266237
| | - C Hu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Q Hu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Hu
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - H Huang
- National Cheng Kung University, Tainan 70101
| | - H Z Huang
- University of California, Los Angeles, California 90095
| | - S L Huang
- State University of New York, Stony Brook, New York 11794
| | - T Huang
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - X Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Central China Normal University, Wuhan, Hubei 430079
| | - T J Humanic
- The Ohio State University, Columbus, Ohio 43210
| | - D Isenhower
- Abilene Christian University, Abilene, Texas 79699
| | - M Isshiki
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - W W Jacobs
- Indiana University, Bloomington, Indiana 47408
| | - A Jalotra
- University of Jammu, Jammu 180001, India
| | - C Jena
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - Y Ji
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J Jia
- Brookhaven National Laboratory, Upton, New York 11973
- State University of New York, Stony Brook, New York 11794
| | - C Jin
- Rice University, Houston, Texas 77251
| | - X Ju
- University of Science and Technology of China, Hefei, Anhui 230026
| | - E G Judd
- University of California, Berkeley, California 94720
| | - S Kabana
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
| | - M L Kabir
- University of California, Riverside, California 92521
| | - D Kalinkin
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - K Kang
- Tsinghua University, Beijing 100084
| | - D Kapukchyan
- University of California, Riverside, California 92521
| | - K Kauder
- Brookhaven National Laboratory, Upton, New York 11973
| | - H W Ke
- Brookhaven National Laboratory, Upton, New York 11973
| | - D Keane
- Kent State University, Kent, Ohio 44242
| | - A Kechechyan
- Joint Institute for Nuclear Research, Dubna 141 980
| | - M Kelsey
- Wayne State University, Detroit, Michigan 48201
| | - B Kimelman
- University of California, Davis, California 95616
| | - A Kiselev
- Brookhaven National Laboratory, Upton, New York 11973
| | - A G Knospe
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - H S Ko
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - L Kochenda
- National Research Nuclear University MEPhI, Moscow 115409
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- National Research Nuclear University MEPhI, Moscow 115409
| | - L Kumar
- Panjab University, Chandigarh 160014, India
| | - S Kumar
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | | | - R Lacey
- State University of New York, Stony Brook, New York 11794
| | - J M Landgraf
- Brookhaven National Laboratory, Upton, New York 11973
| | - A Lebedev
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Lednicky
- Joint Institute for Nuclear Research, Dubna 141 980
| | - J H Lee
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y H Leung
- University of Heidelberg, Heidelberg 69120, Germany
| | - N Lewis
- Brookhaven National Laboratory, Upton, New York 11973
| | - C Li
- Shandong University, Qingdao, Shandong 266237
| | - W Li
- Rice University, Houston, Texas 77251
| | - X Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Li
- Tsinghua University, Beijing 100084
| | - Z Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - X Liang
- University of California, Riverside, California 92521
| | - Y Liang
- Kent State University, Kent, Ohio 44242
| | - T Lin
- Shandong University, Qingdao, Shandong 266237
| | - C Liu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - F Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - H Liu
- Indiana University, Bloomington, Indiana 47408
| | - H Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - L Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - T Liu
- Yale University, New Haven, Connecticut 06520
| | - X Liu
- The Ohio State University, Columbus, Ohio 43210
| | - Y Liu
- Texas A&M University, College Station, Texas 77843
| | - Z Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - T Ljubicic
- Brookhaven National Laboratory, Upton, New York 11973
| | - W J Llope
- Wayne State University, Detroit, Michigan 48201
| | - O Lomicky
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - R S Longacre
- Brookhaven National Laboratory, Upton, New York 11973
| | - E M Loyd
- University of California, Riverside, California 92521
| | - T Lu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - N S Lukow
- Temple University, Philadelphia, Pennsylvania 19122
| | - X F Luo
- Central China Normal University, Wuhan, Hubei 430079
| | - V B Luong
- Joint Institute for Nuclear Research, Dubna 141 980
| | - L Ma
- Fudan University, Shanghai, 200433
| | - R Ma
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y G Ma
- Fudan University, Shanghai, 200433
| | - N Magdy
- State University of New York, Stony Brook, New York 11794
| | - D Mallick
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
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- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J A Mazer
- Rutgers University, Piscataway, New Jersey 08854
| | - G McNamara
- Wayne State University, Detroit, Michigan 48201
| | - K Mi
- Central China Normal University, Wuhan, Hubei 430079
| | - N G Minaev
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - B Mohanty
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - M M Mondal
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - I Mooney
- Yale University, New Haven, Connecticut 06520
| | - D A Morozov
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
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- Joint Institute for Nuclear Research, Dubna 141 980
| | - M I Nagy
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - A S Nain
- Panjab University, Chandigarh 160014, India
| | - J D Nam
- Temple University, Philadelphia, Pennsylvania 19122
| | - Md Nasim
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - D Neff
- University of California, Los Angeles, California 90095
| | - J M Nelson
- University of California, Berkeley, California 94720
| | - D B Nemes
- Yale University, New Haven, Connecticut 06520
| | - M Nie
- Shandong University, Qingdao, Shandong 266237
| | - G Nigmatkulov
- National Research Nuclear University MEPhI, Moscow 115409
| | - T Niida
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - R Nishitani
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - L V Nogach
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - T Nonaka
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - G Odyniec
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - A Ogawa
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Oh
- Sejong University, Seoul, 05006, South Korea
| | - V A Okorokov
- National Research Nuclear University MEPhI, Moscow 115409
| | - K Okubo
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - B S Page
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Pak
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Pan
- Texas A&M University, College Station, Texas 77843
| | - A Pandav
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - A K Pandey
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | | | - T Pani
- Rutgers University, Piscataway, New Jersey 08854
| | - P Parfenov
- National Research Nuclear University MEPhI, Moscow 115409
| | - A Paul
- University of California, Riverside, California 92521
| | - C Perkins
- University of California, Berkeley, California 94720
| | - B R Pokhrel
- Temple University, Philadelphia, Pennsylvania 19122
| | - M Posik
- Temple University, Philadelphia, Pennsylvania 19122
| | - T Protzman
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - N K Pruthi
- Panjab University, Chandigarh 160014, India
| | - J Putschke
- Wayne State University, Detroit, Michigan 48201
| | - Z Qin
- Tsinghua University, Beijing 100084
| | - H Qiu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - A Quintero
- Temple University, Philadelphia, Pennsylvania 19122
| | - C Racz
- University of California, Riverside, California 92521
| | | | - N Raha
- Wayne State University, Detroit, Michigan 48201
| | - R L Ray
- University of Texas, Austin, Texas 78712
| | - H G Ritter
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
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- Rutgers University, Piscataway, New Jersey 08854
| | - L Ruan
- Brookhaven National Laboratory, Upton, New York 11973
| | - A K Sahoo
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - N R Sahoo
- Shandong University, Qingdao, Shandong 266237
| | - H Sako
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - S Salur
- Rutgers University, Piscataway, New Jersey 08854
| | - E Samigullin
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute," Moscow 117218
| | - S Sato
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - W B Schmidke
- Brookhaven National Laboratory, Upton, New York 11973
| | - N Schmitz
- Max-Planck-Institut für Physik, Munich 80805, Germany
| | - J Seger
- Creighton University, Omaha, Nebraska 68178
| | - R Seto
- University of California, Riverside, California 92521
| | - P Seyboth
- Max-Planck-Institut für Physik, Munich 80805, Germany
| | - N Shah
- Indian Institute Technology, Patna, Bihar 801106, India
| | - E Shahaliev
- Joint Institute for Nuclear Research, Dubna 141 980
| | | | - T Shao
- Fudan University, Shanghai, 200433
| | - M Sharma
- University of Jammu, Jammu 180001, India
| | - N Sharma
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - R Sharma
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - S R Sharma
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | | | - D Y Shen
- Fudan University, Shanghai, 200433
| | - K Shen
- University of Science and Technology of China, Hefei, Anhui 230026
| | - S S Shi
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Shi
- Shandong University, Qingdao, Shandong 266237
| | - Q Y Shou
- Fudan University, Shanghai, 200433
| | - F Si
- University of Science and Technology of China, Hefei, Anhui 230026
| | - J Singh
- Panjab University, Chandigarh 160014, India
| | - S Singha
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - P Sinha
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - M J Skoby
- Ball State University, Muncie, Indiana, 47306
- Purdue University, West Lafayette, Indiana 47907
| | - Y Söhngen
- University of Heidelberg, Heidelberg 69120, Germany
| | - Y Song
- Yale University, New Haven, Connecticut 06520
| | - B Srivastava
- Purdue University, West Lafayette, Indiana 47907
| | | | - D J Stewart
- Wayne State University, Detroit, Michigan 48201
| | - M Strikhanov
- National Research Nuclear University MEPhI, Moscow 115409
| | | | - Y Su
- University of Science and Technology of China, Hefei, Anhui 230026
| | - C Sun
- State University of New York, Stony Brook, New York 11794
| | - X Sun
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Sun
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Sun
- Huzhou University, Huzhou, Zhejiang 313000
| | - B Surrow
- Temple University, Philadelphia, Pennsylvania 19122
| | - D N Svirida
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute," Moscow 117218
| | - Z W Sweger
- University of California, Davis, California 95616
| | - A Tamis
- Yale University, New Haven, Connecticut 06520
| | - A H Tang
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Tang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - A Taranenko
- National Research Nuclear University MEPhI, Moscow 115409
| | - T Tarnowsky
- Michigan State University, East Lansing, Michigan 48824
| | - J H Thomas
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - D Tlusty
- Creighton University, Omaha, Nebraska 68178
| | - T Todoroki
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - M V Tokarev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - C A Tomkiel
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - S Trentalange
- University of California, Los Angeles, California 90095
| | - R E Tribble
- Texas A&M University, College Station, Texas 77843
| | - P Tribedy
- Brookhaven National Laboratory, Upton, New York 11973
| | - O D Tsai
- Brookhaven National Laboratory, Upton, New York 11973
- University of California, Los Angeles, California 90095
| | - C Y Tsang
- Brookhaven National Laboratory, Upton, New York 11973
- Kent State University, Kent, Ohio 44242
| | - Z Tu
- Brookhaven National Laboratory, Upton, New York 11973
| | - T Ullrich
- Brookhaven National Laboratory, Upton, New York 11973
| | - D G Underwood
- Argonne National Laboratory, Argonne, Illinois 60439
- Valparaiso University, Valparaiso, Indiana 46383
| | - I Upsal
- Rice University, Houston, Texas 77251
| | - G Van Buren
- Brookhaven National Laboratory, Upton, New York 11973
| | - A N Vasiliev
- National Research Nuclear University MEPhI, Moscow 115409
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - V Verkest
- Wayne State University, Detroit, Michigan 48201
| | - F Videbæk
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Vokal
- Joint Institute for Nuclear Research, Dubna 141 980
| | | | - F Wang
- Purdue University, West Lafayette, Indiana 47907
| | - G Wang
- University of California, Los Angeles, California 90095
| | - J S Wang
- Huzhou University, Huzhou, Zhejiang 313000
| | - X Wang
- Shandong University, Qingdao, Shandong 266237
| | - Y Wang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Wang
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Wang
- Tsinghua University, Beijing 100084
| | - Z Wang
- Shandong University, Qingdao, Shandong 266237
| | - J C Webb
- Brookhaven National Laboratory, Upton, New York 11973
| | | | - G D Westfall
- Michigan State University, East Lansing, Michigan 48824
| | - H Wieman
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - G Wilks
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - S W Wissink
- Indiana University, Bloomington, Indiana 47408
| | - J Wu
- Central China Normal University, Wuhan, Hubei 430079
| | - J Wu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - X Wu
- University of California, Los Angeles, California 90095
| | - Y Wu
- University of California, Riverside, California 92521
| | - B Xi
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800
| | - Z G Xiao
- Tsinghua University, Beijing 100084
| | - G Xie
- University of Chinese Academy of Sciences, Beijing, 101408
| | - W Xie
- Purdue University, West Lafayette, Indiana 47907
| | - H Xu
- Huzhou University, Huzhou, Zhejiang 313000
| | - N Xu
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - Q H Xu
- Shandong University, Qingdao, Shandong 266237
| | - Y Xu
- Shandong University, Qingdao, Shandong 266237
| | - Y Xu
- Central China Normal University, Wuhan, Hubei 430079
| | - Z Xu
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Xu
- University of California, Los Angeles, California 90095
| | - G Yan
- Shandong University, Qingdao, Shandong 266237
| | - Z Yan
- State University of New York, Stony Brook, New York 11794
| | - C Yang
- Shandong University, Qingdao, Shandong 266237
| | - Q Yang
- Shandong University, Qingdao, Shandong 266237
| | - S Yang
- South China Normal University, Guangzhou, Guangdong 510631
| | - Y Yang
- National Cheng Kung University, Tainan 70101
| | - Z Ye
- Rice University, Houston, Texas 77251
| | - Z Ye
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - L Yi
- Shandong University, Qingdao, Shandong 266237
| | - K Yip
- Brookhaven National Laboratory, Upton, New York 11973
| | - N Yu
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Yu
- Shandong University, Qingdao, Shandong 266237
| | - W Zha
- University of Science and Technology of China, Hefei, Anhui 230026
| | - C Zhang
- State University of New York, Stony Brook, New York 11794
| | - D Zhang
- Central China Normal University, Wuhan, Hubei 430079
| | - J Zhang
- Shandong University, Qingdao, Shandong 266237
| | - S Zhang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - X Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Zhang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Zhang
- Central China Normal University, Wuhan, Hubei 430079
| | - Z J Zhang
- National Cheng Kung University, Tainan 70101
| | - Z Zhang
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Zhang
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - F Zhao
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - J Zhao
- Fudan University, Shanghai, 200433
| | - M Zhao
- Brookhaven National Laboratory, Upton, New York 11973
| | - C Zhou
- Fudan University, Shanghai, 200433
| | - J Zhou
- University of Science and Technology of China, Hefei, Anhui 230026
| | - S Zhou
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Zhou
- Central China Normal University, Wuhan, Hubei 430079
| | - X Zhu
- Tsinghua University, Beijing 100084
| | - M Zurek
- Argonne National Laboratory, Argonne, Illinois 60439
- Brookhaven National Laboratory, Upton, New York 11973
| | - M Zyzak
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
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Zhang JH, Wang XY, Wang JS, Zhang C, Liu Z, Li JR. [Study on the time-point distribution characteristics of the occurrence of laryngopharyngeal reflux]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2023; 58:345-350. [PMID: 37026155 DOI: 10.3760/cma.j.cn115330-20220525-00301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
Abstract
Objective: To investigate the characteristics of the time-point distribution of the occurrence of laryngopharyngeal reflux (LPR) by 24-hour multichannel intraluminal impedance-pH monitoring (24 h MII-pH) and to provide guidance for the development of individualized anti-reflux strategies for LPR patients. Methods: We conducted a retrospective analysis of 24 h MII-pH data from 408 patients [339 males and 69 females, aged 23-84 (55.08±11.08) years] attending the Department of Otorhinolaryngology Head and Neck Surgery at the Sixth Medical Center of the PLA General Hospital from January 2013 to March 2020. The number of gas acid/weak-acid reflux, mixed gas-liquid acid/weak-acid reflux, liquid acid/weak-acid reflux and alkaline reflux events at different time points were recorded and statistically analyzed through SPSS 26.0 software. Results: A total of 408 patients were included. Based on the 24 h MII-pH, the total positive rate of LPR was 77.45% (316/408). The type of positive gaseous weak-acid reflux was significantly higher than the remaining types of LPR (χ2=297.12,P<0.001). Except the gaseous weak-acid reflux, the occurrence of the remaining types of LPR showed a tendency to increase after meals, especially after dinner. Liquid acid reflux events occurred mainly between after dinner and the following morning, and 47.11% (57/121) of them occurred within 3 h after dinner. There was a significant positive association between Reflux Symptom Index scores and gaseous weak-acid reflux(r=0.127,P<0.01), liquid acid reflux(r=0.205,P<0.01) and liquid weak-acid reflux(r=0.103,P<0.05)events. Conclusions: With the exception of gaseous weak-acid reflux events, the occurrence of the remaining types of LPR events has a tendency to increase after meals, especially after dinner. Gaseous weak-acid reflux events accounts for the largest proportion of all types of LPR events, but the pathogenic mechanisms of gaseous weak-acid reflux are needed to further investigate.
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Affiliation(s)
- J H Zhang
- Department of Otorhinolaryngology Head and Neck Surgery, the Sixth Medical Center of PLA General Hospital, and National Clinical Research Center for Otolaryngologic Diseases, Beijing 100048, China Department of Otorhinolaryngology Head and Neck Surgery, School of Medicine, South China University of Technology, Guangzhou 510006, China
| | - X Y Wang
- Department of Otorhinolaryngology Head and Neck Surgery, the Sixth Medical Center of PLA General Hospital, and National Clinical Research Center for Otolaryngologic Diseases, Beijing 100048, China
| | - J S Wang
- Department of Otorhinolaryngology Head and Neck Surgery, the Sixth Medical Center of PLA General Hospital, and National Clinical Research Center for Otolaryngologic Diseases, Beijing 100048, China
| | - C Zhang
- Department of Otorhinolaryngology Head and Neck Surgery, the Sixth Medical Center of PLA General Hospital, and National Clinical Research Center for Otolaryngologic Diseases, Beijing 100048, China
| | - Z Liu
- Department of Otorhinolaryngology Head and Neck Surgery, the Sixth Medical Center of PLA General Hospital, and National Clinical Research Center for Otolaryngologic Diseases, Beijing 100048, China
| | - J R Li
- Department of Otorhinolaryngology Head and Neck Surgery, the Sixth Medical Center of PLA General Hospital, and National Clinical Research Center for Otolaryngologic Diseases, Beijing 100048, China Department of Otorhinolaryngology Head and Neck Surgery, School of Medicine, South China University of Technology, Guangzhou 510006, China
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91
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Liu YH, Wang JJ, Wang HZ, Liu S, Wu YC, Hu SG, Yu Q, Liu Z, Chen TP, Yin Y, Liu Y. Braille recognition by E-skin system based on binary memristive neural network. Sci Rep 2023; 13:5437. [PMID: 37012399 PMCID: PMC10070348 DOI: 10.1038/s41598-023-31934-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 03/20/2023] [Indexed: 04/05/2023] Open
Abstract
Braille system is widely used worldwide for communication by visually impaired people. However, there are still some visually impaired people who are unable to learn Braille system due to various factors, such as the age (too young or too old), brain damage, etc. A wearable and low-cost Braille recognition system may substantially help these people recognize Braille or assist them in Braille learning. In this work, we fabricated polydimethylsiloxane (PDMS)-based flexible pressure sensors to construct an electronic skin (E-skin) for the application of Braille recognition. The E-skin mimics human touch sensing function for collecting Braille information. Braille recognition is realized with a neural network based on memristors. We utilize a binary neural network algorithm with only two bias layers and three fully connected layers. Such neural network design remarkably reduces the calculation burden and, thus, the system cost. Experiments show that the system can achieve a recognition accuracy of up to 91.25%. This work demonstrates the possibility of realizing a wearable and low-cost Braille recognition system and a Braille learning-assistance system.
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Affiliation(s)
- Y H Liu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - J J Wang
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
| | - H Z Wang
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - S Liu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Y C Wu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - S G Hu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Q Yu
- State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Z Liu
- School of Integrated Circuits, Guangdong University of Technology, Guangzhou, 510006, China
| | - T P Chen
- School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Y Yin
- Graduate School of Engineering, Gunma University, 1-5-1Tenjin, Kiryu, Gunma, 376-8515, Japan
| | - Y Liu
- Deepcreatic Technologies Ltd, Chengdu, 610000, Sichuan, People's Republic of China
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Liu Z, Pan X, Guo J, Li L, Tang Y, Wu G, Li M, Wang H. Long-term sevoflurane exposure resulted in temporary rather than lasting cognitive impairment in Drosophila. Behav Brain Res 2023; 442:114327. [PMID: 36738841 DOI: 10.1016/j.bbr.2023.114327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 01/19/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023]
Abstract
Sevoflurane is the primary inhaled anesthetic used in pediatric surgery. It has been the focus of research since animal models studies found that it was neurotoxic to the developing brain two decades ago. However, whether pediatric general anesthesia can lead to permanent cognitive deficits remained a subject of heated debate. Therefore, our study aims to determine the lifetime neurotoxicity of early long-time sevoflurane exposure using a short-life-cycle animal model, Drosophila melanogaster. To investigate this question, we measured the lifetime changes of two-day-old flies' learning and memory abilities after anesthesia with 3 % sevoflurane for 6 h by the T-maze memory assay. We evaluated the apoptosis, levels of ATP and ROS, and related genes in the fly head. Our results suggest that 6 h 3 % sevoflurane exposure at a young age can only induce transient neuroapoptosis and cognitive deficits around the first week after anesthesia. But this brain damage recedes with time and vanishes in late life. We also found that the mRNA level of caspases and Bcl-2, ROS level, and ATP level increased during this temporary neuroapoptosis process. And mRNA levels of antioxidants, such as SOD2 and CAT, increased and decreased simultaneously with the rise and fall of the ROS level, indicating a possible contribution to the recovery from the sevoflurane impairment. In conclusion, our results suggest that one early prolonged sevoflurane-based general anesthesia can induce neuroapoptosis and learning and memory deficit transiently but not permanently in Drosophila.
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Affiliation(s)
- Ziming Liu
- Department of Anesthesiology, Affiliated Hospital of Hebei University, Baoding 071000, Hebei, China
| | - Xuanyi Pan
- Department of Anesthesiology, Affiliated Hospital of Hebei University, Baoding 071000, Hebei, China
| | - Jiguang Guo
- School of Basic Medical Sciences, Hebei University, Baoding 071000, Hebei, China
| | - Liping Li
- Institute of Materia Medical, Hebei Centers for Disease Control and Prevention, Shijiazhuang 050021, Hebei, China
| | - Yuxin Tang
- School of Basic Medical Sciences, Hebei University, Baoding 071000, Hebei, China
| | - Guangyi Wu
- Department of Anesthesiology, Affiliated Hospital of Hebei University, Baoding 071000, Hebei, China
| | - Ming Li
- School of Basic Medical Sciences, Hebei University, Baoding 071000, Hebei, China.
| | - Hongjie Wang
- Department of Anesthesiology, Affiliated Hospital of Hebei University, Baoding 071000, Hebei, China; Hebei Provincial Key Laboratory of Skeletal Metabolic Physiology of Chronic Kidney Disease, Affiliated Hospital of Hebei University, Baoding 071000, Hebei, China.
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93
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Aboona BE, Adam J, Adamczyk L, Adams JR, Aggarwal I, Aggarwal MM, Ahammed Z, Anderson DM, Aschenauer EC, Atchison J, Bairathi V, Baker W, Ball Cap JG, Barish K, Bellwied R, Bhagat P, Bhasin A, Bhatta S, Bielcik J, Bielcikova J, Brandenburg JD, Cai XZ, Caines H, Calderón de la Barca Sánchez M, Cebra D, Ceska J, Chakaberia I, Chaloupka P, Chan BK, Chang Z, Chen D, Chen J, Chen JH, Chen Z, Cheng J, Cheng Y, Choudhury S, Christie W, Chu X, Crawford HJ, Csanád M, Dale-Gau G, Das A, Daugherity M, Deppner IM, Dhamija A, Di Carlo L, Didenko L, Dixit P, Dong X, Drachenberg JL, Duckworth E, Dunlop JC, Engelage J, Eppley G, Esumi S, Evdokimov O, Ewigleben A, Eyser O, Fatemi R, Fazio S, Feng CJ, Feng Y, Finch E, Fisyak Y, Flor FA, Fu C, Gagliardi CA, Galatyuk T, Geurts F, Ghimire N, Gibson A, Gopal K, Gou X, Grosnick D, Gupta A, Guryn W, Hamed A, Han Y, Harabasz S, Harasty MD, Harris JW, Harrison H, He W, He XH, He Y, Heppelmann S, Herrmann N, Holub L, Hu C, Hu Q, Hu Y, Huang H, Huang HZ, Huang SL, Huang T, Huang X, Huang Y, Huang Y, Humanic TJ, Isenhower D, Isshiki M, Jacobs WW, Jalotra A, Jena C, Jentsch A, Ji Y, Jia J, Jin C, Ju X, Judd EG, Kabana S, Kabir ML, Kagamaster S, Kalinkin D, Kang K, Kapukchyan D, Kauder K, Ke HW, Keane D, Kelsey M, Khyzhniak YV, Kikoła DP, Kimelman B, Kincses D, Kisel I, Kiselev A, Knospe AG, Ko HS, Kosarzewski LK, Kramarik L, Kumar L, Kumar S, Kunnawalkam Elayavalli R, Lacey R, Landgraf JM, Lauret J, Lebedev A, Lee JH, Leung YH, Lewis N, Li C, Li C, Li W, Li X, Li Y, Li Y, Li Z, Liang X, Liang Y, Licenik R, Lin T, Lisa MA, Liu C, Liu F, Liu H, Liu H, Liu L, Liu T, Liu X, Liu Y, Liu Z, Ljubicic T, Llope WJ, Lomicky O, Longacre RS, Loyd E, Lu T, Lukow NS, Luo XF, Ma L, Ma R, Ma YG, Magdy N, Mallick D, Margetis S, Markert C, Matis HS, Mazer JA, McNamara G, Mi K, Mioduszewski S, Mohanty B, Mooney I, Mukherjee A, Nagy MI, Nain AS, Nam JD, Nasim M, Neff D, Nelson JM, Nemes DB, Nie M, Niida T, Nishitani R, Nonaka T, Nunes AS, Odyniec G, Ogawa A, Oh S, Okubo K, Page BS, Pak R, Pan J, Pandav A, Pandey AK, Pani T, Paul A, Pawlik B, Pawlowska D, Perkins C, Pluta J, Pokhrel BR, Posik M, Protzman T, Prozorova V, Pruthi NK, Przybycien M, Putschke J, Qin Z, Qiu H, Quintero A, Racz C, Radhakrishnan SK, Raha N, Ray RL, Reed R, Ritter HG, Robertson CW, Robotkova M, Romero JL, Rosales Aguilar MA, Roy D, Roy Chowdhury P, Ruan L, Sahoo AK, Sahoo NR, Sako H, Salur S, Sato S, Schmidke WB, Schmitz N, Seck FJ, Seger J, Seto R, Seyboth P, Shah N, Shanmuganathan PV, Shao M, Shao T, Sharma M, Sharma N, Sharma R, Sharma SR, Sheikh AI, Shen DY, Shen K, Shi SS, Shi Y, Shou QY, Si F, Singh J, Singha S, Sinha P, Skoby MJ, Smirnov N, Söhngen Y, Song Y, Srivastava B, Stanislaus TDS, Stefaniak M, Stewart DJ, Stringfellow B, Su Y, Suaide AAP, Sumbera M, Sun C, Sun X, Sun Y, Sun Y, Surrow B, Sweger ZW, Szymanski P, Tamis A, Tang AH, Tang Z, Tarnowsky T, Thomas JH, Timmins AR, Tlusty D, Todoroki T, Tomkiel CA, Trentalange S, Tribble RE, Tribedy P, Truhlar T, Trzeciak BA, Tsai OD, Tsang CY, Tu Z, Ullrich T, Underwood DG, Upsal I, Van Buren G, Vanek J, Vassiliev I, Verkest V, Videbæk F, Voloshin SA, Wang F, Wang G, Wang JS, Wang X, Wang Y, Wang Y, Wang Y, Wang Z, Webb JC, Weidenkaff PC, Westfall GD, Wielanek D, Wieman H, Wilks G, Wissink SW, Witt R, Wu J, Wu J, Wu X, Wu Y, Xi B, Xiao ZG, Xie W, Xu H, Xu N, Xu QH, Xu Y, Xu Y, Xu Z, Xu Z, Yan G, Yan Z, Yang C, Yang Q, Yang S, Yang Y, Ye Z, Ye Z, Yi L, Yip K, Yu Y, Zbroszczyk H, Zha W, Zhang C, Zhang D, Zhang J, Zhang S, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhang ZJ, Zhang Z, Zhang Z, Zhao F, Zhao J, Zhao M, Zhou C, Zhou J, Zhou S, Zhou Y, Zhu X, Zurek M, Zyzak M. Measurement of Sequential ϒ Suppression in Au+Au Collisions at sqrt[s_{NN}]=200 GeV with the STAR Experiment. Phys Rev Lett 2023; 130:112301. [PMID: 37001106 DOI: 10.1103/physrevlett.130.112301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 11/30/2022] [Accepted: 01/26/2023] [Indexed: 06/19/2023]
Abstract
We report on measurements of sequential ϒ suppression in Au+Au collisions at sqrt[s_{NN}]=200 GeV with the STAR detector at the Relativistic Heavy Ion Collider (RHIC) through both the dielectron and dimuon decay channels. In the 0%-60% centrality class, the nuclear modification factors (R_{AA}), which quantify the level of yield suppression in heavy-ion collisions compared to p+p collisions, for ϒ(1S) and ϒ(2S) are 0.40±0.03(stat)±0.03(sys)±0.09(norm) and 0.26±0.08(stat)±0.02(sys)±0.06(norm), respectively, while the upper limit of the ϒ(3S) R_{AA} is 0.17 at a 95% confidence level. This provides experimental evidence that the ϒ(3S) is significantly more suppressed than the ϒ(1S) at RHIC. The level of suppression for ϒ(1S) is comparable to that observed at the much higher collision energy at the Large Hadron Collider. These results point to the creation of a medium at RHIC whose temperature is sufficiently high to strongly suppress excited ϒ states.
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Affiliation(s)
- B E Aboona
- Texas A&M University, College Station, Texas 77843
| | - J Adam
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - L Adamczyk
- AGH University of Science and Technology, FPACS, Cracow 30-059, Poland
| | - J R Adams
- The Ohio State University, Columbus, Ohio 43210
| | - I Aggarwal
- Panjab University, Chandigarh 160014, India
| | | | - Z Ahammed
- Variable Energy Cyclotron Centre, Kolkata 700064, India
| | - D M Anderson
- Texas A&M University, College Station, Texas 77843
| | | | - J Atchison
- Abilene Christian University, Abilene, Texas 79699
| | - V Bairathi
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
| | - W Baker
- University of California, Riverside, California 92521
| | | | - K Barish
- University of California, Riverside, California 92521
| | - R Bellwied
- University of Houston, Houston, Texas 77204
| | - P Bhagat
- University of Jammu, Jammu 180001, India
| | - A Bhasin
- University of Jammu, Jammu 180001, India
| | - S Bhatta
- State University of New York, Stony Brook, New York 11794
| | - J Bielcik
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - J Bielcikova
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | | | - X Z Cai
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800
| | - H Caines
- Yale University, New Haven, Connecticut 06520
| | | | - D Cebra
- University of California, Davis, California 95616
| | - J Ceska
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - I Chakaberia
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - P Chaloupka
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - B K Chan
- University of California, Los Angeles, California 90095
| | - Z Chang
- Indiana University, Bloomington, Indiana 47408
| | - D Chen
- University of California, Riverside, California 92521
| | - J Chen
- Shandong University, Qingdao, Shandong 266237
| | - J H Chen
- Fudan University, Shanghai 200433
| | - Z Chen
- Shandong University, Qingdao, Shandong 266237
| | - J Cheng
- Tsinghua University, Beijing 100084
| | - Y Cheng
- University of California, Los Angeles, California 90095
| | | | - W Christie
- Brookhaven National Laboratory, Upton, New York 11973
| | - X Chu
- Brookhaven National Laboratory, Upton, New York 11973
| | - H J Crawford
- University of California, Berkeley, California 94720
| | - M Csanád
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - G Dale-Gau
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - A Das
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - M Daugherity
- Abilene Christian University, Abilene, Texas 79699
| | - I M Deppner
- University of Heidelberg, Heidelberg 69120, Germany
| | - A Dhamija
- Panjab University, Chandigarh 160014, India
| | - L Di Carlo
- Wayne State University, Detroit, Michigan 48201
| | - L Didenko
- Brookhaven National Laboratory, Upton, New York 11973
| | - P Dixit
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - X Dong
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | | | - J C Dunlop
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Engelage
- University of California, Berkeley, California 94720
| | - G Eppley
- Rice University, Houston, Texas 77251
| | - S Esumi
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - O Evdokimov
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - A Ewigleben
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - O Eyser
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Fatemi
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - S Fazio
- University of Calabria & INFN-Cosenza, Rende 87036, Italy
| | - C J Feng
- National Cheng Kung University, Tainan 70101
| | - Y Feng
- Purdue University, West Lafayette, Indiana 47907
| | - E Finch
- Southern Connecticut State University, New Haven, Connecticut 06515
| | - Y Fisyak
- Brookhaven National Laboratory, Upton, New York 11973
| | - F A Flor
- Yale University, New Haven, Connecticut 06520
| | - C Fu
- Central China Normal University, Wuhan, Hubei 430079
| | | | - T Galatyuk
- Technische Universität Darmstadt, Darmstadt 64289, Germany
| | - F Geurts
- Rice University, Houston, Texas 77251
| | - N Ghimire
- Temple University, Philadelphia, Pennsylvania 19122
| | - A Gibson
- Valparaiso University, Valparaiso, Indiana 46383
| | - K Gopal
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - X Gou
- Shandong University, Qingdao, Shandong 266237
| | - D Grosnick
- Valparaiso University, Valparaiso, Indiana 46383
| | - A Gupta
- University of Jammu, Jammu 180001, India
| | - W Guryn
- Brookhaven National Laboratory, Upton, New York 11973
| | - A Hamed
- American University of Cairo, New Cairo 11835, New Cairo, Egypt
| | - Y Han
- Rice University, Houston, Texas 77251
| | - S Harabasz
- Technische Universität Darmstadt, Darmstadt 64289, Germany
| | - M D Harasty
- University of California, Davis, California 95616
| | - J W Harris
- Yale University, New Haven, Connecticut 06520
| | - H Harrison
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - W He
- Fudan University, Shanghai 200433
| | - X H He
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y He
- Shandong University, Qingdao, Shandong 266237
| | - S Heppelmann
- University of California, Davis, California 95616
| | - N Herrmann
- University of Heidelberg, Heidelberg 69120, Germany
| | - L Holub
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - C Hu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Q Hu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Hu
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - H Huang
- National Cheng Kung University, Tainan 70101
| | - H Z Huang
- University of California, Los Angeles, California 90095
| | - S L Huang
- State University of New York, Stony Brook, New York 11794
| | - T Huang
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - X Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Central China Normal University, Wuhan, Hubei 430079
| | - T J Humanic
- The Ohio State University, Columbus, Ohio 43210
| | - D Isenhower
- Abilene Christian University, Abilene, Texas 79699
| | - M Isshiki
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - W W Jacobs
- Indiana University, Bloomington, Indiana 47408
| | - A Jalotra
- University of Jammu, Jammu 180001, India
| | - C Jena
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - A Jentsch
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y Ji
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J Jia
- Brookhaven National Laboratory, Upton, New York 11973
- State University of New York, Stony Brook, New York 11794
| | - C Jin
- Rice University, Houston, Texas 77251
| | - X Ju
- University of Science and Technology of China, Hefei, Anhui 230026
| | - E G Judd
- University of California, Berkeley, California 94720
| | - S Kabana
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
| | - M L Kabir
- University of California, Riverside, California 92521
| | - S Kagamaster
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - D Kalinkin
- Brookhaven National Laboratory, Upton, New York 11973
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - K Kang
- Tsinghua University, Beijing 100084
| | - D Kapukchyan
- University of California, Riverside, California 92521
| | - K Kauder
- Brookhaven National Laboratory, Upton, New York 11973
| | - H W Ke
- Brookhaven National Laboratory, Upton, New York 11973
| | - D Keane
- Kent State University, Kent, Ohio 44242
| | - M Kelsey
- Wayne State University, Detroit, Michigan 48201
| | | | - D P Kikoła
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - B Kimelman
- University of California, Davis, California 95616
| | - D Kincses
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - I Kisel
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
| | - A Kiselev
- Brookhaven National Laboratory, Upton, New York 11973
| | - A G Knospe
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - H S Ko
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - L K Kosarzewski
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - L Kramarik
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - L Kumar
- Panjab University, Chandigarh 160014, India
| | - S Kumar
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | | | - R Lacey
- State University of New York, Stony Brook, New York 11794
| | - J M Landgraf
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Lauret
- Brookhaven National Laboratory, Upton, New York 11973
| | - A Lebedev
- Brookhaven National Laboratory, Upton, New York 11973
| | - J H Lee
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y H Leung
- University of Heidelberg, Heidelberg 69120, Germany
| | - N Lewis
- Brookhaven National Laboratory, Upton, New York 11973
| | - C Li
- Shandong University, Qingdao, Shandong 266237
| | - C Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - W Li
- Rice University, Houston, Texas 77251
| | - X Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Li
- Tsinghua University, Beijing 100084
| | - Z Li
- University of Science and Technology of China, Hefei, Anhui 230026
| | - X Liang
- University of California, Riverside, California 92521
| | - Y Liang
- Kent State University, Kent, Ohio 44242
| | - R Licenik
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - T Lin
- Shandong University, Qingdao, Shandong 266237
| | - M A Lisa
- The Ohio State University, Columbus, Ohio 43210
| | - C Liu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - F Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - H Liu
- Indiana University, Bloomington, Indiana 47408
| | - H Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - L Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - T Liu
- Yale University, New Haven, Connecticut 06520
| | - X Liu
- The Ohio State University, Columbus, Ohio 43210
| | - Y Liu
- Texas A&M University, College Station, Texas 77843
| | - Z Liu
- Central China Normal University, Wuhan, Hubei 430079
| | - T Ljubicic
- Brookhaven National Laboratory, Upton, New York 11973
| | - W J Llope
- Wayne State University, Detroit, Michigan 48201
| | - O Lomicky
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - R S Longacre
- Brookhaven National Laboratory, Upton, New York 11973
| | - E Loyd
- University of California, Riverside, California 92521
| | - T Lu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - N S Lukow
- Temple University, Philadelphia, Pennsylvania 19122
| | - X F Luo
- Central China Normal University, Wuhan, Hubei 430079
| | - L Ma
- Fudan University, Shanghai 200433
| | - R Ma
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y G Ma
- Fudan University, Shanghai 200433
| | - N Magdy
- State University of New York, Stony Brook, New York 11794
| | - D Mallick
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | | | - C Markert
- University of Texas, Austin, Texas 78712
| | - H S Matis
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J A Mazer
- Rutgers University, Piscataway, New Jersey 08854
| | - G McNamara
- Wayne State University, Detroit, Michigan 48201
| | - K Mi
- Central China Normal University, Wuhan, Hubei 430079
| | | | - B Mohanty
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - I Mooney
- Yale University, New Haven, Connecticut 06520
| | - A Mukherjee
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - M I Nagy
- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
| | - A S Nain
- Panjab University, Chandigarh 160014, India
| | - J D Nam
- Temple University, Philadelphia, Pennsylvania 19122
| | - Md Nasim
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - D Neff
- University of California, Los Angeles, California 90095
| | - J M Nelson
- University of California, Berkeley, California 94720
| | - D B Nemes
- Yale University, New Haven, Connecticut 06520
| | - M Nie
- Shandong University, Qingdao, Shandong 266237
| | - T Niida
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - R Nishitani
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - T Nonaka
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - A S Nunes
- Brookhaven National Laboratory, Upton, New York 11973
| | - G Odyniec
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - A Ogawa
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Oh
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - K Okubo
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - B S Page
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Pak
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Pan
- Texas A&M University, College Station, Texas 77843
| | - A Pandav
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - A K Pandey
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - T Pani
- Rutgers University, Piscataway, New Jersey 08854
| | - A Paul
- University of California, Riverside, California 92521
| | - B Pawlik
- Institute of Nuclear Physics PAN, Cracow 31-342, Poland
| | - D Pawlowska
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - C Perkins
- University of California, Berkeley, California 94720
| | - J Pluta
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - B R Pokhrel
- Temple University, Philadelphia, Pennsylvania 19122
| | - M Posik
- Temple University, Philadelphia, Pennsylvania 19122
| | - T Protzman
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - V Prozorova
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - N K Pruthi
- Panjab University, Chandigarh 160014, India
| | - M Przybycien
- AGH University of Science and Technology, FPACS, Cracow 30-059, Poland
| | - J Putschke
- Wayne State University, Detroit, Michigan 48201
| | - Z Qin
- Tsinghua University, Beijing 100084
| | - H Qiu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - A Quintero
- Temple University, Philadelphia, Pennsylvania 19122
| | - C Racz
- University of California, Riverside, California 92521
| | | | - N Raha
- Wayne State University, Detroit, Michigan 48201
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- University of Texas, Austin, Texas 78712
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- Lehigh University, Bethlehem, Pennsylvania 18015
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- Lawrence Berkeley National Laboratory, Berkeley, California 94720
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- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - J L Romero
- University of California, Davis, California 95616
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- Rutgers University, Piscataway, New Jersey 08854
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- Brookhaven National Laboratory, Upton, New York 11973
| | - A K Sahoo
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - N R Sahoo
- Shandong University, Qingdao, Shandong 266237
| | - H Sako
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
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- Rutgers University, Piscataway, New Jersey 08854
| | - S Sato
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - W B Schmidke
- Brookhaven National Laboratory, Upton, New York 11973
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- Max-Planck-Institut für Physik, Munich 80805, Germany
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- Technische Universität Darmstadt, Darmstadt 64289, Germany
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- Creighton University, Omaha, Nebraska 68178
| | - R Seto
- University of California, Riverside, California 92521
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- Max-Planck-Institut für Physik, Munich 80805, Germany
| | - N Shah
- Indian Institute Technology, Patna, Bihar 801106, India
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- University of Science and Technology of China, Hefei, Anhui 230026
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- Fudan University, Shanghai 200433
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- University of Jammu, Jammu 180001, India
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- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
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- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
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- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
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- Fudan University, Shanghai 200433
| | - K Shen
- University of Science and Technology of China, Hefei, Anhui 230026
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- Central China Normal University, Wuhan, Hubei 430079
| | - Y Shi
- Shandong University, Qingdao, Shandong 266237
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- Fudan University, Shanghai 200433
| | - F Si
- University of Science and Technology of China, Hefei, Anhui 230026
| | - J Singh
- Panjab University, Chandigarh 160014, India
| | - S Singha
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - P Sinha
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - M J Skoby
- Ball State University, Muncie, Indiana 47306
- Purdue University, West Lafayette, Indiana 47907
| | - N Smirnov
- Yale University, New Haven, Connecticut 06520
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- University of Heidelberg, Heidelberg 69120, Germany
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- Yale University, New Haven, Connecticut 06520
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- Purdue University, West Lafayette, Indiana 47907
| | | | - M Stefaniak
- The Ohio State University, Columbus, Ohio 43210
| | - D J Stewart
- Wayne State University, Detroit, Michigan 48201
| | | | - Y Su
- University of Science and Technology of China, Hefei, Anhui 230026
| | - A A P Suaide
- Universidade de São Paulo, São Paulo, Brazil 05314-970
| | - M Sumbera
- Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
| | - C Sun
- State University of New York, Stony Brook, New York 11794
| | - X Sun
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Sun
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Sun
- Huzhou University, Huzhou, Zhejiang 313000
| | - B Surrow
- Temple University, Philadelphia, Pennsylvania 19122
| | - Z W Sweger
- University of California, Davis, California 95616
| | - P Szymanski
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - A Tamis
- Yale University, New Haven, Connecticut 06520
| | - A H Tang
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Tang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - T Tarnowsky
- Michigan State University, East Lansing, Michigan 48824
| | - J H Thomas
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | - D Tlusty
- Creighton University, Omaha, Nebraska 68178
| | - T Todoroki
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - C A Tomkiel
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - S Trentalange
- University of California, Los Angeles, California 90095
| | - R E Tribble
- Texas A&M University, College Station, Texas 77843
| | - P Tribedy
- Brookhaven National Laboratory, Upton, New York 11973
| | - T Truhlar
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - B A Trzeciak
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - O D Tsai
- Brookhaven National Laboratory, Upton, New York 11973
- University of California, Los Angeles, California 90095
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- Brookhaven National Laboratory, Upton, New York 11973
- Kent State University, Kent, Ohio 44242
| | - Z Tu
- Brookhaven National Laboratory, Upton, New York 11973
| | - T Ullrich
- Brookhaven National Laboratory, Upton, New York 11973
| | - D G Underwood
- Argonne National Laboratory, Argonne, Illinois 60439
- Valparaiso University, Valparaiso, Indiana 46383
| | - I Upsal
- Rice University, Houston, Texas 77251
| | - G Van Buren
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Vanek
- Brookhaven National Laboratory, Upton, New York 11973
| | - I Vassiliev
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
| | - V Verkest
- Wayne State University, Detroit, Michigan 48201
| | - F Videbæk
- Brookhaven National Laboratory, Upton, New York 11973
| | | | - F Wang
- Purdue University, West Lafayette, Indiana 47907
| | - G Wang
- University of California, Los Angeles, California 90095
| | - J S Wang
- Huzhou University, Huzhou, Zhejiang 313000
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- Shandong University, Qingdao, Shandong 266237
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- University of Science and Technology of China, Hefei, Anhui 230026
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- Central China Normal University, Wuhan, Hubei 430079
| | - Y Wang
- Tsinghua University, Beijing 100084
| | - Z Wang
- Shandong University, Qingdao, Shandong 266237
| | - J C Webb
- Brookhaven National Laboratory, Upton, New York 11973
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- Michigan State University, East Lansing, Michigan 48824
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- Warsaw University of Technology, Warsaw 00-661, Poland
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- Lawrence Berkeley National Laboratory, Berkeley, California 94720
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- University of Illinois at Chicago, Chicago, Illinois 60607
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- Indiana University, Bloomington, Indiana 47408
| | - R Witt
- United States Naval Academy, Annapolis, Maryland 21402
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- Central China Normal University, Wuhan, Hubei 430079
| | - J Wu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - X Wu
- University of California, Los Angeles, California 90095
| | - Y Wu
- University of California, Riverside, California 92521
| | - B Xi
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800
| | - Z G Xiao
- Tsinghua University, Beijing 100084
| | - W Xie
- Purdue University, West Lafayette, Indiana 47907
| | - H Xu
- Huzhou University, Huzhou, Zhejiang 313000
| | - N Xu
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - Q H Xu
- Shandong University, Qingdao, Shandong 266237
| | - Y Xu
- Shandong University, Qingdao, Shandong 266237
| | - Y Xu
- Central China Normal University, Wuhan, Hubei 430079
| | - Z Xu
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Xu
- University of California, Los Angeles, California 90095
| | - G Yan
- Shandong University, Qingdao, Shandong 266237
| | - Z Yan
- State University of New York, Stony Brook, New York 11794
| | - C Yang
- Shandong University, Qingdao, Shandong 266237
| | - Q Yang
- Shandong University, Qingdao, Shandong 266237
| | - S Yang
- South China Normal University, Guangzhou, Guangdong 510631
| | - Y Yang
- National Cheng Kung University, Tainan 70101
| | - Z Ye
- Rice University, Houston, Texas 77251
| | - Z Ye
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - L Yi
- Shandong University, Qingdao, Shandong 266237
| | - K Yip
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y Yu
- Shandong University, Qingdao, Shandong 266237
| | - H Zbroszczyk
- Warsaw University of Technology, Warsaw 00-661, Poland
| | - W Zha
- University of Science and Technology of China, Hefei, Anhui 230026
| | - C Zhang
- State University of New York, Stony Brook, New York 11794
| | - D Zhang
- Central China Normal University, Wuhan, Hubei 430079
| | - J Zhang
- Shandong University, Qingdao, Shandong 266237
| | - S Zhang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - X Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Zhang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Zhang
- Central China Normal University, Wuhan, Hubei 430079
| | - Z J Zhang
- National Cheng Kung University, Tainan 70101
| | - Z Zhang
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Zhang
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - F Zhao
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - J Zhao
- Fudan University, Shanghai 200433
| | - M Zhao
- Brookhaven National Laboratory, Upton, New York 11973
| | - C Zhou
- Fudan University, Shanghai 200433
| | - J Zhou
- University of Science and Technology of China, Hefei, Anhui 230026
| | - S Zhou
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Zhou
- Central China Normal University, Wuhan, Hubei 430079
| | - X Zhu
- Tsinghua University, Beijing 100084
| | - M Zurek
- Argonne National Laboratory, Argonne, Illinois 60439
| | - M Zyzak
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
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Qin C, Xiang Y, Liu J, Zhang R, Liu Z, Li T, Sun Z, Ouyang X, Zong Y, Zhang HM, Ouyang Q, Qian L, Lou C. Precise programming of multigene expression stoichiometry in mammalian cells by a modular and programmable transcriptional system. Nat Commun 2023; 14:1500. [PMID: 36932109 PMCID: PMC10023750 DOI: 10.1038/s41467-023-37244-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 03/09/2023] [Indexed: 03/19/2023] Open
Abstract
Context-dependency of mammalian transcriptional elements has hindered the quantitative investigation of multigene expression stoichiometry and its biological functions. Here, we describe a host- and local DNA context-independent transcription system to gradually fine-tune single and multiple gene expression with predictable stoichiometries. The mammalian transcription system is composed of a library of modular and programmable promoters from bacteriophage and its cognate RNA polymerase (RNAP) fused to a capping enzyme. The relative expression of single genes is quantitatively determined by the relative binding affinity of the RNAP to the promoters, while multigene expression stoichiometry is predicted by a simple biochemical model with resource competition. We use these programmable and modular promoters to predictably tune the expression of three components of an influenza A virus-like particle (VLP). Optimized stoichiometry leads to a 2-fold yield of intact VLP complexes. The host-independent orthogonal transcription system provides a platform for dose-dependent control of multiple protein expression which may be applied for advanced vaccine engineering, cell-fate programming and other therapeutic applications.
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Affiliation(s)
- Chenrui Qin
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, 100871, Beijing, China
- Peking-Tsinghua Joint Center for Life Sciences, Peking University, 100871, Beijing, China
| | - Yanhui Xiang
- Center for Cell and Gene Circuit Design, CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China
| | - Jie Liu
- Center for Cell and Gene Circuit Design, CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China
| | - Ruilin Zhang
- Yuanpei College, Peking University, 100871, Beijing, China
| | - Ziming Liu
- Center for Cell and Gene Circuit Design, CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China
| | - Tingting Li
- Center for Cell and Gene Circuit Design, CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China
| | - Zhi Sun
- College of Life Science, University of Chinese Academy of Science, 100149, Beijing, China
| | - Xiaoyi Ouyang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, 100871, Beijing, China
| | | | | | - Qi Ouyang
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, 100871, Beijing, China
| | - Long Qian
- Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, 100871, Beijing, China.
| | - Chunbo Lou
- Center for Cell and Gene Circuit Design, CAS Key Laboratory of Quantitative Engineering Biology, Guangdong Provincial Key Laboratory of Synthetic Genomics, Shenzhen Key Laboratory of Synthetic Genomics, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China.
- College of Life Science, University of Chinese Academy of Science, 100149, Beijing, China.
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Xu Y, Luan G, Li Z, Liu Z, Qin G, Chu Y. Correction to: Tumour-derived exosomal lncRNA SNHG16 induces telocytes to promote metastasis of hepatocellular carcinoma via the miR-942-3p/MMP9 axis. Cell Oncol (Dordr) 2023; 46:265-266. [PMID: 36913069 DOI: 10.1007/s13402-023-00782-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2023] Open
Affiliation(s)
- Ying Xu
- Shandong Cancer Hospital and Institute, Shandong Fist Medical University and Shandong Academy of Medical Science, No 440, Jiyan Road, Ji'nan, Shandong, China.
| | | | - Zhongchao Li
- Shandong Cancer Hospital and Institute, Shandong Fist Medical University and Shandong Academy of Medical Science, No 440, Jiyan Road, Ji'nan, Shandong, China
| | - Ziming Liu
- Shandong Fist Medical University and Shandong Academy of Medical Science, Ji'nan, Shandong, China
| | - Guangyang Qin
- Shandong Fist Medical University and Shandong Academy of Medical Science, Ji'nan, Shandong, China
| | - Yifu Chu
- Shandong Fist Medical University and Shandong Academy of Medical Science, Ji'nan, Shandong, China
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Li W, Pan X, Li M, Ling L, Zhang M, Liu Z, Zhang K, Guo J, Wang H. Impact of age on the rotenone-induced sporadic Parkinson's disease model using Drosophila melanogaster. Neurosci Lett 2023; 805:137187. [PMID: 36921666 DOI: 10.1016/j.neulet.2023.137187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 02/22/2023] [Accepted: 03/10/2023] [Indexed: 03/15/2023]
Abstract
Rotenone, a naturally occurring toxin, has been used to induce sporadic Parkinson's disease (PD) in Drosophila melanogaster for decades. However, the age of flies varies considerably between studies in this model. To investigate the impact of age on the rotenone-induced PD model, we collected male flies at the age of 1, 5, 7, and 10 days post-eclosion, respectively. Then, flies were immediately exposed to a feeding medium supplemented with 250 μM rotenone for seven days. The motor ability of Drosophila was detected by negative geotaxis assay, and the number of dopamine (DA) neurons and tyrosine hydroxylase (TH) expression levels were evaluated. The results showed that both the motor deficits and mortality increased with age. The flies older than five days showed typical PD features, including the loss of DA neurons, decreased TH expression levels, and decreased locomotive ability. However, 1-day-old flies displayed an unstable motor deficit and little TH expression changes after seven days of rotenone exposure. Lastly, after 7 days of exposure to rotenone, the death rate of flies rapidly increased with increasing starting age. The death rates of 1-, 5-, 7-, and 10-days old flies were 10.0%, 22.8%, 41.5%, and 50.4%, respectively. The findings of this study suggest that age is a crucial factor impacting the Drosophila PD model. This information provides a reference for the age selection to use this model for future studies.
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Affiliation(s)
- Wanrui Li
- Anesthesia Department, Affiliated Hospital of Hebei University, Baoding 071000, China
| | - Xuanyi Pan
- Anesthesia Department, Affiliated Hospital of Hebei University, Baoding 071000, China
| | - Ming Li
- Department of Pathogenic Biology, School of Basic Medical Sciences, Hebei University, Baoding 071000, China
| | - Li Ling
- Anesthesia Department, Affiliated Hospital of Hebei University, Baoding 071000, China
| | - MengMeng Zhang
- Anesthesia Department, Affiliated Hospital of Hebei University, Baoding 071000, China
| | - Ziming Liu
- Anesthesia Department, Affiliated Hospital of Hebei University, Baoding 071000, China
| | - Ke Zhang
- Department of Pathogenic Biology, School of Basic Medical Sciences, Hebei University, Baoding 071000, China
| | - Jiguang Guo
- Department of Pathogenic Biology, School of Basic Medical Sciences, Hebei University, Baoding 071000, China
| | - Hongjie Wang
- Anesthesia Department, Affiliated Hospital of Hebei University, Baoding 071000, China.
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Zhang L, Zhang W, Wu X, Cui H, Yan P, Yang C, Zhao X, Xiao J, Xiao C, Tang M, Wang Y, Chen L, Liu Y, Zou Y, Zhang L, Yang Y, Yao Y, Li J, Liu Z, Yang C, Zhang B, Jiang X. A sex- and site-specific relationship between body mass index and osteoarthritis: evidence from observational and genetic analyses. Osteoarthritis Cartilage 2023; 31:819-828. [PMID: 36889626 DOI: 10.1016/j.joca.2023.02.073] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 02/18/2023] [Accepted: 02/22/2023] [Indexed: 03/10/2023]
Abstract
OBJECTIVE We primarily aimed to investigate whether there are phenotypic and genetic links underlying body mass index (BMI) and overall osteoarthritis (OA). We then intended to explore whether the relationships differ across sexes and sites. METHOD We first evaluated the phenotypic association between BMI and overall OA using data from the UK Biobank. We then investigated the genetic relationship leveraging summary statistics of the hitherto largest genome-wide association studies performed for BMI and overall OA. Finally, we repeated all analyses in a sex- (female, male) and site- (knee, hip, spine) specific manner. RESULTS Observational analysis suggested an increased hazard of diagnosed OA per 5 kg/m2 increment in BMI (hazard ratio = 1.38, 95% confidence interval (CI) = 1.37-1.39). A positive overall genetic correlation was observed for BMI and OA (rg = 0.43, P = 4.72 × 10-133), corroborated by 11 significant local signals. Cross-trait meta-analysis identified 34 pleiotropic loci shared between BMI and OA, of which seven were novel. Transcriptome-wide association study revealed 29 shared gene-tissue pairs, targeting nervous, digestive, and exo/endocrine systems. Mendelian randomization demonstrated a robust BMI-OA causal relationship (odds ratio = 1.47, 95% CI = 1.42-1.52). A similar pattern of effects was observed in sex- and site-specific analyses, with BMI affecting OA comparably in both sexes and most strongly in the knee. CONCLUSION Our work demonstrates an intrinsic relationship underlying BMI and overall OA, reflected by a pronounced phenotypic association, significant biological pleiotropy, and a putative causal link. Stratified analysis further reveals that the effects are distinct across sites and comparable across sexes.
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Affiliation(s)
- L Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - W Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - X Wu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - H Cui
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - P Yan
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - C Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - X Zhao
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - J Xiao
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - C Xiao
- Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - M Tang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Y Wang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - L Chen
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Y Liu
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Y Zou
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - L Zhang
- Department of Iatrical Polymer Material and Artificial Apparatus, School of Polymer Science and Engineering, Sichuan University, Chengdu, China
| | - Y Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Y Yao
- Department of Occupational and Environmental Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - J Li
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Z Liu
- Department of Maternal, Child and Adolescent Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - C Yang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - B Zhang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - X Jiang
- Department of Epidemiology and Biostatistics, Institute of Systems Epidemiology, and West China-PUMC C. C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China; Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China; Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
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Shahani S, Durm G, Althouse S, Liu Z, Hanna N. PP01.64 A Safety and Efficacy Analysis Comparing Elderly vs Nonelderly Patients Treated with Consolidation Immunotherapy after Chemoradiation for stage III NSCLC from the BTCRC LUN 16-081 Clinical Trial. J Thorac Oncol 2023. [DOI: 10.1016/j.jtho.2022.09.090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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99
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Abstract
In the last decade, deep neural networks (DNNs) have become dominant tools for various of supervised learning tasks, especially classification. However, it is demonstrated that they can easily overfit to training set biases, such as label noise and class imbalance. Example reweighting algorithms are simple and effective solutions against this issue, but most of them require manually specifying the weighting functions as well as additional hyperparameters. Recently, a meta-learning-based method Meta-Weight-Net (MW-Net) has been proposed to automatically learn the weighting function parameterized by an MLP via additional unbiased metadata, which significantly improves the robustness of prior arts. The method, however, is proposed in a deterministic manner, and short of intrinsic statistical support. In this work, we propose a probabilistic formulation for MW-Net, probabilistic MW-Net (PMW-Net) in short, which treats the weighting function in a probabilistic way, and can include the original MW-Net as a special case. By this probabilistic formulation, additional randomness is introduced while the flexibility of the weighting function can be further controlled during learning. Our experimental results on both synthetic and real datasets show that the proposed method improves the performance of the original MW-Net. Besides, the proposed PMW-Net can also be further extended to fully Bayesian models, to improve their robustness.
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100
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Zhang Y, Gao C, Wang P, Liu Y, Liu Z, Xie W, Xu H, Dang Y, Liu D, Ren Z, Yan S, Wang Z, Hu W, Dong H. High Electron Mobility Hot-Exciton Induced Delayed Fluorescent Organic Semiconductors. Angew Chem Int Ed Engl 2023; 62:e202217653. [PMID: 36631427 DOI: 10.1002/anie.202217653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/09/2023] [Accepted: 01/10/2023] [Indexed: 01/13/2023]
Abstract
The development of high mobility emissive organic semiconductors is of great significance for the fabrication of miniaturized optoelectronic devices, such as organic light emitting transistors. However, great challenge exists in designing key materials, especially those who integrates triplet exciton utilization ability. Herein, dinaphthylanthracene diimides (DNADIs), with 2,6-extended anthracene donor, and 3'- or 4'-substituted naphthalene monoimide acceptors were designed and synthesized. By introducing acceptor-donor-acceptor structure, both materials show high electron mobility. Moreover, by fine-tuning of substitution sites, good integration with high solid state photoluminescence quantum yield of 26 %, high electron mobility of 0.02 cm2 V-1 s-1 , and the feature of hot-exciton induced delayed fluorescence were obtained in 4'-DNADI. This work opens a new avenue for developing high electron mobility emissive organic semiconductors with efficient utilization of triplet excitons.
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Affiliation(s)
- Y Zhang
- National Laboratory for Molecular Sciences, Key Laboratory of Organic Solids, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China.,Department of Chemistry, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - C Gao
- National Laboratory for Molecular Sciences, Key Laboratory of Organic Solids, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China
| | - P Wang
- National Laboratory for Molecular Sciences, Key Laboratory of Organic Solids, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China.,Department of Chemistry, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Y Liu
- Department Key Laboratory of Rubber-Plastics, Ministry of Education/ Shandong Provincial Key Laboratory of Rubber-plastics, Qingdao University of Science & Technology, Qingdao, 266042, China
| | - Z Liu
- College of Materials Science and Engineering, Fuzhou University, Fuzhou, 350108, China
| | - W Xie
- Key Laboratory of Advanced Energy Materials Chemistry (Ministry of Education) Renewable Energy Conversion and Storage Center, College of Chemistry, Nankai University, Tianjin, 300071, China
| | - H Xu
- Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Sciences, Tianjin University & Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, 300072, China
| | - Y Dang
- Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Sciences, Tianjin University & Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, 300072, China
| | - D Liu
- National Laboratory for Molecular Sciences, Key Laboratory of Organic Solids, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China
| | - Z Ren
- State Key Laboratory of Chemical Resource Engineering, College of Materials Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, China
| | - S Yan
- Department Key Laboratory of Rubber-Plastics, Ministry of Education/ Shandong Provincial Key Laboratory of Rubber-plastics, Qingdao University of Science & Technology, Qingdao, 266042, China.,State Key Laboratory of Chemical Resource Engineering, College of Materials Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Z Wang
- Department of Chemistry, Tsinghua University, Beijing, 100084, China
| | - W Hu
- Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Sciences, Tianjin University & Collaborative Innovation Center of Chemical Science and Engineering, Tianjin, 300072, China.,Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Binhai New City, 350207, China
| | - H Dong
- National Laboratory for Molecular Sciences, Key Laboratory of Organic Solids, Institute of Chemistry, Chinese Academy of Sciences, Beijing, 100190, China.,Department of Chemistry, University of Chinese Academy of Sciences, Beijing, 100049, China
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