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Zhao CG, Cai J, Du C, Gao Q, Han J, Xie J. Manganese(I)-Catalyzed Enantioselective C(sp 2)-C(sp 3) Bond-Forming for the Synthesis of Skipped Dienes with Synergistic Aminocatalysis. Angew Chem Int Ed Engl 2024; 63:e202400177. [PMID: 38488857 DOI: 10.1002/anie.202400177] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Indexed: 04/09/2024]
Abstract
Mn(I)-catalyzed enantioselective C-C bond-forming reactions represent a great challenge in homogeneous catalysis primarily due to a limited understanding of its mechanistic principles. Herein, we have developed an interesting catalytic strategy that leverages a synergistic combination of a dimeric manganese(I) catalyst and a chiral aminocatalyst to address this issue. A range of conjugated dienals and trienals can exclusively proceed 1,4-hydroalkenylation by using readily available aromatic and aliphatic alkenyl boronic acids as coupling partners, producing a rich library of skipped diene aldehydes in synthetically useful yields and high levels of enantioselectivities. Notably, downstream transformations of these products can not only afford a concise approach to construct enantioenriched skipped trienes but also realize enantioselective total synthesis of analogues to (-)-Blepharocalyxin D in four steps. DFT calculations suggest the 1,4-hydroalkenylation is kinetically more favorable than 1,6-hydroalkenylation.
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Affiliation(s)
- Chuan-Gang Zhao
- State Key Laboratory of Coordination Chemistry, Jiangsu Key Laboratory of Advanced Organic Materials, Chemistry and Biomedicine Innovation Center (ChemBIC), School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China
| | - Junzhe Cai
- State Key Laboratory of Coordination Chemistry, Jiangsu Key Laboratory of Advanced Organic Materials, Chemistry and Biomedicine Innovation Center (ChemBIC), School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China
| | - Chaoyu Du
- State Key Laboratory of Coordination Chemistry, Jiangsu Key Laboratory of Advanced Organic Materials, Chemistry and Biomedicine Innovation Center (ChemBIC), School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China
| | - Qi Gao
- State Key Laboratory of Coordination Chemistry, Jiangsu Key Laboratory of Advanced Organic Materials, Chemistry and Biomedicine Innovation Center (ChemBIC), School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China
| | - Jie Han
- State Key Laboratory of Coordination Chemistry, Jiangsu Key Laboratory of Advanced Organic Materials, Chemistry and Biomedicine Innovation Center (ChemBIC), School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China
| | - Jin Xie
- State Key Laboratory of Coordination Chemistry, Jiangsu Key Laboratory of Advanced Organic Materials, Chemistry and Biomedicine Innovation Center (ChemBIC), School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China
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Xu KW, Liu XL, He B, Gao Q. Numerical methods for hemolysis and thrombus evaluation in the percutaneous ventricular assist device. Artif Organs 2024; 48:504-513. [PMID: 38146899 DOI: 10.1111/aor.14701] [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] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 11/03/2023] [Accepted: 12/12/2023] [Indexed: 12/27/2023]
Abstract
BACKGROUND A percutaneous ventricular assist device (pVAD) is an effective method to treat heart failure, but its complications, mainly hemolysis and thrombus formation, cannot be ignored. Accurate evaluation of hemolysis and thrombus formation in pVAD is essential to guide the development of pVAD and reduce the incidence of complications. METHODS This study optimized the numerical model to predict hemolysis and thrombus formation in pVAD. The hemolysis model is based on the power law function, and the multi-component thrombus prediction model is improved by introducing the von Willebrand factor. RESULTS The error between the numerical simulation and the hydraulic performance experiment is within 5%. The numerical results of hemolysis are in good agreement with those of in vitro experiments. Meanwhile, the thrombus location predicted by the numerical model is the same as that found in the in vivo experiment. CONCLUSION The numerical model suggested in this study may therefore accurately assess the possible hemolytic and thrombotic dangers in pVAD, making it an effective tool to support the development of pVAD.
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Affiliation(s)
- Ke-Wei Xu
- State Key Laboratory of Transvascular Implantation Devices, Zhejiang University, Hangzhou, China
- Department of Engineering Mechanics, School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, China
| | - Xing-Li Liu
- Zhejiang Diyuan Medical Instrument Co., Ltd., Hangzhou, China
| | - Bo He
- Zhejiang Diyuan Medical Instrument Co., Ltd., Hangzhou, China
| | - Qi Gao
- State Key Laboratory of Transvascular Implantation Devices, Zhejiang University, Hangzhou, China
- Department of Engineering Mechanics, School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, China
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Xia Y, Luo Q, Gao Q, Huang C, Chen P, Zou Y, Chen X, Liu W, Chen Z. SIRT1 activation ameliorates rhesus monkey liver fibrosis by inhibiting the TGF-β/smad signaling pathway. Chem Biol Interact 2024; 394:110979. [PMID: 38555046 DOI: 10.1016/j.cbi.2024.110979] [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: 12/21/2023] [Revised: 03/20/2024] [Accepted: 03/27/2024] [Indexed: 04/02/2024]
Abstract
TGF-β/Smad signaling pathway plays an important role in the pathogenesis and progression of liver fibrosis. Silent information regulator 1 (SIRT1) is a nicotinamide adenine dinucleotide (NAD+) dependent enzyme and responsible for deacetylating the proteins. Increasing numbers of reports have shown that the molecular mechanism of SIRT1 as an effective therapeutic target for liver fibrosis but the transformation is not very clear. In the present study, liver fibrotic tissues were screened by staining with Masson, hematoxylin-eosin staining (H&E) and Immunohistochemistry (IHC) for histopathological observation from the liver biopsy of seventy-seven rhesus monkey, which fixed with 4% paraformaldehyde (PFA) after treatment with high-fat diet (HFD) for two years. And the liver function was further determined by serum biochemical tests. The mRNA levels and protein expression of rat hepatic stellate (HSC-T6) cells were determined after treatment with Resveratrol (RSV) and Nicotinamide (NAM), respectively. The results showed that with the increasing of hepatic fibrosis in rhesus monkeys, the liver function impaired, and the transforming growth factor-β1 (TGF-β1), p-Smad3 (p-Smad3) and alpha-smooth muscle actin (α-SMA) was up-regulated, while SIRT1 and Smad7 were down-regulated. Moreover, when stimulated the HSC-T6 with RSV to activate SIRT1 for 6, 12, and 24 h, the results showed that RSV promoted the expression of smad7, while the expression of TGF-β1, p-Smad3 and α-SMA were inhibited. In contrast, when the cells stimulated with NAM to inhibit SIRT1 for 6, 12, and 24 h, the Smad7 expression was decreased, while TGF-β1, p-Smad3, and α-SMA expressions were increased. These results indicate that SIRT1 acts as an important protective factor for liver fibrosis, which may be attributed to inhibiting the signaling pathway of TGF-β/Smad in hepatic fibrosis of the rhesus monkey.
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Affiliation(s)
- Yu Xia
- Laboratory of Animal Disease Model, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, 611130, China; Animal Disease Prevention and Control and Healthy Breeding Engineering Technology, Research Centre, Mianyang Normal University, Mianyang, 621000, China
| | - Qihui Luo
- Laboratory of Animal Disease Model, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, 611130, China; Sichuan Primed Bio-Tech Group Co., Ltd., Chengdu, 610041, China
| | - Qi Gao
- Laboratory of Animal Disease Model, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, 611130, China
| | - Chao Huang
- Laboratory of Animal Disease Model, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, 611130, China
| | - Ping Chen
- Laboratory of Animal Disease Model, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, 611130, China
| | - Yao Zou
- Wanzhou District Livestock Industry Development Center, Chongqing, 404120, China
| | - Xiwen Chen
- Animal Disease Prevention and Control and Healthy Breeding Engineering Technology, Research Centre, Mianyang Normal University, Mianyang, 621000, China
| | - Wentao Liu
- Laboratory of Animal Disease Model, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, 611130, China
| | - Zhengli Chen
- Laboratory of Animal Disease Model, College of Veterinary Medicine, Sichuan Agricultural University, Chengdu, 611130, China; Sichuan Primed Bio-Tech Group Co., Ltd., Chengdu, 610041, China.
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Xiao X, Hu H, Meng X, Huang Z, Feng Y, Gao Q, Ruan W. Volatile fatty acids production from kitchen waste slurry using anaerobic membrane bioreactor via alkaline fermentation with high salinity: Evaluation on process performance and microbial succession. Bioresour Technol 2024; 399:130576. [PMID: 38479625 DOI: 10.1016/j.biortech.2024.130576] [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: 11/17/2023] [Revised: 03/08/2024] [Accepted: 03/10/2024] [Indexed: 03/22/2024]
Abstract
In this study, a pilot-scale anaerobic membrane bioreactor (AnMBR) was developed to continuously produce volatile fatty acids (VFAs) from kitchen waste slurry under an alkaline condition. The alkaline fermentation effectively suppressed methanogenesis, thus achieving high VFAs production of 60.3 g/L. Acetic acid, propionic acid, and butyric acid accounted for over 95.0 % of the total VFAs. The VFAs yield, productivity, and chemical oxygen demand (COD) recovery efficiency reached 0.5 g/g-CODinfluent, 6.0 kg/m3/d, and 62.8 %, respectively. Moreover, the CODVFAs/CODeffluent ratio exceeded 96.0 %, and the CODVFAs/NH3-N ratio through ammonia distillation reached up to 192.5. The microbial community was reshaped during the alkaline fermentation with increasing salinity. The membrane fouling of the AnMBR was alleviated by chemical cleaning and sludge discharge, and membrane modules displayed a sustained filtration performance. In conclusion, this study demonstrated that high-quality VFAs could be efficiently produced from kitchen waste slurry using an AnMBR process via alkaline fermentation.
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Affiliation(s)
- Xiaolan Xiao
- Jiangsu Key Laboratory of Anaerobic Biotechnology, School of Environment and Civil Engineering, Jiangnan University, Wuxi 214122, PR China; Jiangsu Collaborative Innovation Center of Technology and Material of Water Treatment, Suzhou University of Science and Technology, Suzhou 215009, PR China
| | - Hongmei Hu
- Jiangsu Key Laboratory of Anaerobic Biotechnology, School of Environment and Civil Engineering, Jiangnan University, Wuxi 214122, PR China
| | - Xingyao Meng
- Key Laboratory of Cleaner Production and Integrated Resource Utilization of China National Light Industry, Beijing Technology and Business University, Beijing 100048, PR China
| | - Zhenxing Huang
- Jiangsu Key Laboratory of Anaerobic Biotechnology, School of Environment and Civil Engineering, Jiangnan University, Wuxi 214122, PR China; Key Laboratory of Cleaner Production and Integrated Resource Utilization of China National Light Industry, Beijing Technology and Business University, Beijing 100048, PR China.
| | - Yongrui Feng
- Jiangsu Key Laboratory of Anaerobic Biotechnology, School of Environment and Civil Engineering, Jiangnan University, Wuxi 214122, PR China
| | - Qi Gao
- Jiangsu Key Laboratory of Anaerobic Biotechnology, School of Environment and Civil Engineering, Jiangnan University, Wuxi 214122, PR China
| | - Wenquan Ruan
- Jiangsu Key Laboratory of Anaerobic Biotechnology, School of Environment and Civil Engineering, Jiangnan University, Wuxi 214122, PR China; Jiangsu Collaborative Innovation Center of Technology and Material of Water Treatment, Suzhou University of Science and Technology, Suzhou 215009, PR China
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Van Asbroeck S, Köhler S, van Boxtel MPJ, Lipnicki DM, Crawford JD, Castro-Costa E, Lima-Costa MF, Blay SL, Shifu X, Wang T, Yue L, Lipton RB, Katz MJ, Derby CA, Guerchet M, Preux PM, Mbelesso P, Norton J, Ritchie K, Skoog I, Najar J, Sterner TR, Scarmeas N, Yannakoulia M, Dardiotis T, Rolandi E, Davin A, Rossi M, Gureje O, Ojagbemi A, Bello T, Kim KW, Han JW, Oh DJ, Trompet S, Gussekloo J, Riedel-Heller SG, Röhr S, Pabst A, Shahar S, Rivan NFM, Singh DKA, Jacobsen E, Ganguli M, Hughes T, Haan M, Aiello AE, Ding D, Zhao Q, Xiao Z, Narazaki K, Chen T, Chen S, Ng TP, Gwee X, Gao Q, Brodaty H, Trollor J, Kochan N, Lobo A, Santabárbara J, Gracia-Garcia P, Sachdev PS, Deckers K. Lifestyle and incident dementia: A COSMIC individual participant data meta‐analysis. Alzheimers Dement 2024. [PMID: 38676366 DOI: 10.1002/alz.13846] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 03/19/2024] [Accepted: 03/19/2024] [Indexed: 04/28/2024]
Abstract
INTRODUCTION The LIfestyle for BRAin Health (LIBRA) index yields a dementia risk score based on modifiable lifestyle factors and is validated in Western samples. We investigated whether the association between LIBRA scores and incident dementia is moderated by geographical location or sociodemographic characteristics. METHODS We combined data from 21 prospective cohorts across six continents (N = 31,680) and conducted cohort-specific Cox proportional hazard regression analyses in a two-step individual participant data meta-analysis. RESULTS A one-standard-deviation increase in LIBRA score was associated with a 21% higher risk for dementia. The association was stronger for Asian cohorts compared to European cohorts, and for individuals aged ≤75 years (vs older), though only within the first 5 years of follow-up. No interactions with sex, education, or socioeconomic position were observed. DISCUSSION Modifiable risk and protective factors appear relevant for dementia risk reduction across diverse geographical and sociodemographic groups. HIGHLIGHTS A two-step individual participant data meta-analysis was conducted. This was done at a global scale using data from 21 ethno-regionally diverse cohorts. The association between a modifiable dementia risk score and dementia was examined. The association was modified by geographical region and age at baseline. Yet, modifiable dementia risk and protective factors appear relevant in all investigated groups and regions.
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Affiliation(s)
- Stephanie Van Asbroeck
- Alzheimer Centrum Limburg, Department of Psychiatry and Neuropsychology, Mental Health and Neuroscience (MHeNs) Research Institute, Maastricht University, Maastricht, The Netherlands
| | - Sebastian Köhler
- Alzheimer Centrum Limburg, Department of Psychiatry and Neuropsychology, Mental Health and Neuroscience (MHeNs) Research Institute, Maastricht University, Maastricht, The Netherlands
| | - Martin P J van Boxtel
- Alzheimer Centrum Limburg, Department of Psychiatry and Neuropsychology, Mental Health and Neuroscience (MHeNs) Research Institute, Maastricht University, Maastricht, The Netherlands
| | - Darren M Lipnicki
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - John D Crawford
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Erico Castro-Costa
- René Rachou Institute, Fiocruz Minas, Belo Horizonte, Minas Gerais, Brazil
| | | | - Sergio Luis Blay
- Department of Psychiatry, Federal University of São Paulo, São Paulo, Brazil
| | - Xiao Shifu
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tao Wang
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Psychiatry & Affective Disorders Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai, China
| | - Ling Yue
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Richard B Lipton
- Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, New York, USA
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA
- Department of Psychiatry and Behavioral Medicine, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Mindy J Katz
- Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Carol A Derby
- Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, New York, USA
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Maëlenn Guerchet
- Inserm U1094, IRD UMR270, University Limoges, CHU Limoges, EpiMaCT - Epidemiology of chronic diseases in tropical zone, Institute of Epidemiology and Tropical Neurology, OmegaHealth, Limoges, France
| | - Pierre-Marie Preux
- Inserm U1094, IRD UMR270, University Limoges, CHU Limoges, EpiMaCT - Epidemiology of chronic diseases in tropical zone, Institute of Epidemiology and Tropical Neurology, OmegaHealth, Limoges, France
| | - Pascal Mbelesso
- Department of Neurology, Amitié Hospital, Bangui, Central African Republic
| | - Joanna Norton
- Institute for Neurosciences of Montpellier (INM), University of Montpellier, Inserm, Montpellier, France
| | - Karen Ritchie
- Institute for Neurosciences of Montpellier (INM), University of Montpellier, Inserm, Montpellier, France
- Institut du Cerveau Trocadéro, Paris, France
| | - Ingmar Skoog
- Department of Psychiatry and Neurochemistry, Neuropsychiatric Epidemiology Unit, Institute of Neuroscience and Physiology, Sahlgrenska Academy, at the University of Gothenburg, Gothenburg, Sweden
- Centre for Ageing and Health (AGECAP), University of Gothenburg, Gothenburg, Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden
| | - Jenna Najar
- Department of Psychiatry and Neurochemistry, Neuropsychiatric Epidemiology Unit, Institute of Neuroscience and Physiology, Sahlgrenska Academy, at the University of Gothenburg, Gothenburg, Sweden
- Centre for Ageing and Health (AGECAP), University of Gothenburg, Gothenburg, Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden
- Department of Clinical Genetics, Section Genomics of Neurodegenerative Diseases and Aging, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Therese Rydberg Sterner
- Department of Psychiatry and Neurochemistry, Neuropsychiatric Epidemiology Unit, Institute of Neuroscience and Physiology, Sahlgrenska Academy, at the University of Gothenburg, Gothenburg, Sweden
- Centre for Ageing and Health (AGECAP), University of Gothenburg, Gothenburg, Sweden
- Department of Neurobiology, Aging Research Center, Care Sciences and Society, Karolinska Institute and Stockholm University, Stockholm, Sweden
| | - Nikolaos Scarmeas
- 1st Department of Neurology, Aiginition Hospital, Medical School, National and Kapodistrian University of Athens, Athens, Greece
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Gertrude H. Sergievsky Center, Columbia University, New York, New York, USA
| | - Mary Yannakoulia
- Department of Nutrition and Dietetics, Harokopio University, Athens, Greece
| | | | - Elena Rolandi
- Golgi Cenci Foundation, Milan, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | | | | | - Oye Gureje
- Department of Psychiatry, WHO Collaborating Centre for Research and Training in Mental Health, Neuroscience and Substance Abuse, University of Ibadan, Ibadan, Nigeria
| | - Akin Ojagbemi
- Department of Psychiatry, College of Medicine University of Ibadan, Ibadan, Nigeria
| | - Toyin Bello
- Department of Psychiatry, College of Medicine University of Ibadan, Ibadan, Nigeria
| | - Ki Woong Kim
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
| | - Ji Won Han
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Dae Jong Oh
- Workplace Mental Health Institute, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Stella Trompet
- Department of Internal Medicine, section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
| | - Jacobijn Gussekloo
- Department of Internal Medicine, section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands
| | - Steffi G Riedel-Heller
- Institute of Social Medicine, Occupational Health and Public Health, Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Susanne Röhr
- Institute of Social Medicine, Occupational Health and Public Health, Medical Faculty, University of Leipzig, Leipzig, Germany
- Health and Ageing Research Team (HART), School of Psychology, Massey University, Palmerston North, Aotearoa New Zealand
- Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin, Ireland
| | - Alexander Pabst
- Institute of Social Medicine, Occupational Health and Public Health, Medical Faculty, University of Leipzig, Leipzig, Germany
| | - Suzana Shahar
- Center for Healthy Ageing & Wellness (H-CARE), Faculty of Health Sciences, University Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Nurul Fatin Malek Rivan
- Center for Healthy Ageing & Wellness (H-CARE), Faculty of Health Sciences, University Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Devinder Kaur Ajit Singh
- Center for Healthy Ageing & Wellness (H-CARE), Faculty of Health Sciences, University Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Erin Jacobsen
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Mary Ganguli
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Departments of Neurology, and Epidemiology, University of Pittsburgh School of Medicine and School of Public Health, Pittsburgh, Pennsylvania, USA
| | - Tiffany Hughes
- Department of Graduate Studies in Health and Rehabilitation Sciences, Bitonte College of Health and Human Services, Youngstown State University, Youngstown, Ohio, USA
| | - Mary Haan
- Department of Epidemiology and Biostatistics, School of Medicine, University of California San Francisco, San Francisco, California, USA
| | - Allison E Aiello
- Columbia Aging Center and the Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA
| | - Ding Ding
- Institute of Neurology, National Center for Neurological Disorders, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Qianhua Zhao
- Institute of Neurology, National Center for Neurological Disorders, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Zhenxu Xiao
- Institute of Neurology, National Center for Neurological Disorders, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Kenji Narazaki
- Center for Liberal Arts, Fukuoka Institute of Technology, Higashi-ku, Fukuoka, Japan
| | - Tao Chen
- Department of Physical Education, Sports and Health Research Center, Tongji University, Shanghai, China
| | - Sanmei Chen
- Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Tze Pin Ng
- Department of Psychological Medicine, Gerontology Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Xinyi Gwee
- Department of Psychological Medicine, Gerontology Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Qi Gao
- Department of Psychological Medicine, Gerontology Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Henry Brodaty
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Julian Trollor
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney, New South Wales, Australia
- Department of Developmental Disability Neuropsychiatry, Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Nicole Kochan
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney, New South Wales, Australia
| | - Antonio Lobo
- Department of Medicine and Psychiatry, Universidad de Zaragoza, Zaragoza, Spain
- Instituto de Investigación Sanitaria Aragón (IIS Aragón), Zaragoza, Spain
- CIBERSAM, Instituto de Salud Carlos III, Madrid, Spain
| | - Javier Santabárbara
- Instituto de Investigación Sanitaria Aragón (IIS Aragón), Zaragoza, Spain
- CIBERSAM, Instituto de Salud Carlos III, Madrid, Spain
- Department of Public Health, Universidad de Zaragoza, Zaragoza, Spain
| | - Patricia Gracia-Garcia
- Department of Medicine and Psychiatry, Universidad de Zaragoza, Zaragoza, Spain
- Instituto de Investigación Sanitaria Aragón (IIS Aragón), Zaragoza, Spain
- CIBERSAM, Instituto de Salud Carlos III, Madrid, Spain
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney, New South Wales, Australia
- Neuropsychiatric Institute, The Prince of Wales Hospital, Sydney, New South Wales, Australia
| | - Kay Deckers
- Alzheimer Centrum Limburg, Department of Psychiatry and Neuropsychology, Mental Health and Neuroscience (MHeNs) Research Institute, Maastricht University, Maastricht, The Netherlands
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Xu F, Cheng P, Xu J, Wang X, Jiang Z, Zhu H, Fan H, Wang Q, Gao Q. Influencing factors of length of stay among repeatedly hospitalized patients with mood disorders: a longitudinal study in China. Ann Gen Psychiatry 2024; 23:15. [PMID: 38664741 PMCID: PMC11046813 DOI: 10.1186/s12991-024-00497-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] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 03/02/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Patients with mood disorders usually require repeated and prolonged hospitalization, resulting in a heavy burden on healthcare resources. This study aims to identify variables associated with length of stay(LOS) of repeatedly hospitalized patients with mood disorders and to provide information for optimizing psychiatry management and healthcare resource allocation. METHODS Electronic medical records (EMRs) of repeatedly hospitalized patients with mood disorders from January 2010 to December 2018 were collected and retrospectively analyzed. Chi-square and t-test were adopted to investigate the differences in characteristics between the two groups of short LOS and long LOS. Generalized estimating equation (GEE) was conducted to investigate potential factors influencing LOS. RESULTS A total of 2,009 repeatedly hospitalized patients with mood disorders were enrolled, of which 797 (39.7%) had a long LOS and 1,212 (60.3%) had a short LOS. Adverse effects of treatment, continuous clinical manifestation, chronic onset type, suicide attempt, comorbidity and use of antidepressants were positively associated with long LOS among all repeatedly hospitalized patients with mood disorders (P < 0.050). For patients with depression, factors associated with long LOS consisted of age, monthly income, adverse effects of treatment, continuous clinical manifestation, suicide attempt and comorbidity (P < 0.050). Whereas, for patients with bipolar disorder (BD), adverse effects of treatment, four or more hospitalizations and use of antidepressants contributed to the long LOS (P < 0.050). Influencing factors of LOS also vary among patients with different effectiveness of treatment. CONCLUSION The LOS in repeatedly hospitalized patients with mood disorders was influenced by multiple factors. There were discrepancies in the factors affecting LOS in patients with different diagnoses and effectiveness of treatment, and specific factors should be addressed when evaluating the LOS.
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Affiliation(s)
- Feng Xu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xitoutiao, Youanmen Wai, Beijing, 100069, China
| | - Peixia Cheng
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xitoutiao, Youanmen Wai, Beijing, 100069, China
| | - Jiaying Xu
- Capital Medical University Affiliated Beijing Anding Hospital, Beijing, China
| | - Xiaonan Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xitoutiao, Youanmen Wai, Beijing, 100069, China
| | - Zhen Jiang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xitoutiao, Youanmen Wai, Beijing, 100069, China
| | - Huiping Zhu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xitoutiao, Youanmen Wai, Beijing, 100069, China
| | - Hua Fan
- Capital Medical University Affiliated Beijing Anding Hospital, Beijing, China
| | - Qian Wang
- Capital Medical University Affiliated Beijing Anding Hospital, Beijing, China
| | - Qi Gao
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, 10 Xitoutiao, Youanmen Wai, Beijing, 100069, China.
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Chen X, Shen J, Liu C, Shi X, Feng W, Sun H, Zhang W, Zhang S, Jiao Y, Chen J, Hao K, Gao Q, Li Y, Hong W, Wang P, Feng L, Yue S. Applications of Data Characteristic AI-Assisted Raman Spectroscopy in Pathological Classification. Anal Chem 2024; 96:6158-6169. [PMID: 38602477 DOI: 10.1021/acs.analchem.3c04930] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
Raman spectroscopy has been widely used for label-free biomolecular analysis of cells and tissues for pathological diagnosis in vitro and in vivo. AI technology facilitates disease diagnosis based on Raman spectroscopy, including machine learning (PCA and SVM), manifold learning (UMAP), and deep learning (ResNet and AlexNet). However, it is not clear how to optimize the appropriate AI classification model for different types of Raman spectral data. Here, we selected five representative Raman spectral data sets, including endometrial carcinoma, hepatoma extracellular vesicles, bacteria, melanoma cell, diabetic skin, with different characteristics regarding sample size, spectral data size, Raman shift range, tissue sites, Kullback-Leibler (KL) divergence, and significant Raman shifts (i.e., wavenumbers with significant differences between groups), to explore the performance of different AI models (e.g., PCA-SVM, SVM, UMAP-SVM, ResNet or AlexNet). For data set of large spectral data size, Resnet performed better than PCA-SVM and UMAP. By building data characteristic-assisted AI classification model, we optimized the network parameters (e.g., principal components, activation function, and loss function) of AI model based on data size and KL divergence etc. The accuracy improved from 85.1 to 94.6% for endometrial carcinoma grading, from 77.1 to 90.7% for hepatoma extracellular vesicles detection, from 89.3 to 99.7% for melanoma cell detection, from 88.1 to 97.9% for bacterial identification, from 53.7 to 85.5% for diabetic skin screening, and mean time expense of 5 s.
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Affiliation(s)
- Xun Chen
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
- School of Engineering Medicine, Beihang University, Beijing 100191, China
| | - Jianghao Shen
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Chang Liu
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Xiaoyu Shi
- Department of Obstetrics & Gynecology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China
| | - Weichen Feng
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Hongyi Sun
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Weifeng Zhang
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Shengpai Zhang
- Department of Obstetrics & Gynecology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China
| | - Yuqing Jiao
- Department of Obstetrics & Gynecology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China
| | - Jing Chen
- Su Zhou Surgi-Master High Tech Co., Ltd., Zhangjiagang, Suzhou 215626, China
| | - Kun Hao
- Research and Development Center, Beijing Yaogen Biotechnology Co., Ltd., Beijing 102600, China
| | - Qi Gao
- Research and Development Center, Beijing Yaogen Biotechnology Co., Ltd., Beijing 102600, China
| | - Yitong Li
- Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100021, China
| | - Weili Hong
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Pu Wang
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Limin Feng
- Department of Obstetrics & Gynecology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China
| | - Shuhua Yue
- Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Institute of Medical Photonics, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
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Zhuang W, Liu C, Hong Y, Zheng Y, Huang M, Tang H, Zhao L, Huang Z, Tu M, Yu L, Chen J, Zhang Y, Chen X, Lin F, Gao Q, Yu C, Huang Y. Tumor-suppressive miR-4732-3p is sorted into fucosylated exosome by hnRNPK to avoid the inhibition of lung cancer progression. J Exp Clin Cancer Res 2024; 43:123. [PMID: 38654325 PMCID: PMC11036635 DOI: 10.1186/s13046-024-03048-1] [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] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 04/16/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Aberrant fucosylation observed in cancer cells contributes to an augmented release of fucosylated exosomes into the bloodstream, where miRNAs including miR-4732-3p hold promise as potential tumor biomarkers in our pilot study. However, the mechanisms underlying the sorting of miR-4732-3p into fucosylated exosomes during lung cancer progression remain poorly understood. METHODS A fucose-captured strategy based on lentil lectin-magnetic beads was utilized to isolate fucosylated exosomes and evaluate the efficiency for capturing tumor-derived exosomes using nanoparticle tracking analysis (NTA). Fluorescence in situ hybridization (FISH) and qRT-PCR were performed to determine the levels of miR-4732-3p in non-small cell lung cancer (NSCLC) tissue samples. A co-culture system was established to assess the release of miRNA via exosomes from NSCLC cells. RNA immunoprecipitation (RIP) and miRNA pull-down were applied to validate the interaction between miR-4732-3p and heterogeneous nuclear ribonucleoprotein K (hnRNPK) protein. Cell functional assays, cell derived xenograft, dual-luciferase reporter experiments, and western blot were applied to examine the effects of miR-4732-3p on MFSD12 and its downstream signaling pathways, and the impact of hnRNPK in NSCLC. RESULTS We enriched exosomes derived from NSCLC cells using the fucose-captured strategy and detected a significant upregulation of miR-4732-3p in fucosylated exosomes present in the serum, while its expression declined in NSCLC tissues. miR-4732-3p functioned as a tumor suppressor in NSCLC by targeting 3'UTR of MFSD12, thereby inhibiting AKT/p21 signaling pathway to induce cell cycle arrest in G2/M phase. NSCLC cells preferentially released miR-4732-3p via exosomes instead of retaining them intracellularly, which was facilitated by the interaction of miR-4732-3p with hnRNPK protein for selective sorting into fucosylated exosomes. Moreover, knockdown of hnRNPK suppressed NSCLC cell proliferation, with the elevated levels of miR-4732-3p in NSCLC tissues but the decreased expression in serum fucosylated exosomes. CONCLUSIONS NSCLC cells escape suppressive effects of miR-4732-3p through hnRNPK-mediated sorting of them into fucosylated exosomes, thus supporting cell malignant properties and promoting NSCLC progression. Our study provides a promising biomarker for NSCLC and opens a novel avenue for NSCLC therapy by targeting hnRNPK to prevent the "exosome escape" of tumor-suppressive miR-4732-3p from NSCLC cells.
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Affiliation(s)
- Wanzhen Zhuang
- Shengli Clinical Medical College, Fujian Medical University, Fuzhou, 350001, China
- Department of Clinical Laboratory, Fujian Provincial Hospital, Fuzhou, 350001, China
| | - Chengxiu Liu
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiamen, 361102, China
- Institute of Future Technology, Beijing Hotgen Biotech Co., Ltd, Beijing, 102600, China
| | - Yilin Hong
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiamen, 361102, China
| | - Yue Zheng
- Shengli Clinical Medical College, Fujian Medical University, Fuzhou, 350001, China
- Department of Clinical Laboratory, Fujian Provincial Hospital, Fuzhou, 350001, China
| | - Minjian Huang
- Shengli Clinical Medical College, Fujian Medical University, Fuzhou, 350001, China
- Center for Experimental Research in Clinical Medicine, Fujian Provincial Hospital, Fuzhou, 350001, China
| | - Haijun Tang
- Shengli Clinical Medical College, Fujian Medical University, Fuzhou, 350001, China
- Center for Experimental Research in Clinical Medicine, Fujian Provincial Hospital, Fuzhou, 350001, China
| | - Lilan Zhao
- Shengli Clinical Medical College, Fujian Medical University, Fuzhou, 350001, China
- Department of Thoracic Surgery, Fujian Provincial Hospital, Fuzhou, 350001, China
| | - Zhixin Huang
- Department of Clinical Laboratory, Fujian Provincial Hospital, Fuzhou, 350001, China
- Integrated Chinese and Western Medicine College, Fujian University of Traditional Chinese Medicine, Fuzhou, 350108, China
| | - Mingshu Tu
- Shengli Clinical Medical College, Fujian Medical University, Fuzhou, 350001, China
- Department of Clinical Laboratory, Fujian Provincial Hospital, Fuzhou, 350001, China
| | - Lili Yu
- Shengli Clinical Medical College, Fujian Medical University, Fuzhou, 350001, China
- Department of Clinical Laboratory, Fujian Provincial Hospital, Fuzhou, 350001, China
| | - Jianlin Chen
- Shengli Clinical Medical College, Fujian Medical University, Fuzhou, 350001, China
- Department of Clinical Laboratory, Fujian Provincial Hospital, Fuzhou, 350001, China
| | - Yi Zhang
- Shengli Clinical Medical College, Fujian Medical University, Fuzhou, 350001, China
- Department of Clinical Laboratory, Fujian Provincial Hospital, Fuzhou, 350001, China
| | - Xiongfeng Chen
- Shengli Clinical Medical College, Fujian Medical University, Fuzhou, 350001, China
- Department of Scientific Research, Fujian Provincial Hospital, Fuzhou, 350001, China
| | - Fan Lin
- Department of Geriatric Medicine, Fujian Provincial Hospital, Fuzhou, 350001, China
- Fujian Provincial Center for Geriatrics, Fujian Provincial Hospital, Fuzhou, 350001, China
| | - Qi Gao
- Institute of Future Technology, Beijing Hotgen Biotech Co., Ltd, Beijing, 102600, China
| | - Chundong Yu
- Shengli Clinical Medical College, Fujian Medical University, Fuzhou, 350001, China.
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiamen, 361102, China.
- Center for Experimental Research in Clinical Medicine, Fujian Provincial Hospital, Fuzhou, 350001, China.
| | - Yi Huang
- Shengli Clinical Medical College, Fujian Medical University, Fuzhou, 350001, China.
- Department of Clinical Laboratory, Fujian Provincial Hospital, Fuzhou, 350001, China.
- Center for Experimental Research in Clinical Medicine, Fujian Provincial Hospital, Fuzhou, 350001, China.
- Fujian Provincial Center for Geriatrics, Fujian Provincial Hospital, Fuzhou, 350001, China.
- Central Laboratory, Fujian Provincial Hospital, Fuzhou, 350001, China.
- Fujian Provincial Key Laboratory of Critical Care Medicine, Fujian Provincial Key Laboratory of Cardiovascular Disease, Fuzhou, 350001, China.
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Gao Q, Ji Z, Wang L, Owzar K, Li QJ, Chan C, Xie J. SifiNet: a robust and accurate method to identify feature gene sets and annotate cells. Nucleic Acids Res 2024:gkae307. [PMID: 38647069 DOI: 10.1093/nar/gkae307] [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: 08/10/2023] [Revised: 03/25/2024] [Accepted: 04/14/2024] [Indexed: 04/25/2024] Open
Abstract
SifiNet is a robust and accurate computational pipeline for identifying distinct gene sets, extracting and annotating cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Uniquely, SifiNet bypasses the cell clustering stage, commonly integrated into other cellular annotation pipelines, thereby circumventing potential inaccuracies in clustering that may compromise subsequent analyses. Consequently, SifiNet has demonstrated superior performance in multiple experimental datasets compared with other state-of-the-art methods. SifiNet can analyze both single-cell RNA and ATAC sequencing data, thereby rendering comprehensive multi-omic cellular profiles. It is conveniently available as an open-source R package.
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Affiliation(s)
- Qi Gao
- Department of Biostatistics and Bioinformatics, Duke University, USA
| | - Zhicheng Ji
- Department of Biostatistics and Bioinformatics, Duke University, USA
| | - Liuyang Wang
- Department of Molecular Genetics and Microbiology, Duke University, USA
| | - Kouros Owzar
- Department of Biostatistics and Bioinformatics, Duke University, USA
| | - Qi-Jing Li
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore
- Singapore Immunology Network, Agency for Science, Technology and Research, Singapore
| | - Cliburn Chan
- Department of Biostatistics and Bioinformatics, Duke University, USA
| | - Jichun Xie
- Department of Biostatistics and Bioinformatics, Duke University, USA
- Department of Mathematics, Duke University, USA
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Zhang X, Wu C, Guo Y, Ren X, Meng Y, Gao Q, Zhang F, Wang Y, Guo J. Genome-Wide Analysis Elucidates the Roles of GhTIR1/ AFB Genes Reveals the Function of Gh_D08G0763 ( GhTIR1) in Cold Stress in G. hirsutum. Plants (Basel) 2024; 13:1152. [PMID: 38674561 PMCID: PMC11055017 DOI: 10.3390/plants13081152] [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: 02/22/2024] [Revised: 04/16/2024] [Accepted: 04/17/2024] [Indexed: 04/28/2024]
Abstract
This study identified 13 GhTIR1/AFB members in G. hirsutum through bioinformatics methods and divided them into three subgroups by phylogenetic tree analysis. Motif and gene structure analysis showed that the genes in this family were highly conserved. Promoter cis-acting element analysis found that the promoters of GhTIR1/AFBs contained a large number of cis-acting elements in response to growth and development and abiotic stress. Further RT-qPCR results showed that GhTIR1/AFB genes responded to various abiotic stresses such as IAA, ABA, cold, and heat, and the expression levels of each gene changed obviously, especially Gh_D08G0763 (GhTIR1), which responded significantly to cold injury. Using VIGS (virus-induced gene silencing) technology to silence Gh_D08G0763 in the cold-tolerant cotton variety ZM36, it was found that the resistance of ZM36 to cold damage was significantly reduced. The physiological response mechanism of the Gh_D08G0763 in resisting cold damage was further analyzed through trypan blue staining of leaves and determination of enzyme activity levels. This study provided effective genetic resources for cotton cold-tolerance breeding.
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Affiliation(s)
- Xianliang Zhang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (X.Z.); (X.R.); (Y.M.); (Q.G.); (F.Z.)
- Western Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Changji 831100, China
| | - Cuicui Wu
- Institute of Cotton Research, Shanxi Agricultural University, Yuncheng 044000, China;
| | - Yutao Guo
- Institute of Plant Stress Biology, State Key Laboratory of Cotton Biology, Henan University, Kaifeng 475000, China;
| | - Xiang Ren
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (X.Z.); (X.R.); (Y.M.); (Q.G.); (F.Z.)
| | - Yongming Meng
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (X.Z.); (X.R.); (Y.M.); (Q.G.); (F.Z.)
- Western Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Changji 831100, China
| | - Qi Gao
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (X.Z.); (X.R.); (Y.M.); (Q.G.); (F.Z.)
- Western Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Changji 831100, China
| | - Fei Zhang
- State Key Laboratory of Cotton Biology, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, China; (X.Z.); (X.R.); (Y.M.); (Q.G.); (F.Z.)
| | - Yaping Wang
- Sanya Institute of Henan University, Sanya 572025, China;
| | - Jinggong Guo
- Institute of Plant Stress Biology, State Key Laboratory of Cotton Biology, Henan University, Kaifeng 475000, China;
- Sanya Institute of Henan University, Sanya 572025, China;
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Geng T, Sun Q, He J, Chen Y, Cheng W, Shen J, Liu B, Zhang M, Wang S, Asan K, Song M, Gao Q, Song Y, Liu R, Liu X, Ding Y, Jing A, Ye X, Ren H, Zeng K, Zhou Y, Zhang B, Ma S, Liu W, Liu S, Ji J. CXXC5 drove inflammation and ovarian cancer proliferation via transcriptional activation of ZNF143 and EGR1. Cell Signal 2024; 119:111180. [PMID: 38642782 DOI: 10.1016/j.cellsig.2024.111180] [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: 01/01/2024] [Revised: 03/28/2024] [Accepted: 04/14/2024] [Indexed: 04/22/2024]
Abstract
CXXC5, a zinc-finger protein, is known for its role in epigenetic regulation via binding to unmethylated CpG islands in gene promoters. As a transcription factor and epigenetic regulator, CXXC5 modulates various signaling processes and acts as a key coordinator. Altered expression or activity of CXXC5 has been linked to various pathological conditions, including tumorigenesis. Despite its known role in cancer, CXXC5's function and mechanism in ovarian cancer are unclear. We analyzed multiple public databases and found that CXXC5 is highly expressed in ovarian cancer, with high expression correlating with poor patient prognosis. We show that CXXC5 expression is regulated by oxygen concentration and is a direct target of HIF1A. CXXC5 is critical for maintaining the proliferative potential of ovarian cancer cells, with knockdown decreasing and overexpression increasing cell proliferation. Loss of CXXC5 led to inactivation of multiple inflammatory signaling pathways, while overexpression activated these pathways. Through in vitro and in vivo experiments, we confirmed ZNF143 and EGR1 as downstream transcription factors of CXXC5, mediating its proliferative potential in ovarian cancer. Our findings suggest that the CXXC5-ZNF143/EGR1 axis forms a network driving ovarian cell proliferation and tumorigenesis, and highlight CXXC5 as a potential therapeutic target for ovarian cancer treatment.
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Affiliation(s)
- Ting Geng
- Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang 222005, China
| | - Qigang Sun
- Department of Hepatobiliary and Pancreatic Surgery, Hainan General Hospital, Affiliated Hainan Hospital of Hainan Medical College, Haikou 570311, China
| | - Jingliang He
- Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang 222005, China
| | - Yulu Chen
- Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang 222005, China
| | - Wenhao Cheng
- Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang 222005, China
| | - Jing Shen
- Department of Obstetrics and Gynecology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou, Hubei, China
| | - Bin Liu
- Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang 222005, China
| | - Meiqi Zhang
- Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang 222005, China
| | - Sen Wang
- Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang 222005, China
| | - Kadirya Asan
- Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang 222005, China
| | - Mengwei Song
- Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang 222005, China
| | - Qi Gao
- Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang 222005, China
| | - Yizhuo Song
- Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang 222005, China
| | - Ruotong Liu
- Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang 222005, China
| | - Xing Liu
- Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang 222005, China
| | - Yuanyuan Ding
- Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang 222005, China
| | - Aixin Jing
- Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang 222005, China
| | - Xiaoqing Ye
- Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang 222005, China
| | - Hongyu Ren
- Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang 222005, China
| | - Kaile Zeng
- Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang 222005, China
| | - Ying Zhou
- Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang 222005, China
| | - Boyu Zhang
- Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang 222005, China
| | - Shaojie Ma
- Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang 222005, China.
| | - Wei Liu
- Cancer Center and Department of Pharmacology and Toxicology, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
| | - Shunfang Liu
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
| | - Jing Ji
- Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang 222005, China.
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Zhou X, Li R, Cheng P, Wang X, Gao Q, Zhu H. Global burden of self-harm and interpersonal violence and influencing factors study 1990-2019: analysis of the global burden of disease study. BMC Public Health 2024; 24:1035. [PMID: 38614987 PMCID: PMC11016221 DOI: 10.1186/s12889-024-18151-3] [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: 01/07/2024] [Accepted: 02/19/2024] [Indexed: 04/15/2024] Open
Abstract
INTRODUCTION Widespread concern exists in today's world regarding self-harm and interpersonal violence. This study to analyze the changes in temporal trends and spatial patterns of risk factors and burdens of self-harm and interpersonal violence using the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019. METHODS Temporal trends in self-harm and interpersonal violence were initially summarized using the estimated annual percentage change (EAPC). Data were compiled and visualized to delineate changes in disease burden and factors influencing self-harm and interpersonal violence from 1990 to 2019, stratified by gender, age and GBD region. RESULTS In 2019, the DALY rates of self-harm were 424.7(95% UI 383.25, 466.93). Over the period from 1999 to 2019, self-harm exhibited an overall decreasing trend, with the EAPC of -1.5351 (95% CI -1.6194, -1.4507), -2.0205 (95% CI -2.166, -1.8740) and -2.0605 (95% CI -2.2089, -1.9119), respectively. In contrast, the incidence rate of interpersonal violence was significantly higher than self-harm, with a rate of 413.44 (95% UI 329.88, 502.37) per 100,000 population. Mortality and DALYs of interpersonal violence were lower than those of self-harm, at 5.22 (95% UI 4.87, 5.63) and 342.43 (95% UI 316.61, 371.55). Disease burden of self-harm and interpersonal violence varied by gender, age groups and region. Specific risk factors showed that alcohol use, high temperature and drug use were the main risk factors for self-harm, while alcohol use, intimate partner violence and high temperature were associated with interpersonal violence. Low temperature was a common protective factor for both self-harm and interpersonal violence. The burden of self-harm and interpersonal violence was attributed to different factors influences in different SDI regions. CONCLUSIONS The study explored temporal trends and spatial distribution of the global disease burden of self-harm and interpersonal violence, emphasizing the significant impact of factors such as alcohol use, temperature, and drug use on disease burden. Further research and policy actions are needed to interpret recent changes of disease burden of self-harm and interpersonal violence, and dedicated efforts should be implemented to devise evidence-based interventions and policies to curtail risk factors and protect high-risk groups.
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Affiliation(s)
- Xiaoding Zhou
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, People's Republic of China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, People's Republic of China
| | - Ruyu Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, People's Republic of China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, People's Republic of China
| | - Peixia Cheng
- Department of Maternal and Child Health, School of Public Health, Capital Medical University, Beijing, People's Republic of China
- Laboratory for Gene-Environment and Reproductive Health, Laboratory for Clinical Medicine, Capital Medical University, Beijing, People's Republic of China
| | - Xiaonan Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, People's Republic of China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, People's Republic of China
| | - Qi Gao
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, People's Republic of China.
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, People's Republic of China.
| | - Huiping Zhu
- Department of Maternal and Child Health, School of Public Health, Capital Medical University, Beijing, People's Republic of China.
- Laboratory for Gene-Environment and Reproductive Health, Laboratory for Clinical Medicine, Capital Medical University, Beijing, People's Republic of China.
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Hou J, Wei W, Geng Z, Zhang Z, Yang H, Zhang X, Li L, Gao Q. Developing Plant Exosomes as an Advanced Delivery System for Cosmetic Peptide. ACS Appl Bio Mater 2024. [PMID: 38598772 DOI: 10.1021/acsabm.4c00096] [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] [Indexed: 04/12/2024]
Abstract
Peptides are a promising skincare ingredient, but due to their inherent instability and the barrier function of the skin's surface, they often have limited skin absorption and penetration, which can significantly hinder their skincare benefits. To address this, a novel technique called NanoGlow has been introduced for encapsulating peptide-based cosmetic raw materials into engineered nanosized plant-derived exosomes (pExo) to achieve the goal of a healthier and more radiant skin state. In this approach, pExo served as carriers for cosmetic peptides across the intact skin barrier, enhancing their biological effectiveness in skin beauty. The NanoGlow strategy combines chemical activation and physical proencapsulation, boasting a high success rate and straightforward and stable operation, making it suitable for large-scale production. Comprehensive analysis using in vitro cellular absorption and skin penetration models has demonstrated that the nanosized pExo carriers significantly improve peptide penetration into the skin compared to free peptides. Furthermore, in vivo tissue slice studies have shown that pExo carriers efficiently deliver acetyl hexapeptide-8 to the skin's dermis, surpassing the performance of free peptides. Cosmetic skincare effect analysis has also indicated that pExo-loaded cosmetic peptides deliver superior results. Therefore, the NanoGlow technique harnesses the natural size and properties of pExo to maximize the bioavailability of cosmetic peptides, holding great promise for developing advanced peptide delivery systems in both the cosmetic and medical drug industries.
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Affiliation(s)
- Jiali Hou
- Beijing Youngen Biotechnology Co. Ltd., Beijing 102600, China
| | - Wei Wei
- Beijing Youngen Biotechnology Co. Ltd., Beijing 102600, China
| | - Zaijun Geng
- Beijing Key Lab of Plant Resource Research and Development, Beijing Technology and Business University, Beijing 100048, China
| | - Zhenxing Zhang
- Beijing Youngen Biotechnology Co. Ltd., Beijing 102600, China
| | - Hui Yang
- Beijing Youngen Biotechnology Co. Ltd., Beijing 102600, China
| | - Xuhui Zhang
- Beijing Youngen Biotechnology Co. Ltd., Beijing 102600, China
| | - Li Li
- Beijing Key Lab of Plant Resource Research and Development, Beijing Technology and Business University, Beijing 100048, China
| | - Qi Gao
- Beijing Youngen Biotechnology Co. Ltd., Beijing 102600, China
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14
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Vermeulen BJ, Böhler A, Gao Q, Neuner A, Župa E, Chu Z, Würtz M, Jäkle U, Gruss OJ, Pfeffer S, Schiebel E. γ-TuRC asymmetry induces local protofilament mismatch at the RanGTP-stimulated microtubule minus end. EMBO J 2024:10.1038/s44318-024-00087-4. [PMID: 38600243 DOI: 10.1038/s44318-024-00087-4] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 03/15/2024] [Accepted: 03/18/2024] [Indexed: 04/12/2024] Open
Abstract
The γ-tubulin ring complex (γ-TuRC) is a structural template for de novo microtubule assembly from α/β-tubulin units. The isolated vertebrate γ-TuRC assumes an asymmetric, open structure deviating from microtubule geometry, suggesting that γ-TuRC closure may underlie regulation of microtubule nucleation. Here, we isolate native γ-TuRC-capped microtubules from Xenopus laevis egg extract nucleated through the RanGTP-induced pathway for spindle assembly and determine their cryo-EM structure. Intriguingly, the microtubule minus end-bound γ-TuRC is only partially closed and consequently, the emanating microtubule is locally misaligned with the γ-TuRC and asymmetric. In the partially closed conformation of the γ-TuRC, the actin-containing lumenal bridge is locally destabilised, suggesting lumenal bridge modulation in microtubule nucleation. The microtubule-binding protein CAMSAP2 specifically binds the minus end of γ-TuRC-capped microtubules, indicating that the asymmetric minus end structure may underlie recruitment of microtubule-modulating factors for γ-TuRC release. Collectively, we reveal a surprisingly asymmetric microtubule minus end protofilament organisation diverging from the regular microtubule structure, with direct implications for the kinetics and regulation of nucleation and subsequent modulation of microtubules during spindle assembly.
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Affiliation(s)
- Bram Ja Vermeulen
- Zentrum für Molekulare Biologie, Universität Heidelberg, DKFZ-ZMBH Allianz, Heidelberg, Germany
| | - Anna Böhler
- Zentrum für Molekulare Biologie, Universität Heidelberg, DKFZ-ZMBH Allianz, Heidelberg, Germany
| | - Qi Gao
- Zentrum für Molekulare Biologie, Universität Heidelberg, DKFZ-ZMBH Allianz, Heidelberg, Germany
| | - Annett Neuner
- Zentrum für Molekulare Biologie, Universität Heidelberg, DKFZ-ZMBH Allianz, Heidelberg, Germany
| | - Erik Župa
- Zentrum für Molekulare Biologie, Universität Heidelberg, DKFZ-ZMBH Allianz, Heidelberg, Germany
| | - Zhenzhen Chu
- Institut für Genetik, Universität Bonn, Bonn, Germany
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education, Beijing), Lymphoma Department, Peking University Cancer Hospital & Institute, Beijing, China
| | - Martin Würtz
- Zentrum für Molekulare Biologie, Universität Heidelberg, DKFZ-ZMBH Allianz, Heidelberg, Germany
| | - Ursula Jäkle
- Zentrum für Molekulare Biologie, Universität Heidelberg, DKFZ-ZMBH Allianz, Heidelberg, Germany
| | | | - Stefan Pfeffer
- Zentrum für Molekulare Biologie, Universität Heidelberg, DKFZ-ZMBH Allianz, Heidelberg, Germany.
| | - Elmar Schiebel
- Zentrum für Molekulare Biologie, Universität Heidelberg, DKFZ-ZMBH Allianz, Heidelberg, Germany.
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15
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Wang Y, Gao Q, Liu W, Bao C, Li H, Wang Y, Wang ZL, Cheng T. Wind Aggregation Enhanced Triboelectric-Electromagnetic Hybrid Generator with Slit Effect. ACS Appl Mater Interfaces 2024. [PMID: 38600737 DOI: 10.1021/acsami.4c03113] [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] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
It is of great significance to establish a low-cost, high-efficiency, self-powered micrometeorological monitoring system for agriculture, animal husbandry, and transportation. However, each additional detection element in the meteorological monitoring system increases the power consumption of the whole system by about 0.7 W. As a renewable energy technology, a triboelectric nanogenerator has the advantages of low price and self-powered sensing. To reduce the power consumption of the micrometeorological monitoring system, this work introduces an innovative solution: the wind-gathering enhanced triboelectric-electromagnetic hybrid generator (WGE-TEHG). Coupling the thin-film vibrating triboelectric nanogenerator (TENG) and electromagnetic generator (EMG), the TENG is used to monitor wind direction and the EMG is used to monitor wind speed and provide energy needed by the system. In particular, the TENG can be used as a self-powered sensor to reduce the power consumption of the sensing system. Besides, the TENG is used to produce slit effect to enhance the output performance of EMG. The experimental results show that the WGE-TEHG can build a self-powered natural environment micrometeorological sensing system. It can monitor the wind direction, wind speed, temperature, and relative humidity. This research has great application value for the self-powered sensing implementation of a hybrid TENG and EMG.
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Affiliation(s)
- Yuqi Wang
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qi Gao
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wenkai Liu
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
| | - Changcheng Bao
- The Institute of Precision Machinery and Smart Structure, College of Engineering, Zhejiang Normal University, Jinhua 321004, China
| | - Hengyu Li
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- College of Materials Science and Optoelectronic Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yingting Wang
- The Institute of Precision Machinery and Smart Structure, College of Engineering, Zhejiang Normal University, Jinhua 321004, China
| | - Zhong Lin Wang
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- Georgia Institute of Technology, Atlanta, Georgia 30332-0245, United States
- Guangzhou Institute of Blue Energy, Knowledge City, Huangpu District, Guangzhou 510555, China
| | - Tinghai Cheng
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, China
- School of Nanoscience and Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
- Guangzhou Institute of Blue Energy, Knowledge City, Huangpu District, Guangzhou 510555, China
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16
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Jin S, Gao Q, Dunn DW, Zhao H, Liang Z, Li M, Zhao Y, Chen Z, Gao G, He G, Li B, Guo S. Variation in placentophagy in golden snub-nosed monkeys (Rhinopithecus roxellana) reflects nutritional constraints. Integr Zool 2024. [PMID: 38597117 DOI: 10.1111/1749-4877.12827] [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] [Indexed: 04/11/2024]
Abstract
Golden snub-nosed monkeys show inconsistent frequency of placentophagy between wild and captive populations, with almost all births in the wild but around half of the births in captivity accompanied by the female's consumption of placenta. This aligns with nutritional demands-driven placentophagy, as captive populations are generally under less nutritional constraints for breeding females than the wild population. Placentophagy is probably adaptive in the wild and under positive selection due to nutritional benefits to both mothers and infants.
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Affiliation(s)
- Shiyu Jin
- Shaanxi Key Laboratory for Animal Conservation, School of Life Sciences, Northwest University, Xi'an, China
| | - Qi Gao
- Shaanxi Key Laboratory for Animal Conservation, School of Life Sciences, Northwest University, Xi'an, China
| | - Derek W Dunn
- Shaanxi Key Laboratory for Animal Conservation, School of Life Sciences, Northwest University, Xi'an, China
| | - Haitao Zhao
- Shaanxi Key Laboratory for Animal Conservation, Shaanxi Institute of Zoology, Xi'an, China
| | | | - Meirong Li
- Nanjing Hongshan Forest Zoo, Nanjing, China
| | - Yang Zhao
- Xi'an Qinling Wildlife Zoo, Xi'an, China
| | - Zujin Chen
- Guangzhou Zoo (Guangzhou Wildlife Research Center), Guangzhou, China
| | - Genggeng Gao
- Research Center for the Qinling Giant Panda (Shaanxi Rare Wildlife Rescue Base), Xi'an, China
| | - Gang He
- Shaanxi Key Laboratory for Animal Conservation, School of Life Sciences, Northwest University, Xi'an, China
| | - Baoguo Li
- Shaanxi Key Laboratory for Animal Conservation, School of Life Sciences, Northwest University, Xi'an, China
- Shaanxi Key Laboratory for Animal Conservation, Shaanxi Institute of Zoology, Xi'an, China
- College of Life Science, Yanan University, Yanan, China
| | - Songtao Guo
- Shaanxi Key Laboratory for Animal Conservation, School of Life Sciences, Northwest University, Xi'an, China
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17
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Gao Q, Ji Z, Wang L, Owzar K, Li QJ, Chan C, Xie J. SifiNet: A robust and accurate method to identify feature gene sets and annotate cells. bioRxiv 2024:2023.05.24.541352. [PMID: 37577619 PMCID: PMC10418061 DOI: 10.1101/2023.05.24.541352] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
SifiNet is a robust and accurate computational pipeline for identifying distinct gene sets, extracting and annotating cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Uniquely, SifiNet bypasses the cell clustering stage, commonly integrated into other cellular annotation pipelines, thereby circumventing potential inaccuracies in clustering that may compromise subsequent analyses. Consequently, SifiNet has demonstrated superior performance in multiple experimental datasets compared with other state-of-the-art methods. SifiNet can analyze both single-cell RNA and ATAC sequencing data, thereby rendering comprehensive multiomic cellular profiles. It is conveniently available as an open-source R package.
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18
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Kumar J, Lewis NE, Sherpa S, Londono D, Sun X, Gao Q, Arcila ME, Roshal M, Zhang Y, Xiao W, Chan A. Diagnostic challenges and proposed classification of myeloid neoplasms with overlapping features of thrombocytosis, ring sideroblasts and concurrent del(5q) and SF3B1 mutations. Haematologica 2024. [PMID: 38572547 DOI: 10.3324/haematol.2023.284599] [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: 11/02/2023] [Indexed: 04/05/2024] Open
Abstract
Not available.
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Affiliation(s)
- Jyoti Kumar
- Hematopathology Service, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Diagnostic Molecular Pathology Service, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Natasha E Lewis
- Hematopathology Service, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Sarina Sherpa
- Cytogenetics Laboratory, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Dory Londono
- Cytogenetics Laboratory, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Xiaotian Sun
- Hematopathology Service, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Qi Gao
- Hematopathology Service, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Maria E Arcila
- Hematopathology Service, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Diagnostic Molecular Pathology Service, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Mikhail Roshal
- Hematopathology Service, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Yanming Zhang
- Cytogenetics Laboratory, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Wenbin Xiao
- Hematopathology Service, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY.
| | - Alexander Chan
- Hematopathology Service, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY.
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19
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Arafa A, Kashima R, Shimamoto K, Kawachi H, Teramoto M, Sakai Y, Gao Q, Matsumoto C, Kokubo Y. Hypertensive disorders of pregnancy and the risk of dementia: a systematic review and meta-analysis of cohort studies. Hypertens Res 2024; 47:859-866. [PMID: 38040840 DOI: 10.1038/s41440-023-01520-7] [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: 04/20/2023] [Revised: 10/16/2023] [Accepted: 11/05/2023] [Indexed: 12/03/2023]
Abstract
This study aimed to investigate the association between hypertensive disorders of pregnancy (HDP) and subsequent risk of dementia using a systematic review and meta-analysis of cohort studies. We searched PubMed and Scopus for eligible studies that investigated the association between HDP and dementia risk. Using the random-effects model, pooled hazard ratio (HR) and 95% confidence interval (CI) of dementia risk in women with HDP were calculated. We applied the I2 statistic to measure heterogeneity across studies and the test for funnel plot asymmetry to evaluate publication bias. Six cohort studies were eligible: three from the United States, two from Sweden, and one from Denmark. When combined, HDP was associated with the risk of dementia: pooled HR (95% CI) = 1.31 (1.12, 1.53). The heterogeneity across studies was moderate (I2 = 47.3%, p-heterogeneity = 0.091), but no signs of publication bias were detected. The association of HDP with vascular dementia was stronger than that with Alzheimer's disease: pooled HRs (95% CIs) = 1.66 (1.13, 2.43) and 1.29 (0.97, 1.72), respectively. In conclusion, HDP was associated with a higher risk of dementia and this association was more prominent with vascular dementia.
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Affiliation(s)
- Ahmed Arafa
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Japan.
- Department of Public Health, Faculty of Medicine, Beni-Suef University, Beni-Suef, Egypt.
| | - Rena Kashima
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Japan
- Department of Cardiovascular Pathophysiology and Therapeutics, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Keiko Shimamoto
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Japan
- Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Haruna Kawachi
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Japan
- Department of Environmental Medicine and Population Sciences, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Masayuki Teramoto
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Yukie Sakai
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Qi Gao
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Japan
| | - Chisa Matsumoto
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Japan
- Department of Cardiology, Center for Health Surveillance and Preventive Medicine, Tokyo Medical University Hospital, Shinjuku, Japan
| | - Yoshihiro Kokubo
- Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Japan
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20
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Gao Q, Raza N, Sun D, Akmal M, Nayab F. Energy and social factor decomposition to identify drivers impeding sustainable environmental transition in emerging countries: SDGs-2030 progress assessment using LMDI analysis. Environ Sci Pollut Res Int 2024; 31:24599-24618. [PMID: 38446301 DOI: 10.1007/s11356-024-32529-1] [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: 11/14/2023] [Accepted: 02/14/2024] [Indexed: 03/07/2024]
Abstract
The balance between human growth, economic prosperity, and the consumption of hydrocarbon energy factors has become a prerequisite for environmental sustainability. However, the complexities of these factors force researchers to work for more viable combinations of such a balance. Therefore, this study attempted to determine the factors driving environmental sustainability in leading populated economies. For this purpose, the Logarithmic Mean Division Index (LMDI) utilized to decompose critical factors such as activity, economy, real density, energy intensity, and suburban effects for the period 1999-2022. Both population and its consequences (economic activity) have been found to be the leading factors behind environmental fluctuations, and energy has a negative impact on hydrocarbon forms, while contributing positively to environmental sustainability with high efficiency and low intensity. Therefore, sustainable demographic and energy transitions can be leading pathways for environmental sustainability in developing economies.
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Affiliation(s)
- Qi Gao
- School of Business Administration and Customs Affairs, Shanghai Customs College, Shanghai, 201204, China
| | - Nida Raza
- Department of Economics, Ghazi University, DG. Khan, Pakistan
| | - Dandan Sun
- Industry School of Innovation and Entrepreneurship, Shandong Institute of Commerce and Technology, Jinan, 250103, China.
| | - Muhammad Akmal
- Institute of Management Sciences, Bahauddin Zakariya University, Multan, Pakistan
| | - Faiz Nayab
- Department of Botany, Ghazi University, DG. Khan, Pakistan
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21
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Gao Q, Liu Y, Liu Y, Liu Y, Miao C, Zhang Y, Li W, Yi X. Response of plants and soils to inundation duration and construction of the plant‒soil association mode in the hydro‒fluctuation belt of the reservoir wetland. J Environ Manage 2024; 357:120776. [PMID: 38579468 DOI: 10.1016/j.jenvman.2024.120776] [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: 09/01/2023] [Revised: 12/05/2023] [Accepted: 03/26/2024] [Indexed: 04/07/2024]
Abstract
Hydro-Fluctuation Belt (HFB), a periodically exposed bank area formed by changes in water level fluctuations, is critical for damaging the reservoir wetland landscape and ecological balance. Thus, it is important to explore the mechanism of hydrological conditions on the plant-soil system of the HFB for protection of the reservoir wetland and landscape restoration. Here, we investigated the response of plant community characteristics and soil environment of the HFB of Tonghui River National Wetland Park (China), is a typical reservoir wetland, to the duration of inundation, as well as the correlation between the distribution of dominant plants and soil pH, nutrient contents, and enzyme activity by linear regression and canonical correlation analyses. The results show that as the duration of inundation decreases, the vegetation within the HFB is successional from annual or biennial herbs to perennial herbs and shrubs, with dominant plant species prominent and uneven distribution of species. Soil nutrient contents and enzyme activities of HFB decreased with increasing inundation duration. Dominant species of HFB plant community are related to soil environment, with water content, pH, urease, and available potassium being principle soil environmental factors affecting their distribution. When HFB was inundated for 0-30 days, soil pH was strongly acidic, with available potassium content above 150 mg kg-1 and higher urease activity, distributed with Arundo donax L., Polygonum perfoliatum L., Alternanthera philoxeroides (Mart.) Griseb., and Daucus carota L. communities. When inundated for 30-80 days, soil pH was acidic, with lower available potassium content (50-150 mg kg-1) and urease activity, distributed with Beckmannia syzigachne (Steud.) Fern.+ Polygonum lapathifolium L., Polygonum lapathifolium L., Medicago lupulina L. + Dysphania ambrosioides L. and Leptochloa panicea (Retz.) Ohwi communities. Using the constructed HFB plant-soil correlation model, changes in the wetland soil environment can be quickly judged by the succession of plant dominant species, which provides a simpler method for the monitoring of the soil environment in the reservoir wetland, and is of great significance for the scientific management and reasonable protection of the reservoir-type wetland ecosystem.
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Affiliation(s)
- Qi Gao
- College of Resources and Environment, Southwest University, and Key Laboratory of Ecological Environment in Three Gorges Reservoir Area, Ministry of Education, Chongqing, 400715, China
| | - Yuhang Liu
- College of Resources and Environment, Southwest University, and Key Laboratory of Ecological Environment in Three Gorges Reservoir Area, Ministry of Education, Chongqing, 400715, China
| | - Yamin Liu
- College of Resources and Environment, Southwest University, and Key Laboratory of Ecological Environment in Three Gorges Reservoir Area, Ministry of Education, Chongqing, 400715, China
| | - Yumin Liu
- College of Resources and Environment, Southwest University, and Key Laboratory of Ecological Environment in Three Gorges Reservoir Area, Ministry of Education, Chongqing, 400715, China.
| | - Conglin Miao
- College of Resources and Environment, Southwest University, and Key Laboratory of Ecological Environment in Three Gorges Reservoir Area, Ministry of Education, Chongqing, 400715, China
| | - Yulin Zhang
- College of Resources and Environment, Southwest University, and Key Laboratory of Ecological Environment in Three Gorges Reservoir Area, Ministry of Education, Chongqing, 400715, China
| | - Wei Li
- Wetland Protection and Management Center of Qijiang District, Chongqing, 404000, China
| | - Xiaotong Yi
- Wetland Protection and Management Center of Qijiang District, Chongqing, 404000, China
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22
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Nester CO, Gao Q, Wang C, Katz MJ, Lipton RB, Verghese J, Rabin LA. "Cognitive" Criteria in Older Adults With Slow Gait Speed: Implications for Motoric Cognitive Risk Syndrome. J Gerontol A Biol Sci Med Sci 2024; 79:glae038. [PMID: 38349795 PMCID: PMC10943500 DOI: 10.1093/gerona/glae038] [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] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND Motoric cognitive risk syndrome (MCR) is a predementia condition that combines slow gait speed and subjective cognitive concerns (SCC). The SCC criterion is presently unstandardized, possibly limiting risk detection. We sought to (a) characterize SCC practices through MCR literature review; (b) investigate the ability of SCC in slow gait individuals in predicting the likelihood of cognitive impairment in a demographically diverse sample of community-dwelling, nondemented older adults. METHODS First, we comprehensively reviewed the MCR literature, extracting information regarding SCC measures, items, sources, and cognitive domain. Next, Einstein Aging Study (EAS) participants (N = 278, Mage = 77.22 ± 4.74, %female = 67, Meducation = 15 ± 3.61, %non-Hispanic White = 46.3) completed gait, Clinical Dementia Rating Scale (CDR), and SCC assessment at baseline and annual follow-up (Mfollow-up = 3.5). Forty-two participants met slow gait criteria at baseline. Generalized linear mixed-effects models examined baseline SCC to predict cognitive impairment on CDR over follow-up. RESULTS We reviewed all published MCR studies (N = 106) and documented ambiguity in SCC criteria, with a prevalent approach being use of a single self-reported memory item. In EAS, high SCC endorsement on a comprehensive, validated screen significantly affected the rate of cognitive impairment (CDR; βinteraction = 0.039, p = .018) in slow gait individuals. CONCLUSIONS An assessment approach that queries across numerous SCC domains was found to predict future decline in clinical dementia status in slow gait older adults. Current SCC practices in MCR, which tend to utilize a single-memory item, may not be the optimal approach. We discuss the implications of SCC criteria validation and standardization to enhance early dementia detection in MCR.
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Affiliation(s)
- Caroline O Nester
- Department of Psychology, The Graduate Center, City University of New York, New York, New York, USA
- Department of Psychology, Queens College, City University of New York, Flushing, New York, USA
| | - Qi Gao
- Department of Epidemiology & Public Health, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Cuiling Wang
- Department of Epidemiology & Public Health, Albert Einstein College of Medicine, Bronx, New York, USA
- The Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Mindy J Katz
- The Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Richard B Lipton
- Department of Epidemiology & Public Health, Albert Einstein College of Medicine, Bronx, New York, USA
- The Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Joe Verghese
- The Saul R. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, New York, USA
- Department of Medicine (Geriatrics), Albert Einstein College of Medicine, Bronx, New York, USA
| | - Laura A Rabin
- Department of Psychology, The Graduate Center, City University of New York, New York, New York, USA
- Department of Psychology, Brooklyn College, City University of New York, Brooklyn, New York, USA
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Cao Z, Aharonian F, Axikegu, Bai YX, Bao YW, Bastieri D, Bi XJ, Bi YJ, Bian W, Bukevich AV, Cao Q, Cao WY, Cao Z, Chang J, Chang JF, Chen AM, Chen ES, Chen HX, Chen L, Chen L, Chen L, Chen MJ, Chen ML, Chen QH, Chen S, Chen SH, Chen SZ, Chen TL, Chen Y, Cheng N, Cheng YD, Cui MY, Cui SW, Cui XH, Cui YD, Dai BZ, Dai HL, Dai ZG, Danzengluobu, Dong XQ, Duan KK, Fan JH, Fan YZ, Fang J, Fang JH, Fang K, Feng CF, Feng H, Feng L, Feng SH, Feng XT, Feng Y, Feng YL, Gabici S, Gao B, Gao CD, Gao Q, Gao W, Gao WK, Ge MM, Geng LS, Giacinti G, Gong GH, Gou QB, Gu MH, Guo FL, Guo XL, Guo YQ, Guo YY, Han YA, Hasan M, He HH, He HN, He JY, He Y, Hor YK, Hou BW, Hou C, Hou X, Hu HB, Hu Q, Hu SC, Huang DH, Huang TQ, Huang WJ, Huang XT, Huang XY, Huang Y, Ji XL, Jia HY, Jia K, Jiang K, Jiang XW, Jiang ZJ, Jin M, Kang MM, Karpikov I, Kuleshov D, Kurinov K, Li BB, Li CM, Li C, Li C, Li D, Li F, Li HB, Li HC, Li J, Li J, Li K, Li SD, Li WL, Li WL, Li XR, Li X, Li YZ, Li Z, Li Z, Liang EW, Liang YF, Lin SJ, Liu B, Liu C, Liu D, Liu DB, Liu H, Liu HD, Liu J, Liu JL, Liu MY, Liu RY, Liu SM, Liu W, Liu Y, Liu YN, Luo Q, Luo Y, Lv HK, Ma BQ, Ma LL, Ma XH, Mao JR, Min Z, Mitthumsiri W, Mu HJ, Nan YC, Neronov A, Ou LJ, Pattarakijwanich P, Pei ZY, Qi JC, Qi MY, Qiao BQ, Qin JJ, Raza A, Ruffolo D, Sáiz A, Saeed M, Semikoz D, Shao L, Shchegolev O, Sheng XD, Shu FW, Song HC, Stenkin YV, Stepanov V, Su Y, Sun DX, Sun QN, Sun XN, Sun ZB, Takata J, Tam PHT, Tang QW, Tang R, Tang ZB, Tian WW, Wang C, Wang CB, Wang GW, Wang HG, Wang HH, Wang JC, Wang K, Wang K, Wang LP, Wang LY, Wang PH, Wang R, Wang W, Wang XG, Wang XY, Wang Y, Wang YD, Wang YJ, Wang ZH, Wang ZX, Wang Z, Wang Z, Wei DM, Wei JJ, Wei YJ, Wen T, Wu CY, Wu HR, Wu QW, Wu S, Wu XF, Wu YS, Xi SQ, Xia J, Xiang GM, Xiao DX, Xiao G, Xin YL, Xing Y, Xiong DR, Xiong Z, Xu DL, Xu RF, Xu RX, Xu WL, Xue L, Yan DH, Yan JZ, Yan T, Yang CW, Yang CY, Yang F, Yang FF, Yang LL, Yang MJ, Yang RZ, Yang WX, Yao YH, Yao ZG, Yin LQ, Yin N, You XH, You ZY, Yu YH, Yuan Q, Yue H, Zeng HD, Zeng TX, Zeng W, Zha M, Zhang BB, Zhang F, Zhang H, Zhang HM, Zhang HY, Zhang JL, Zhang L, Zhang PF, Zhang PP, Zhang R, Zhang SB, Zhang SR, Zhang SS, Zhang X, Zhang XP, Zhang YF, Zhang Y, Zhang Y, Zhao B, Zhao J, Zhao L, Zhao LZ, Zhao SP, Zhao XH, Zheng F, Zhong WJ, Zhou B, Zhou H, Zhou JN, Zhou M, Zhou P, Zhou R, Zhou XX, Zhou XX, Zhu BY, Zhu CG, Zhu FR, Zhu H, Zhu KJ, Zou YC, Zuo X. Measurements of All-Particle Energy Spectrum and Mean Logarithmic Mass of Cosmic Rays from 0.3 to 30 PeV with LHAASO-KM2A. Phys Rev Lett 2024; 132:131002. [PMID: 38613275 DOI: 10.1103/physrevlett.132.131002] [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: 11/13/2023] [Revised: 01/23/2024] [Accepted: 02/12/2024] [Indexed: 04/14/2024]
Abstract
We present the measurements of all-particle energy spectrum and mean logarithmic mass of cosmic rays in the energy range of 0.3-30 PeV using data collected from LHAASO-KM2A between September 2021 and December 2022, which is based on a nearly composition-independent energy reconstruction method, achieving unprecedented accuracy. Our analysis reveals the position of the knee at 3.67±0.05±0.15 PeV. Below the knee, the spectral index is found to be -2.7413±0.0004±0.0050, while above the knee, it is -3.128±0.005±0.027, with the sharpness of the transition measured with a statistical error of 2%. The mean logarithmic mass of cosmic rays is almost heavier than helium in the whole measured energy range. It decreases from 1.7 at 0.3 PeV to 1.3 at 3 PeV, representing a 24% decline following a power law with an index of -0.1200±0.0003±0.0341. This is equivalent to an increase in abundance of light components. Above the knee, the mean logarithmic mass exhibits a power law trend towards heavier components, which is reversal to the behavior observed in the all-particle energy spectrum. Additionally, the knee position and the change in power-law index are approximately the same. These findings suggest that the knee observed in the all-particle spectrum corresponds to the knee of the light component, rather than the medium-heavy components.
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Affiliation(s)
- Zhen Cao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - F Aharonian
- Dublin Institute for Advanced Studies, 31 Fitzwilliam Place, 2 Dublin, Ireland
- Max-Planck-Institut for Nuclear Physics, P.O. Box 103980, 69029 Heidelberg, Germany
| | - Axikegu
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Y X Bai
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y W Bao
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - D Bastieri
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - X J Bi
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y J Bi
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - W Bian
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - A V Bukevich
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
| | - Q Cao
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - W Y Cao
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - Zhe Cao
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - J Chang
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - J F Chang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - A M Chen
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - E S Chen
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H X Chen
- Research Center for Astronomical Computing, Zhejiang Laboratory, 311121 Hangzhou, Zhejiang, China
| | - Liang Chen
- Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 200030 Shanghai, China
| | - Lin Chen
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Long Chen
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - M J Chen
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - M L Chen
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - Q H Chen
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - S Chen
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - S H Chen
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - S Z Chen
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - T L Chen
- Key Laboratory of Cosmic Rays (Tibet University), Ministry of Education, 850000 Lhasa, Tibet, China
| | - Y Chen
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - N Cheng
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y D Cheng
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - M Y Cui
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - S W Cui
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - X H Cui
- National Astronomical Observatories, Chinese Academy of Sciences, 100101 Beijing, China
| | - Y D Cui
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - B Z Dai
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - H L Dai
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - Z G Dai
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - Danzengluobu
- Key Laboratory of Cosmic Rays (Tibet University), Ministry of Education, 850000 Lhasa, Tibet, China
| | - X Q Dong
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - K K Duan
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - J H Fan
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - Y Z Fan
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - J Fang
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - J H Fang
- Research Center for Astronomical Computing, Zhejiang Laboratory, 311121 Hangzhou, Zhejiang, China
| | - K Fang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - C F Feng
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - H Feng
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
| | - L Feng
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - S H Feng
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X T Feng
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - Y Feng
- Research Center for Astronomical Computing, Zhejiang Laboratory, 311121 Hangzhou, Zhejiang, China
| | - Y L Feng
- Key Laboratory of Cosmic Rays (Tibet University), Ministry of Education, 850000 Lhasa, Tibet, China
| | - S Gabici
- APC, Université Paris Cité, CNRS/IN2P3, CEA/IRFU, Observatoire de Paris, 119 75205 Paris, France
| | - B Gao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - C D Gao
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - Q Gao
- Key Laboratory of Cosmic Rays (Tibet University), Ministry of Education, 850000 Lhasa, Tibet, China
| | - W Gao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - W K Gao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - M M Ge
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - L S Geng
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - G Giacinti
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - G H Gong
- Department of Engineering Physics, Tsinghua University, 100084 Beijing, China
| | - Q B Gou
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - M H Gu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - F L Guo
- Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 200030 Shanghai, China
| | - X L Guo
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Y Q Guo
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y Y Guo
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Y A Han
- School of Physics and Microelectronics, Zhengzhou University, 450001 Zhengzhou, Henan, China
| | - M Hasan
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H H He
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H N He
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - J Y He
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Y He
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Y K Hor
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - B W Hou
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - C Hou
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X Hou
- Yunnan Observatories, Chinese Academy of Sciences, 650216 Kunming, Yunnan, China
| | - H B Hu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Q Hu
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - S C Hu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- China Center of Advanced Science and Technology, Beijing 100190, China
| | - D H Huang
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - T Q Huang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - W J Huang
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - X T Huang
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - X Y Huang
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Y Huang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X L Ji
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - H Y Jia
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - K Jia
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - K Jiang
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - X W Jiang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Z J Jiang
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - M Jin
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - M M Kang
- College of Physics, Sichuan University, 610065 Chengdu, Sichuan, China
| | - I Karpikov
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
| | - D Kuleshov
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
| | - K Kurinov
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
| | - B B Li
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - C M Li
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - Cheng Li
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - Cong Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - D Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - F Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - H B Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H C Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Jian Li
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - Jie Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - K Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - S D Li
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 200030 Shanghai, China
| | - W L Li
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - W L Li
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - X R Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Xin Li
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - Y Z Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Zhe Li
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Zhuo Li
- School of Physics, Peking University, 100871 Beijing, China
| | - E W Liang
- Guangxi Key Laboratory for Relativistic Astrophysics, School of Physical Science and Technology, Guangxi University, 530004 Nanning, Guangxi, China
| | - Y F Liang
- Guangxi Key Laboratory for Relativistic Astrophysics, School of Physical Science and Technology, Guangxi University, 530004 Nanning, Guangxi, China
| | - S J Lin
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - B Liu
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - C Liu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - D Liu
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - D B Liu
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - H Liu
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - H D Liu
- School of Physics and Microelectronics, Zhengzhou University, 450001 Zhengzhou, Henan, China
| | - J Liu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - J L Liu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - M Y Liu
- Key Laboratory of Cosmic Rays (Tibet University), Ministry of Education, 850000 Lhasa, Tibet, China
| | - R Y Liu
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - S M Liu
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - W Liu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y Liu
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - Y N Liu
- Department of Engineering Physics, Tsinghua University, 100084 Beijing, China
| | - Q Luo
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - Y Luo
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - H K Lv
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - B Q Ma
- School of Physics, Peking University, 100871 Beijing, China
| | - L L Ma
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X H Ma
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - J R Mao
- Yunnan Observatories, Chinese Academy of Sciences, 650216 Kunming, Yunnan, China
| | - Z Min
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - W Mitthumsiri
- Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
| | - H J Mu
- School of Physics and Microelectronics, Zhengzhou University, 450001 Zhengzhou, Henan, China
| | - Y C Nan
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - A Neronov
- APC, Université Paris Cité, CNRS/IN2P3, CEA/IRFU, Observatoire de Paris, 119 75205 Paris, France
| | - L J Ou
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - P Pattarakijwanich
- Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
| | - Z Y Pei
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - J C Qi
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - M Y Qi
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - B Q Qiao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - J J Qin
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - A Raza
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - D Ruffolo
- Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
| | - A Sáiz
- Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
| | - M Saeed
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - D Semikoz
- APC, Université Paris Cité, CNRS/IN2P3, CEA/IRFU, Observatoire de Paris, 119 75205 Paris, France
| | - L Shao
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - O Shchegolev
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
- Moscow Institute of Physics and Technology, 141700 Moscow, Russia
| | - X D Sheng
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - F W Shu
- Center for Relativistic Astrophysics and High Energy Physics, School of Physics and Materials Science and Institute of Space Science and Technology, Nanchang University, 330031 Nanchang, Jiangxi, China
| | - H C Song
- School of Physics, Peking University, 100871 Beijing, China
| | - Yu V Stenkin
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
- Moscow Institute of Physics and Technology, 141700 Moscow, Russia
| | - V Stepanov
- Institute for Nuclear Research of Russian Academy of Sciences, 117312 Moscow, Russia
| | - Y Su
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - D X Sun
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Q N Sun
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - X N Sun
- Guangxi Key Laboratory for Relativistic Astrophysics, School of Physical Science and Technology, Guangxi University, 530004 Nanning, Guangxi, China
| | - Z B Sun
- National Space Science Center, Chinese Academy of Sciences, 100190 Beijing, China
| | - J Takata
- School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - P H T Tam
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - Q W Tang
- Center for Relativistic Astrophysics and High Energy Physics, School of Physics and Materials Science and Institute of Space Science and Technology, Nanchang University, 330031 Nanchang, Jiangxi, China
| | - R Tang
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - Z B Tang
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - W W Tian
- University of Chinese Academy of Sciences, 100049 Beijing, China
- National Astronomical Observatories, Chinese Academy of Sciences, 100101 Beijing, China
| | - C Wang
- National Space Science Center, Chinese Academy of Sciences, 100190 Beijing, China
| | - C B Wang
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - G W Wang
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - H G Wang
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - H H Wang
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - J C Wang
- Yunnan Observatories, Chinese Academy of Sciences, 650216 Kunming, Yunnan, China
| | - Kai Wang
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - Kai Wang
- School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - L P Wang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - L Y Wang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - P H Wang
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - R Wang
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - W Wang
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - X G Wang
- Guangxi Key Laboratory for Relativistic Astrophysics, School of Physical Science and Technology, Guangxi University, 530004 Nanning, Guangxi, China
| | - X Y Wang
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - Y Wang
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Y D Wang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y J Wang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Z H Wang
- College of Physics, Sichuan University, 610065 Chengdu, Sichuan, China
| | - Z X Wang
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - Zhen Wang
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - Zheng Wang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - D M Wei
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - J J Wei
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Y J Wei
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - T Wen
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - C Y Wu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H R Wu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Q W Wu
- School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - S Wu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X F Wu
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Y S Wu
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - S Q Xi
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - J Xia
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - G M Xiang
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 200030 Shanghai, China
| | - D X Xiao
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - G Xiao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y L Xin
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Y Xing
- Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 200030 Shanghai, China
| | - D R Xiong
- Yunnan Observatories, Chinese Academy of Sciences, 650216 Kunming, Yunnan, China
| | - Z Xiong
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - D L Xu
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - R F Xu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - R X Xu
- School of Physics, Peking University, 100871 Beijing, China
| | - W L Xu
- College of Physics, Sichuan University, 610065 Chengdu, Sichuan, China
| | - L Xue
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - D H Yan
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - J Z Yan
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - T Yan
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - C W Yang
- College of Physics, Sichuan University, 610065 Chengdu, Sichuan, China
| | - C Y Yang
- Yunnan Observatories, Chinese Academy of Sciences, 650216 Kunming, Yunnan, China
| | - F Yang
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - F F Yang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - L L Yang
- School of Physics and Astronomy (Zhuhai) and School of Physics (Guangzhou) and Sino-French Institute of Nuclear Engineering and Technology (Zhuhai), Sun Yat-sen University, 519000 Zhuhai and 510275 Guangzhou, Guangdong, China
| | - M J Yang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - R Z Yang
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - W X Yang
- Center for Astrophysics, Guangzhou University, 510006 Guangzhou, Guangdong, China
| | - Y H Yao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Z G Yao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - L Q Yin
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - N Yin
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - X H You
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Z Y You
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y H Yu
- University of Science and Technology of China, 230026 Hefei, Anhui, China
| | - Q Yuan
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - H Yue
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H D Zeng
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - T X Zeng
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - W Zeng
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - M Zha
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - B B Zhang
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - F Zhang
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - H Zhang
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - H M Zhang
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - H Y Zhang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - J L Zhang
- National Astronomical Observatories, Chinese Academy of Sciences, 100101 Beijing, China
| | - Li Zhang
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - P F Zhang
- School of Physics and Astronomy, Yunnan University, 650091 Kunming, Yunnan, China
| | - P P Zhang
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - R Zhang
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - S B Zhang
- University of Chinese Academy of Sciences, 100049 Beijing, China
- National Astronomical Observatories, Chinese Academy of Sciences, 100101 Beijing, China
| | - S R Zhang
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - S S Zhang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X Zhang
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - X P Zhang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - Y F Zhang
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - Yi Zhang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - Yong Zhang
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - B Zhao
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - J Zhao
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - L Zhao
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - L Z Zhao
- Hebei Normal University, 050024 Shijiazhuang, Hebei, China
| | - S P Zhao
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - X H Zhao
- Yunnan Observatories, Chinese Academy of Sciences, 650216 Kunming, Yunnan, China
| | - F Zheng
- National Space Science Center, Chinese Academy of Sciences, 100190 Beijing, China
| | - W J Zhong
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - B Zhou
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - H Zhou
- Tsung-Dao Lee Institute and School of Physics and Astronomy, Shanghai Jiao Tong University, 200240 Shanghai, China
| | - J N Zhou
- Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 200030 Shanghai, China
| | - M Zhou
- Center for Relativistic Astrophysics and High Energy Physics, School of Physics and Materials Science and Institute of Space Science and Technology, Nanchang University, 330031 Nanchang, Jiangxi, China
| | - P Zhou
- School of Astronomy and Space Science, Nanjing University, 210023 Nanjing, Jiangsu, China
| | - R Zhou
- College of Physics, Sichuan University, 610065 Chengdu, Sichuan, China
| | - X X Zhou
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
| | - X X Zhou
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - B Y Zhu
- University of Science and Technology of China, 230026 Hefei, Anhui, China
- Key Laboratory of Dark Matter and Space Astronomy and Key Laboratory of Radio Astronomy, Purple Mountain Observatory, Chinese Academy of Sciences, 210023 Nanjing, Jiangsu, China
| | - C G Zhu
- Institute of Frontier and Interdisciplinary Science, Shandong University, 266237 Qingdao, Shandong, China
| | - F R Zhu
- School of Physical Science and Technology and School of Information Science and Technology, Southwest Jiaotong University, 610031 Chengdu, Sichuan, China
| | - H Zhu
- National Astronomical Observatories, Chinese Academy of Sciences, 100101 Beijing, China
| | - K J Zhu
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
- State Key Laboratory of Particle Detection and Electronics, China
| | - Y C Zou
- School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - X Zuo
- Key Laboratory of Particle Astrophysics and Experimental Physics Division and Computing Center, Institute of High Energy Physics, Chinese Academy of Sciences, 100049 Beijing, China
- Tianfu Cosmic Ray Research Center, 610000 Chengdu, Sichuan, China
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Li M, Lu C, Wang Q, Gao Q. Does prenatal diagnosis of meconium peritonitis have the better recovery? A single-center over 10 years of experience. Pediatr Surg Int 2024; 40:94. [PMID: 38551785 DOI: 10.1007/s00383-024-05682-4] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/19/2024] [Indexed: 04/02/2024]
Abstract
OBJECTIVE To evaluate whether infants with prenatal diagnosis of meconium peritonitis (MP) have a poorer prognosis. METHODS A retrospective analysis of data from infants treated with surgery from January 2008 to December 2020 was conducted. The patients were divided into prenatal diagnosis group and postnatal diagnosis group based on the timing of diagnosis. The intraoperative and postoperative parameters of the two groups of patients were compared. RESULTS A total of 71 cases of MP were included in the study, with 48 cases in the prenatal diagnosis group and 23 cases in the postnatal diagnosis group. The comparison of preoperative indicators between the two groups of patients showed no statistically significant differences in baseline (p > 0.05). Intraoperative indicators, including blood loss, anastomosis, retained intestinal tube length and excised intestinal tube length, showed no statistically significant differences between the two groups (p > 0.05). However, the postnatal diagnosis group had a significantly shorter operation time than the prenatal diagnosis group (p < 0.05). Postoperative indicators, including fasting time, albumin usage, complications, and abandonment or mortality rates, show no difference (p > 0.05). Nevertheless, the postnatal diagnosis group exhibited significantly shorter hospital stay and time to first bowel movement compared to the prenatal diagnosis group (p < 0.05). CONCLUSION Prenatal diagnosis of meconium peritonitis is associated with increased surgical complexity, prolonged hospital stay, and delayed recovery of intestinal function. However, there is no evidence of higher mortality or more complications compared to infants diagnosed postnatally, and there is no significant difference in long-term prognosis.
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Affiliation(s)
| | | | - Qi Wang
- Xi'an Children's Hospital, Xi'an, China
| | - Qi Gao
- Xi'an Children's Hospital, Xi'an, China
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Yin R, Gao Q, Fu G, Zhao Q. The causal effect of iron status on risk of anxiety disorders: A two-sample Mendelian randomization study. PLoS One 2024; 19:e0300143. [PMID: 38547239 PMCID: PMC10977787 DOI: 10.1371/journal.pone.0300143] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 02/21/2024] [Indexed: 04/02/2024] Open
Abstract
OBJECTIVES Observational studies had investigated the association of iron metabolism with anxiety disorders. The conclusions were inconsistent and not available to reveal the causal or reverse-causal association due to the confounding. In this study we estimated the potential causal effect of iron homeostasis markers on anxiety disorders using two-sample Mendelian randomization (MR) analysis. METHODS Summary data of single nucleotide polymorphisms (SNPs) associated with four iron-related biomarkers were extracted from a recent report about analysis of three genome-wide association study (GWAS), the sample size of which ranged from 131471 to 246139 individuals. The corresponding data for anxiety disorders were from Finngen database (20992 cases and 197800 controls). The analyses were mainly based on inverse variance weighted (IVW) method. In addition, the heterogeneity and pleiotropy of the results were assessed by Cochran's Q test and MR-Egger regression. RESULTS Basing on IVW method, genetically predicted serum iron level, ferritin and transferrin had negative effects on anxiety disorders. The odd ratios (OR) of anxiety disorders per 1 standard deviation (SD) unit increment in iron status biomarkers were 0.922 (95% confidence interval (CI) 0.862-0.986; p = 0.018) for serum iron level, 0.873 (95% CI 0.790-0.964; p = 0.008) for log-transformed ferritin and 0.917 (95% CI 0.867-0.969; p = 0.002) for transferrin saturation. But no statical significance was found in the association of 1 SD unit increased total iron-binding capacity (TIBC) with anxiety disorders (OR 1.080; 95% CI 0.988-1.180; p = 0.091). The analyses were supported by pleiotropy test which suggested no pleiotropic bias. CONCLUSION Our results indicated that genetically determined iron status biomarkers causally linked to the risk of anxiety disorders, providing valuable insights into the genetic research and clinical intervention of anxiety disorders.
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Affiliation(s)
- Ruiying Yin
- Department of Clinical Laboratory, The First Affiliated Hospital of Zhengzhou University, Key Clinical Laboratory of Henan Province, Zhengzhou, Henan, China
| | - Qi Gao
- Department of Clinical Laboratory, The First Affiliated Hospital of Zhengzhou University, Key Clinical Laboratory of Henan Province, Zhengzhou, Henan, China
| | - Guangzhen Fu
- Department of Clinical Laboratory, The First Affiliated Hospital of Zhengzhou University, Key Clinical Laboratory of Henan Province, Zhengzhou, Henan, China
| | - Qiang Zhao
- Department of Clinical Laboratory, The First Affiliated Hospital of Zhengzhou University, Key Clinical Laboratory of Henan Province, Zhengzhou, Henan, China
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Xu R, Gao Q, Zhang Y, Lin Y, Li Y, Su L, Zhou S, Cao Y, Gao P, Li P, Luo F, Chen R, Zhang X, Nie S, Xu X. Associations between Different Antivirals and Hospital acquired Acute Kidney Injury in Adults with Herpes Zoster. Clin J Am Soc Nephrol 2024:01277230-990000000-00367. [PMID: 38527975 DOI: 10.2215/cjn.0000000000000452] [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: 08/12/2023] [Accepted: 03/19/2024] [Indexed: 03/27/2024]
Abstract
BACKGROUND To examine the association of use of different antivirals with hospital acquired acute kidney injury (AKI) among Chinese adults with herpes zoster. METHODS The study selected 3273 adult patients who received antiviral therapy for herpes zoster during hospitalization from the China Renal Data System (CRDS). We identified and staged AKI using patient-level serum creatinine data according to the Kidney Disease Improving Global Outcomes (KDIGO) criteria. We compared the relative risks of hospital acquired AKI among patients treated with different antivirals using Cox proportional hazards models. RESULTS Among 3273 patients, 1480 (45%), 681 (21%), 489 (15%) and 623 (19%) were treated with acyclovir/valacyclovir, ganciclovir, penciclovir/famciclovir, and foscarnet, respectively. During the follow-up period, a total of 111 cases of hospital acquired AKI occurred, predominantly classified as AKI stage 1. The cumulative incidences of hospital acquired AKI were 5%, 3%, 3% and 1%, in the patients receiving acyclovir/valacyclovir, ganciclovir, penciclovir/famciclovir, and foscarnet, respectively. Compared with acyclovir/valacyclovir, penciclovir/famciclovir/ganciclovir and foscarnet were associated with a lower risk of hospital acquired AKI, with an adjusted hazard ratio [aHR] of 0.59 (95% CI, 0.37-0.94) and 0.27 (95% CI, 0.11-0.63), respectively. Compared with intravenous acyclovir, intravenous penciclovir/ganciclovir and foscarnet were associated with a lower risk of hospital acquired AKI with an aHR of 0.53 (95% CI, 0.29-0.98) and 0.31 (95% CI, 0.12-0.76), respectively. The associations were consistent across various subgroups and sensitivity analyses. CONCLUSIONS Among antiviral therapies for herpes zoster, we found different risks of hospital acquired AKI among the patients receiving different antivirals, in particular, those administered intravenously. Among intravenous antivirals, acyclovir was associated with highest risk of hospital acquired AKI, followed by penciclovir/ganciclovir and foscarnet. Confirmation studies with large samples from other populations are warranted.
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Affiliation(s)
- Ruqi Xu
- Division of Nephrology, Nanfang Hospital, Southern Medical University; National Clinical Research Center for Kidney Disease; State Key Laboratory of Organ Failure Research; Guangdong Provincial Institute of Nephrology; Guangdong Provincial Key Laboratory of Renal Failure Research, Guangzhou 510515, China
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Zhang G, Zhang J, Gao Q, Zhao Y, Lai Y. Clinical and immunologic features of co-infection in COVID-19 patients, along with potential traditional Chinese medicine treatments. Front Immunol 2024; 15:1357638. [PMID: 38576608 PMCID: PMC10991704 DOI: 10.3389/fimmu.2024.1357638] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 03/11/2024] [Indexed: 04/06/2024] Open
Abstract
Objectives With the increasing number of people worldwide infected with SARS-CoV-2, the likelihood of co-infection and/or comorbidities is rising. The impact of these co-infections on the patient's immune system remains unclear. This study aims to investigate the immunological characteristics of secondary infections in hospitalized COVID-19 patients, and preliminarily predict potential therapeutic effects of traditional Chinese medicine and their derivatives for the treatment of co-infections. Methods In this retrospective cohort study, we included 131 hospitalized patients with laboratory-confirmed COVID-19, of whom there were 64 mild and 67 severe cases. We analyzed clinical characteristics and immunologic data, including circulating immune cell numbers, levels of inflammatory factors and viral load, comparing COVID-19 patients with and without co-infection. Results Among 131 hospitalized COVID-19 patients, 41 (31.3%) were co-infection positive, with 33 (80.5%) having severe disease and 14 (34.1%) of them resulting in fatalities. Co-infected patients exhibited significantly higher severity and mortality rates compared to non-co-infected counterparts. Co-infected patients had significantly lower absolute counts of lymphocytes, total T lymphocytes, CD4+ T cells, CD8+ T cells and B lymphocytes, while levels of hs-CRP, PCT and IL-6 were significantly elevated compared to non-co-infected patients. Additionally, the viral load of co-infected patients was significantly higher than non-co-infected patients. Conclusion Co-infection emerges as a dangerous factor for COVID-19 patients, elevating the risk of severe pneumonia and mortality. Co-infection suppresses the host's immune response by reducing the number of lymphocytes and increasing inflammation, thereby diminishing the antiviral and anti-infective effects of the immune system, which promotes the severity of the disease. Therefore, it is crucial to implement infection prevention measures to minimize the spread of co-infections among COVID-19 hospitalized patients. Additionally, changes in these biomarkers provide a theoretical basis for the effective treatment of co-infections with traditional Chinese medicine.
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Affiliation(s)
- Guochao Zhang
- Department of Clinical Laboratory, Ninth Hospital of Xi’an, Xi’an, Shannxi, China
| | - Junjun Zhang
- Xianyang Center for Disease Control and Prevention, Xianyang, Shannxi, China, China
| | - Qi Gao
- Department of Clinical Laboratory, Ninth Hospital of Xi’an, Xi’an, Shannxi, China
| | - Yingying Zhao
- Department of Pathology, Fenyang College of Shanxi Medical University, Fenyang, Shanxi, China
| | - Yanjun Lai
- Department of Clinical Laboratory, Ninth Hospital of Xi’an, Xi’an, Shannxi, China
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Gao Q, Xu Y, Feng Y, Zheng X, Gong T, Kuang Q, Xiang Q, Gong L, Zhang G. Deoxycholic acid inhibits ASFV replication by inhibiting MAPK signaling pathway. Int J Biol Macromol 2024:130939. [PMID: 38493816 DOI: 10.1016/j.ijbiomac.2024.130939] [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: 12/12/2023] [Revised: 02/25/2024] [Accepted: 03/14/2024] [Indexed: 03/19/2024]
Abstract
African swine fever (ASF) is an acute, febrile, highly contagious infection of pigs caused by the African swine fever virus (ASFV). The purpose of this study is to understand the molecular mechanism of ASFV infection and evaluate the effect of DCA on MAPK pathway, so as to provide scientific basis for the development of new antiviral drugs. The transcriptome analysis found that ASFV infection up-regulated the IL-17 and MAPK signaling pathways to facilitate viral replication. Metabolome analysis showed that DCA levels were up-regulated after ASFV infection, and that exogenous DCA could inhibit activation of the MAPK pathway by ASFV infection and thus inhibit viral replication. Dual-luciferase reporter assays were used to screen the genes of ASFV and revealed that I73R could significantly up-regulate the transcription level of AP-1 transcription factor in the MAPK pathway. Confocal microscopy demonstrated that I73R could promote AP-1 entry into the nucleus, and that DCA could inhibit the I73R-mediated nuclear entry of AP-1, inhibiting MAPK pathway, and I73R interacts with AP-1. These results indicated that DCA can inhibit ASFV-mediated activation of the MAPK pathway, thus inhibiting ASFV replication. This study provides a theoretical basis for research on ASF pathogenesis and for antiviral drug development.
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Affiliation(s)
- Qi Gao
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou 510642, China; African Swine Fever Regional Laboratory of China (Guangzhou), Guangzhou 510642, China; Key Laboratory of Animal Vaccine Development, Ministry of Agriculture and Rural Affairs, Guangzhou 510000, China
| | - Yifan Xu
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou 510642, China; African Swine Fever Regional Laboratory of China (Guangzhou), Guangzhou 510642, China
| | - Yongzhi Feng
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou 510642, China; African Swine Fever Regional Laboratory of China (Guangzhou), Guangzhou 510642, China
| | - Xiaoyu Zheng
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou 510642, China; Maoming Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Maoming 525000, China
| | - Ting Gong
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou 510642, China; Maoming Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Maoming 525000, China; Key Laboratory of Animal Vaccine Development, Ministry of Agriculture and Rural Affairs, Guangzhou 510000, China
| | - Qiyuan Kuang
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou 510642, China; African Swine Fever Regional Laboratory of China (Guangzhou), Guangzhou 510642, China
| | - Qinxin Xiang
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou 510642, China; African Swine Fever Regional Laboratory of China (Guangzhou), Guangzhou 510642, China
| | - Lang Gong
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou 510642, China; African Swine Fever Regional Laboratory of China (Guangzhou), Guangzhou 510642, China; Key Laboratory of Animal Vaccine Development, Ministry of Agriculture and Rural Affairs, Guangzhou 510000, China.
| | - Guihong Zhang
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou 510642, China; African Swine Fever Regional Laboratory of China (Guangzhou), Guangzhou 510642, China; Key Laboratory of Animal Vaccine Development, Ministry of Agriculture and Rural Affairs, Guangzhou 510000, China.
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Pius AK, Toya M, Gao Q, Lee ML, Ergul YS, Chow SKH, Goodman SB. Correction: Pius et al. Effects of Aging on Osteosynthesis at Bone-Implant Interfaces. Biomolecules 2024, 14, 52. Biomolecules 2024; 14:340. [PMID: 38540803 PMCID: PMC10967888 DOI: 10.3390/biom14030340] [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: 02/01/2024] [Accepted: 02/09/2024] [Indexed: 04/12/2024] Open
Abstract
Max L. Lee was not included as an author in the original publication [...].
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Affiliation(s)
- Alexa K. Pius
- Department of Orthopaedic Surgery, School of Medicine, Stanford University, Stanford, CA 94063, USA (Y.S.E.)
| | - Masakazu Toya
- Department of Orthopaedic Surgery, School of Medicine, Stanford University, Stanford, CA 94063, USA (Y.S.E.)
| | - Qi Gao
- Department of Orthopaedic Surgery, School of Medicine, Stanford University, Stanford, CA 94063, USA (Y.S.E.)
| | - Max L. Lee
- Department of Orthopaedic Surgery, School of Medicine, Stanford University, Stanford, CA 94063, USA (Y.S.E.)
| | - Yasemin Sude Ergul
- Department of Orthopaedic Surgery, School of Medicine, Stanford University, Stanford, CA 94063, USA (Y.S.E.)
| | - Simon Kwoon-Ho Chow
- Department of Orthopaedic Surgery, School of Medicine, Stanford University, Stanford, CA 94063, USA (Y.S.E.)
| | - Stuart Barry Goodman
- Department of Orthopaedic Surgery, School of Medicine, Stanford University, Stanford, CA 94063, USA (Y.S.E.)
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
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Liang R, Liu D, Zhao JG, Gao Q, Zhai ZG. [Advances in the use of neuromuscular electrical stimulation in the prevention of venous thromboembolism]. Zhonghua Jie He He Hu Xi Za Zhi 2024; 47:269-274. [PMID: 38448181 DOI: 10.3760/cma.j.cn112147-20231017-00243] [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: 03/08/2024]
Abstract
Pharmacologic prophylaxis is the most commonly used prophylaxis for venous thromboembolism (VTE), but the pharmacologic prophylaxis is limited in patients at high risk of bleeding. Mechanical prophylaxis alone or in combination is an important method of VTE prophylaxis in patients at high risk of bleeding, but the current mainstream mechanical prophylaxis, which includes graded compression stockings, intermittent inflatable compression pumps and plantar venous compression pumps, has some limitations, leading to discomfort for patients wearing them due to the large contact area, and even affecting ability to perform daily activities. Many clinical studies have found that NMES combined with pharmacological prophylaxis has better efficacy and safety than pharmacological prophylaxis alone in preventing VTE in medical and surgical patients, and the preventive effect of NMES alone is not inferior to other mechanical prophylaxis. Besides, it also has the advantages of ease of wear and patient compliance. Currently, clinicians have limited experience and knowledge of NMES. We aimed to present the rationale, progress in clinical research and future perspective of NMES in VTE prophylaxis.
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Affiliation(s)
- R Liang
- Beijing University of Chinese Medicine China-Japan Friendship School of Clinical Medicine, Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, National Center for Respiratory Medicine Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, National Clinical Research Center for Respiratory Diseases, Beijing 100029, China
| | - D Liu
- Peking University China-Japan Friendship School of Clinical Medicine, Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, National Center for Respiratory Medicine Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, National Clinical Research Center for Respiratory Diseases, Beijing 100029, China
| | - J G Zhao
- Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, National Center for Respiratory Medicine Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, National Clinical Research Center for Respiratory Diseases, Beijing 100029, China
| | - Q Gao
- Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, National Center for Respiratory Medicine Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, National Clinical Research Center for Respiratory Diseases, Beijing 100029, China
| | - Z G Zhai
- Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, National Center for Respiratory Medicine Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, National Clinical Research Center for Respiratory Diseases, Beijing 100029, China
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Zhang Z, Leng XK, Zhai YY, Zhang X, Sun ZW, Xiao JY, Lu JF, Liu K, Xia B, Gao Q, Jia M, Xu CQ, Jiang YN, Zhang XG, Tao KS, Wu JW. Deficiency of ASGR1 promotes liver injury by increasing GP73-mediated hepatic endoplasmic reticulum stress. Nat Commun 2024; 15:1908. [PMID: 38459023 PMCID: PMC10924105 DOI: 10.1038/s41467-024-46135-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 02/13/2024] [Indexed: 03/10/2024] Open
Abstract
Liver injury is a core pathological process in the majority of liver diseases, yet the genetic factors predisposing individuals to its initiation and progression remain poorly understood. Here we show that asialoglycoprotein receptor 1 (ASGR1), a lectin specifically expressed in the liver, is downregulated in patients with liver fibrosis or cirrhosis and male mice with liver injury. ASGR1 deficiency exacerbates while its overexpression mitigates acetaminophen-induced acute and CCl4-induced chronic liver injuries in male mice. Mechanistically, ASGR1 binds to an endoplasmic reticulum stress mediator GP73 and facilitates its lysosomal degradation. ASGR1 depletion increases circulating GP73 levels and promotes the interaction between GP73 and BIP to activate endoplasmic reticulum stress, leading to liver injury. Neutralization of GP73 not only attenuates ASGR1 deficiency-induced liver injuries but also improves survival in mice received a lethal dose of acetaminophen. Collectively, these findings identify ASGR1 as a potential genetic determinant of susceptibility to liver injury and propose it as a therapeutic target for the treatment of liver injury.
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Affiliation(s)
- Zhe Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Xiang Kai Leng
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Yuan Yuan Zhai
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Xiao Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Zhi Wei Sun
- Beijing Sungen Biomedical Technology Co. Ltd, Beijing, China
| | - Jun Ying Xiao
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Jun Feng Lu
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Kun Liu
- Department of Hepatobiliary Surgery, Xi-Jing Hospital, Air Force Medical University, Xi'an, China
| | - Bo Xia
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Qi Gao
- Beijing Sungen Biomedical Technology Co. Ltd, Beijing, China
| | - Miao Jia
- Beijing Sungen Biomedical Technology Co. Ltd, Beijing, China
| | - Cheng Qi Xu
- College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Yi Na Jiang
- Department of Pathology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xiao Gang Zhang
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
| | - Kai Shan Tao
- Department of Hepatobiliary Surgery, Xi-Jing Hospital, Air Force Medical University, Xi'an, China.
| | - Jiang Wei Wu
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China.
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Han GS, Gao Q, Peng LZ, Tang J. Hessian Regularized [Formula: see text]-Nonnegative Matrix Factorization and Deep Learning for miRNA-Disease Associations Prediction. Interdiscip Sci 2024; 16:176-191. [PMID: 38099958 DOI: 10.1007/s12539-023-00594-8] [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] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 11/05/2023] [Accepted: 11/07/2023] [Indexed: 02/22/2024]
Abstract
Since the identification of microRNAs (miRNAs), empirical research has demonstrated their crucial involvement in the functioning of organisms. Investigating miRNAs significantly bolsters efforts related to averting, diagnosing, and treating intricate human maladies. Yet, exploring every conceivable miRNA-disease association consumes significant resources and time within conventional wet experiments. On the computational front, forecasting potential miRNA-disease connections serves as a valuable source of preliminary insights for medical investigators. As a result, we have developed a novel matrix factorization model known as Hessian-regularized [Formula: see text] nonnegative matrix factorization in combination with deep learning for predicting associations between miRNAs and diseases, denoted as [Formula: see text]-NMF-DF. In particular, we introduce a novel iterative fusion approach to integrate all similarities. This method effectively diminishes the sparsity of the initial miRNA-disease associations matrix. Additionally, we devise a mixed model framework that utilizes deep learning, matrix decomposition, and singular value decomposition to capture and depict the intricate nonlinear features of miRNA and disease. The prediction performance of the six matrix factorization methods is improved by comparison and analysis, similarity matrix fusion, data preprocessing, and parameter adjustment. The AUC and AUPR obtained by the new matrix factorization model under fivefold cross validation are comparative or better with other matrix factorization models. Finally, we select three diseases including lung tumor, bladder tumor and breast tumor for case analysis, and further extend the matrix factorization model based on deep learning. The results show that the hybrid algorithm combining matrix factorization with deep learning proposed in this paper can predict miRNAs related to different diseases with high accuracy.
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Affiliation(s)
- Guo-Sheng Han
- Department of Mathematics and Computational Science, Xiangtan University, Xiangtan, 411105, China.
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, 411105, China.
| | - Qi Gao
- Department of Mathematics and Computational Science, Xiangtan University, Xiangtan, 411105, China
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, 411105, China
| | - Ling-Zhi Peng
- Department of Mathematics and Computational Science, Xiangtan University, Xiangtan, 411105, China
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, 411105, China
| | - Jing Tang
- Department of Mathematics and Computational Science, Xiangtan University, Xiangtan, 411105, China
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, 411105, China
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Wang F, Yu L, Ren Y, Zhang Q, He S, Zhao M, He Z, Gao Q, Chen J. An optimized culturomics strategy for isolation of human milk microbiota. Front Microbiol 2024; 15:1272062. [PMID: 38495514 PMCID: PMC10940525 DOI: 10.3389/fmicb.2024.1272062] [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: 08/03/2023] [Accepted: 02/05/2024] [Indexed: 03/19/2024] Open
Abstract
Viable microorganisms and a diverse microbial ecosystem found in human milk play a crucial role in promoting healthy immune system and shaping the microbial community in the infant's gut. Culturomics is a method to obtain a comprehensive repertoire of human milk microbiota. However, culturomics is an onerous procedure, and needs expertise, making it difficult to be widely implemented. Currently, there is no efficient and feasible culturomics method specifically designed for human milk microbiota yet. Therefore, the aim of this study was to develop a more efficient and feasible culturomics method specifically designed for human milk microbiota. We obtained fresh samples of human milk from healthy Chinese mothers and conducted a 27-day enrichment process using blood culture bottles. Bacterial isolates were harvested at different time intervals and cultured on four different types of media. Using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis, we identified a total of 6601 colonies and successfully obtained 865 strains, representing 4 phyla, 21 genera, and 54 species. By combining CBA and MRS media, we were able to cultivate over 94.4% of bacterial species with high diversity, including species-specific microorganisms. Prolonged pre-incubation in blood culture bottles significantly increased the number of bacterial species by about 33% and improved the isolation efficiency of beneficial bacteria with low abundance in human milk. After optimization, we reduced the pre-incubation time in blood culture bottles and selected optimal picking time-points (0, 3, and 6 days) at 37°C. By testing 6601 colonies using MALDI-TOF MS, we estimated that this new protocol could obtain more than 90% of bacterial species, reducing the workload by 57.0%. In conclusion, our new culturomics strategy, which involves the combination of CBA and MRS media, extended pre-incubation enrichment, and optimized picking time-points, is a feasible method for studying the human milk microbiota. This protocol significantly improves the efficiency of culturomics and allows for the establishment of a comprehensive repertoire of bacterial species and strains in human milk.
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Affiliation(s)
- Fan Wang
- Beijing YuGen Pharmaceutical Co., Ltd., Beijing, China
| | - Lingmin Yu
- YingTan City people’s Hospital, Yingtan, China
| | - Yuting Ren
- Beijing YuGen Pharmaceutical Co., Ltd., Beijing, China
| | - Qianwen Zhang
- Beijing YuGen Pharmaceutical Co., Ltd., Beijing, China
| | - Shanshan He
- Beijing YuGen Pharmaceutical Co., Ltd., Beijing, China
| | - Minlei Zhao
- Beijing YuGen Pharmaceutical Co., Ltd., Beijing, China
| | - Zhili He
- Beijing YuGen Pharmaceutical Co., Ltd., Beijing, China
| | - Qi Gao
- Beijing Hotgen Biotechnology Inc., Beijing, China
| | - Jianguo Chen
- Beijing YuGen Pharmaceutical Co., Ltd., Beijing, China
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Chen X, Liang Y, Weng Z, Hu C, Peng Y, Sun Y, Gao Q, Huang Z, Tang S, Gong L, Zhang G. ALIX and TSG101 are essential for cellular entry and replication of two porcine alphacoronaviruses. PLoS Pathog 2024; 20:e1012103. [PMID: 38489378 PMCID: PMC10971774 DOI: 10.1371/journal.ppat.1012103] [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: 05/23/2023] [Revised: 03/27/2024] [Accepted: 03/06/2024] [Indexed: 03/17/2024] Open
Abstract
Alphacoronaviruses are the primary coronaviruses responsible for causing severe economic losses in the pig industry with the potential to cause human outbreaks. Currently, extensive studies have reported the essential role of endosomal sorting and transport complexes (ESCRT) in the life cycle of enveloped viruses. However, very little information is available about which ESCRT components are crucial for alphacoronaviruses infection. By using RNA interference in combination with Co-immunoprecipitation, as well as fluorescence and electron microscopy approaches, we have dissected the role of ALIX and TSG101 for two porcine alphacoronavirus cellular entry and replication. Results show that infection by two porcine alphacoronaviruses, including porcine epidemic diarrhea virus (PEDV) and porcine enteric alphacoronavirus (PEAV), is dramatically decreased in ALIX- or TSG101-depleted cells. Furthermore, PEDV entry significantly increases the interaction of ALIX with caveolin-1 (CAV1) and RAB7, which are crucial for viral endocytosis and lysosomal transport, however, does not require TSG101. Interestingly, PEAV not only relies on ALIX to regulate viral endocytosis and lysosomal transport, but also requires TSG101 to regulate macropinocytosis. Besides, ALIX and TSG101 are recruited to the replication sites of PEDV and PEAV where they become localized within the endoplasmic reticulum and virus-induced double-membrane vesicles. PEDV and PEAV replication were significantly inhibited by depletion of ALIX and TSG101 in Vero cells or primary jejunal epithelial cells, indicating that ALIX and TSG101 are crucial for PEDV and PEAV replication. Collectively, these data highlight the dual role of ALIX and TSG101 in the entry and replication of two porcine alphacoronaviruses. Thus, ESCRT proteins could serve as therapeutic targets against two porcine alphacoronaviruses infection.
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Affiliation(s)
- Xiongnan Chen
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Utilization and Conservation of Food and Medicinal Resources in Northern Region, Shaoguan University, Shaoguan, China
- Maoming Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Guangdong, China
| | - Yifan Liang
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
- Maoming Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Guangdong, China
| | - Zhijun Weng
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
- Maoming Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Guangdong, China
| | - Chen Hu
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
| | - Yunzhao Peng
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
- Maoming Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Guangdong, China
| | - Yingshuo Sun
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
- Maoming Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Guangdong, China
| | - Qi Gao
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
- Maoming Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Guangdong, China
| | - Zhao Huang
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Utilization and Conservation of Food and Medicinal Resources in Northern Region, Shaoguan University, Shaoguan, China
- Maoming Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Guangdong, China
| | - Shengqiu Tang
- Guangdong Provincial Key Laboratory of Utilization and Conservation of Food and Medicinal Resources in Northern Region, Shaoguan University, Shaoguan, China
| | - Lang Gong
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
- Maoming Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Guangdong, China
- Key Laboratory of Animal Vaccine Development, Ministry of Agriculture and Rural Affairs, China
| | - Guihong Zhang
- Guangdong Provincial Key Laboratory of Zoonosis Prevention and Control, College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
- Maoming Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Guangdong, China
- Key Laboratory of Animal Vaccine Development, Ministry of Agriculture and Rural Affairs, China
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Liu Y, Ho C, Yu W, Huang Y, Miller J, Gao Q, Syed M, Ma Y, Wang M, Maciag L, Petrova-Drus K, Zhu M, Yao J, Vanderbilt C, Durham B, Benhamida J, Ewalt MD, Dogan A, Roshal M, Nafa K, Arcila ME. Quantification of Measurable Residual Disease Detection by Next-Generation Sequencing-Based Clonality Testing in B-Cell and Plasma Cell Neoplasms. J Mol Diagn 2024; 26:168-178. [PMID: 38103591 PMCID: PMC10918645 DOI: 10.1016/j.jmoldx.2023.11.009] [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: 07/04/2023] [Revised: 10/27/2023] [Accepted: 11/09/2023] [Indexed: 12/19/2023] Open
Abstract
Next-generation sequencing (NGS)-based measurable residual disease (MRD) monitoring in post-treatment settings can be crucial for relapse risk stratification in patients with B-cell and plasma cell neoplasms. Prior studies have focused on validation of various technical aspects of the MRD assays, but more studies are warranted to establish the performance characteristics and enable standardization and broad utilization in routine clinical practice. Here, the authors describe an NGS-based IGH MRD quantification assay, incorporating a spike-in calibrator for monitoring B-cell and plasma cell neoplasms based on their unique IGH rearrangement status. Comparison of MRD status (positive or undetectable) by NGS and flow cytometry (FC) assays showed high concordance (91%, 471/519 cases) and overall good linear correlation in MRD quantitation, particularly for chronic lymphocytic leukemia and B-lymphoblastic leukemia/lymphoma (R = 0.85). Quantitative correlation was lower for plasma cell neoplasms, where underestimation by FC is a known limitation. No significant effects on sequencing efficiency by the spike-in calibrator were observed, with excellent inter- and intra-assay reproducibility within the authors' laboratory, and in comparison to an external laboratory, using the same assay and protocols. Assays performed both at internal and external laboratories showed highly concordant MRD detection (100%) and quantitation (R = 0.97). Overall, this NGS-based MRD assay showed highly reproducible results with quantitation that correlated well with FC MRD assessment, particularly for B-cell neoplasms.
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Affiliation(s)
- Ying Liu
- Diagnostic Molecular Pathology, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York; Hematopathology Service, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.
| | - Caleb Ho
- Loxo Oncology, Inc., Stamford, Connecticut
| | - Wayne Yu
- Diagnostic Molecular Pathology, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Ying Huang
- Invivoscribe, Inc., San Diego, California
| | | | - Qi Gao
- Hematopathology Service, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Mustafa Syed
- Diagnostic Molecular Pathology, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Yuanyuan Ma
- Diagnostic Molecular Pathology, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Meiyi Wang
- Diagnostic Molecular Pathology, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Lidia Maciag
- Diagnostic Molecular Pathology, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Kseniya Petrova-Drus
- Diagnostic Molecular Pathology, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York; Hematopathology Service, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Menglei Zhu
- Diagnostic Molecular Pathology, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York; Hematopathology Service, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - JinJuan Yao
- Diagnostic Molecular Pathology, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Chad Vanderbilt
- Diagnostic Molecular Pathology, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Benjamin Durham
- Diagnostic Molecular Pathology, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Jamal Benhamida
- Diagnostic Molecular Pathology, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Mark D Ewalt
- Diagnostic Molecular Pathology, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York; Hematopathology Service, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Ahmet Dogan
- Hematopathology Service, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Mikhail Roshal
- Hematopathology Service, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Khedoudja Nafa
- Diagnostic Molecular Pathology, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Maria E Arcila
- Diagnostic Molecular Pathology, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York; Hematopathology Service, Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.
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Chan A, Auclair R, Gao Q, Ghione P, Horwitz S, Dogan A, Roshal M, Lin O. Role of flow cytometric immunophenotyping in the diagnosis of breast implant-associated anaplastic large cell lymphoma: A 6-year, single-institution experience. Cytometry B Clin Cytom 2024; 106:117-125. [PMID: 38297808 PMCID: PMC10978229 DOI: 10.1002/cyto.b.22162] [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] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/05/2024] [Accepted: 01/18/2024] [Indexed: 02/02/2024]
Abstract
Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) is an uncommon mature T-cell neoplasm occurring in patients with textured breast implants, typically after 7-10 years of exposure. Although cytopathologic or histopathologic assessment is considered the gold standard diagnostic method for BIA-ALCL, flow cytometry (FC)-based immunophenotyping is recommended as an adjunct test. However, the diagnostic efficacy of FC is not well reported. We reviewed 290 FC tests from breast implant pericapsular fluid and capsule tissue from 182 patients, including 16 patients with BIA-ALCL over a 6-year period, calculating diagnostic rates and test efficacy. FC showed an overall sensitivity of 75.9%, specificity of 100%, and negative and positive predictive values of 95.4% and 100%, respectively. Blinded expert review of false-negative cases identified diagnostic pitfalls, improving sensitivity to 96.6%. Fluid samples had better rates of adequate samples for FC testing compared with tissue samples. Paired with FC testing of operating room (OR)-acquired fluid samples, capsulectomy FC specimens added no diagnostic value in patients with concurrent fluid samples; no cases had positive capsule FC with negative fluid FC. Fluid samples are adequate for FC testing more often than tissue. Capsule tissue FC specimens do not improve FC efficacy when paired with OR-acquired fluid FC samples and are often inadequate samples. FC is 100% specific for BIA-ALCL and can serve as a confirmatory test but should not be the sole diagnostic method. Awareness of sample-specific diagnostic pitfalls greatly improves the sensitivity of BIA-ALCL testing by FC.
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Affiliation(s)
- Alexander Chan
- Department of Pathology, Hematopathology Service, Memorial Sloan Kettering Cancer Center
| | - Romany Auclair
- Department of Pathology, Hematopathology Service, Memorial Sloan Kettering Cancer Center
| | - Qi Gao
- Department of Pathology, Hematopathology Service, Memorial Sloan Kettering Cancer Center
| | - Paola Ghione
- Department of Medicine, Lymphoma Service, Memorial Sloan Kettering Cancer Center
| | - Steven Horwitz
- Department of Medicine, Lymphoma Service, Memorial Sloan Kettering Cancer Center
| | - Ahmet Dogan
- Department of Pathology, Hematopathology Service, Memorial Sloan Kettering Cancer Center
| | - Mikhail Roshal
- Department of Pathology, Hematopathology Service, Memorial Sloan Kettering Cancer Center
| | - Oscar Lin
- Department of Pathology, Hematopathology Service, Memorial Sloan Kettering Cancer Center
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Black WC, Abdoli A, An X, Auger A, Beaulieu P, Bernatchez M, Caron C, Chefson A, Crane S, Diallo M, Dorich S, Fader LD, Ferraro GB, Fournier S, Gao Q, Ginzburg Y, Hamel M, Han Y, Jones P, Lanoix S, Lacbay CM, Leclaire ME, Levy M, Mamane Y, Mulani A, Papp R, Pellerin C, Picard A, Skeldon A, Skorey K, Stocco R, St-Onge M, Truchon JF, Truong VL, Zimmermann M, Zinda M, Roulston A. Discovery of the Potent and Selective ATR Inhibitor Camonsertib (RP-3500). J Med Chem 2024; 67:2349-2368. [PMID: 38299539 DOI: 10.1021/acs.jmedchem.3c01917] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2024]
Abstract
ATR is a key kinase in the DNA-damage response (DDR) that is synthetic lethal with several other DDR proteins, making it an attractive target for the treatment of genetically selected solid tumors. Herein we describe the discovery of a novel ATR inhibitor guided by a pharmacophore model to position a key hydrogen bond. Optimization was driven by potency and selectivity over the related kinase mTOR, resulting in the identification of camonsertib (RP-3500) with high potency and excellent ADME properties. Preclinical evaluation focused on the impact of camonsertib on myelosuppression, and an exploration of intermittent dosing schedules to allow recovery of the erythroid compartment and mitigate anemia. Camonsertib is currently undergoing clinical evaluation both as a single agent and in combination with talazoparib, olaparib, niraparib, lunresertib, or gemcitabine (NCT04497116, NCT04972110, NCT04855656). A preliminary recommended phase 2 dose for monotherapy was identified as 160 mg QD given 3 days/week.
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Affiliation(s)
- W Cameron Black
- Repare Therapeutics, Inc., 7171 Frederick-Banting, Building 2, Saint-Laurent, Quebec H4S 1Z9, Canada
| | - Abbas Abdoli
- Nuchem Sciences, Inc., 2350 Rue Cohen, Suite 201, Saint-Laurent, Quebec H4R 2N6, Canada
| | - Xiuli An
- New York Blood Center Enterprises, New York, New York 10065, United States
| | - Anick Auger
- Ventus Therapeutics, Inc., 7150 Frederick-Banting, Saint-Laurent, Quebec H4S 2A1, Canada
| | | | | | - Cathy Caron
- Repare Therapeutics, Inc., 7171 Frederick-Banting, Building 2, Saint-Laurent, Quebec H4S 1Z9, Canada
| | - Amandine Chefson
- Ventus Therapeutics, Inc., 7150 Frederick-Banting, Saint-Laurent, Quebec H4S 2A1, Canada
| | - Sheldon Crane
- Nuchem Sciences, Inc., 2350 Rue Cohen, Suite 201, Saint-Laurent, Quebec H4R 2N6, Canada
| | - Mohamed Diallo
- Repare Therapeutics, Inc., 7171 Frederick-Banting, Building 2, Saint-Laurent, Quebec H4S 1Z9, Canada
| | - Stéphane Dorich
- Ventus Therapeutics, Inc., 7150 Frederick-Banting, Saint-Laurent, Quebec H4S 2A1, Canada
| | - Lee D Fader
- Ventus Therapeutics, Inc., 7150 Frederick-Banting, Saint-Laurent, Quebec H4S 2A1, Canada
| | - Gino B Ferraro
- Repare Therapeutics, Inc., 7171 Frederick-Banting, Building 2, Saint-Laurent, Quebec H4S 1Z9, Canada
| | - Sara Fournier
- Repare Therapeutics, Inc., 7171 Frederick-Banting, Building 2, Saint-Laurent, Quebec H4S 1Z9, Canada
| | - Qi Gao
- J-Star Research, Inc., 3001 Hadley Road, Suites 1-5A, South Plainfield, New Jersey 07080, United States
| | - Yelena Ginzburg
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
| | - Martine Hamel
- Repare Therapeutics, Inc., 7171 Frederick-Banting, Building 2, Saint-Laurent, Quebec H4S 1Z9, Canada
| | - Yongshuai Han
- New York Blood Center Enterprises, New York, New York 10065, United States
| | - Paul Jones
- Nuchem Sciences, Inc., 2350 Rue Cohen, Suite 201, Saint-Laurent, Quebec H4R 2N6, Canada
| | - Stéphanie Lanoix
- Nuchem Sciences, Inc., 2350 Rue Cohen, Suite 201, Saint-Laurent, Quebec H4R 2N6, Canada
| | - Cyrus M Lacbay
- Nuchem Sciences, Inc., 2350 Rue Cohen, Suite 201, Saint-Laurent, Quebec H4R 2N6, Canada
| | - Marie-Eve Leclaire
- Repare Therapeutics, Inc., 7171 Frederick-Banting, Building 2, Saint-Laurent, Quebec H4S 1Z9, Canada
| | - Maayan Levy
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
| | - Yael Mamane
- Repare Therapeutics, Inc., 7171 Frederick-Banting, Building 2, Saint-Laurent, Quebec H4S 1Z9, Canada
| | - Amina Mulani
- Nuchem Sciences, Inc., 2350 Rue Cohen, Suite 201, Saint-Laurent, Quebec H4R 2N6, Canada
| | - Robert Papp
- Repare Therapeutics, Inc., 7171 Frederick-Banting, Building 2, Saint-Laurent, Quebec H4S 1Z9, Canada
| | - Charles Pellerin
- Ventus Therapeutics, Inc., 7150 Frederick-Banting, Saint-Laurent, Quebec H4S 2A1, Canada
| | - Audrey Picard
- Ventus Therapeutics, Inc., 7150 Frederick-Banting, Saint-Laurent, Quebec H4S 2A1, Canada
| | - Alexander Skeldon
- Ventus Therapeutics, Inc., 7150 Frederick-Banting, Saint-Laurent, Quebec H4S 2A1, Canada
| | - Kathryn Skorey
- Nuchem Sciences, Inc., 2350 Rue Cohen, Suite 201, Saint-Laurent, Quebec H4R 2N6, Canada
| | - Rino Stocco
- Repare Therapeutics, Inc., 7171 Frederick-Banting, Building 2, Saint-Laurent, Quebec H4S 1Z9, Canada
| | - Miguel St-Onge
- Ventus Therapeutics, Inc., 7150 Frederick-Banting, Saint-Laurent, Quebec H4S 2A1, Canada
| | - Jean-François Truchon
- Repare Therapeutics, Inc., 7171 Frederick-Banting, Building 2, Saint-Laurent, Quebec H4S 1Z9, Canada
| | - Vouy Linh Truong
- Nuchem Sciences, Inc., 2350 Rue Cohen, Suite 201, Saint-Laurent, Quebec H4R 2N6, Canada
| | - Michal Zimmermann
- Repare Therapeutics, Inc., 7171 Frederick-Banting, Building 2, Saint-Laurent, Quebec H4S 1Z9, Canada
| | - Michael Zinda
- Repare Therapeutics, Inc., 7171 Frederick-Banting, Building 2, Saint-Laurent, Quebec H4S 1Z9, Canada
| | - Anne Roulston
- Repare Therapeutics, Inc., 7171 Frederick-Banting, Building 2, Saint-Laurent, Quebec H4S 1Z9, Canada
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Liu Z, Wang H, Lin S, Guo J, Shi YP, Gao Q, Zhou H. First Report of Colletotrichum fructicola Causing Leaf Spot on Smilax glabra Roxb in China. Plant Dis 2024. [PMID: 38386302 DOI: 10.1094/pdis-09-23-1933-pdn] [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] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Smilax glabra Roxb is a medicinal plant distributed in 17 countries and used in the production of food and tea (Wu et al. 2022). In May 2021, a leaf spot disease was observed on ~60% of S. glabra plants in a field (∼0.4 ha) in Qinzhou City, Guangxi Province. Initially, small, circular, brown spots appeared on the leaf surfaces, which then gradually expanded into large, sunken, dark brown necrotic areas. As disease progressed, lesions merged into large spots, eventually leading to defoliation. To determine the causal agent, six symptomatic plants were collected from the field. Small pieces (∼5 mm2) were cut from the infected leaves (n = 12), sterilized for two min in 1% NaOCl, and rinsed three times in sterile water. Then, the leaf tissues were placed on potato dextrose agar (PDA) with chloramphenicol (0.1 g/liter) and incubated for 3 days at 28°C (12-h photoperiod). Pure cultures were obtained by transferring hyphal tips from recently germinated spores or colony edges onto PDA. Among the 17 isolates, 15 exhibited similar morphologies. Two single-spore isolates (TFL45.1 and TFL46.2) were subjected to further morphological and molecular characterization. Colonies on PDA were grayish green with a white outer ring and cottony surface, and pale blackish green on the reverse side. Conidia were hyaline, aseptate, straight, and cylindrical, with rounded ends, and 11.4 to 16.5 μm × 4.1 to 6.1 μm (average 13.9 × 4.8 μm, n = 100). Appressoria were brown to dark brown, with a smooth edge and different shapes such as ovoid, elliptical or irregular, and 6.8 to 8.9 μm × 5.9 to 7.8 μm (average 7.7 × 6.6 μm, n = 25). For molecular identification, eight target gene sequences, internal transcribed spacer (ITS), glyceraldehydes-3-phosphate dehydrogenase (GAPHD), calmodulin (CAL), partial actin (ACT), chitin synthase (CHS-1), glutamine synthetase (GS), manganese superoxide dismutase (SOD2), and β-tubulin (TUB) were selected for PCR amplification (Weir et al. 2012). The resulting sequences were deposited in GenBank (OR399160-61 and OR432537-50). BLASTn analysis of the obtained sequences showed 99-100% identity with those of the ex-type strain C. fructicola ICMP:18581 (JX010165, JX010033, FJ917508, FJ907426, JX009866, JX010095, JX010327, JX010405) (Weir et al. 2012). In addition, a phylogenetic analysis confirmed the isolates as C. fructicola. Therefore, based on morphological and molecular characteristics (Park et al. 2018; Weir et al. 2012), the isolates were identified as C. fructicola. To verify pathogenicity, three healthy leaves on each of six two-year-old S. glabra plants were inoculated with ∼5 mm2 mycelial discs or aliquots of 10 μl suspension (106 conidia/ml) of the strain TFL46.2, and six control plants were inoculated with sterile PDA discs or sterile water. All plants were enclosed in plastic bags and incubated in a greenhouse at 25°C (12-h photoperiod). Six days post-inoculation, leaf spot symptoms appeared on the inoculated leaves. No symptoms were detected in the controls. Experiments were replicated three times with similar results. To fulfill Koch's postulates, C. fructicola was consistently re-isolated from symptomatic tissue and confirmed by morphology and sequencing of the eight genes, whereas no fungus was isolated from the control plants. To our knowledge, this is the first report of C. fructicola causing leaf spot disease on S. glabra. Further studies will be needed to develop strategies against this disease based on the identification of this pathogen.
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Affiliation(s)
- Ze Liu
- Guangxi Minzu University, 47874, Guangxi Key Laboratory for Polysaccharide Materials and Modifications, Nanning, Guangxi, China;
| | - Hanyi Wang
- Guangxi Minzu University, 47874, Guangxi Key Laboratory for Polysaccharide Materials and Modifications, Guangxi University for Nationalities, Nanning, Guangxi, China;
| | - Siyu Lin
- Guangxi Minzu University, 47874, Guangxi Key Laboratory for Polysaccharide Materials and Modifications, Nanning, Guangxi, China;
| | - Jingyi Guo
- Guangxi Minzu University, 47874, Guangxi Key Laboratory for Polysaccharide Materials and Modifications, Guangxi Minzu University, Nanning, Guangxi, China;
| | - Yun Ping Shi
- Biotechnology Research Institute, Guangxi Academy of Agricultural Sciences, Nanning, China;
| | - Qi Gao
- Guangxi Minzu University, 47874, Guangxi Key Laboratory for Polysaccharide Materials and Modifications, Nanning, Guangxi, China;
| | - Hao Zhou
- Guangxi Minzu University, 47874, Guangxi Key Laboratory for Polysaccharide Materials and Modifications, Nanning, Guangxi, China
- Guangxi Minzu University, 47874, Key Laboratory of Protection and Utilization of Marine Resources, Nanning, Guangxi, China;
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Gao Q, Young DK, Pan Z. Oblatopyrochroabellula, an enigmatic new genus and species of Pyrochroinae (Coleoptera, Pyrochroidae) from Xizang, China. Zookeys 2024; 1191:369-377. [PMID: 38405675 PMCID: PMC10892146 DOI: 10.3897/zookeys.1191.118653] [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: 01/12/2024] [Accepted: 01/29/2024] [Indexed: 02/27/2024] Open
Abstract
Oblatopyrochroabellula, a new genus and species of Pyrochroinae Latreille, 1807 from Xizang, China, is described and illustrated. The antennae, cranial apparatus, and genitalia of the new genus form a truly unique set of characters not observed in any other pyrochroid genus. The taxonomic position and phylogenetic relationships of Oblatopyrochroagen. nov. are also discussed but appear difficult to resolve.
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Affiliation(s)
- Qi Gao
- Key Laboratory of Zoological Systematics and Application, School of Life Sciences, Hebei University, 071002, Baoding, Hebei Province, ChinaHebei UniversityBaodingChina
| | - Daniel K. Young
- Department of Entomology, University of Wisconsin, Madison, WI 53706, USAUniversity of WisconsinMadisonUnited States of America
| | - Zhao Pan
- Key Laboratory of Zoological Systematics and Application, School of Life Sciences, Hebei University, 071002, Baoding, Hebei Province, ChinaHebei UniversityBaodingChina
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Hou X, Qian J, Cai J, Su W, Ruan B, Gao Q. Using clinician-oriented and laboratory-oriented assessments to study dynamic stability of individuals with chronic ankle instability. iScience 2024; 27:108842. [PMID: 38327777 PMCID: PMC10847673 DOI: 10.1016/j.isci.2024.108842] [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] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 10/08/2023] [Accepted: 01/03/2024] [Indexed: 02/09/2024] Open
Abstract
To compare the dynamic stability of lower extremities between Copers and individuals with chronic ankle instability (CAI) using clinician-oriented assessments (Y-balance test, YBT) and laboratory-oriented assessments (time to stabilization, TTS). 90 participants (Copers, 45; CAIs, 45) were recruited and measured by YBT and TTS to evaluate dynamic stability. The difference of dynamic stability between Copers and CAIs was examined using a two-factor MANOVA. Only for females in anterior direction, YBT scores for the AS side of Copers were significantly higher than that of CAIs. For males, the TTS of CAIs was significantly shorter than that of Copers in the anterior, lateral, and medial direction separately. For females, the TTS of CAIs is also significantly shorter than that of Copers in the anterior, lateral, and medial direction separately. There are opposite results when evaluating the dynamic stability difference between Copers and CAIs using YBT and TTS.
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Affiliation(s)
- Xiao Hou
- School of Sport Science, Beijing Sport University, Beijing, China
- Exercise Rehabilitation Science Laboratory, Beijing Sport University, Beijing, China
| | - Jinghua Qian
- Exercise Rehabilitation Science Laboratory, Beijing Sport University, Beijing, China
- School of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing, China
| | - Jingxian Cai
- Exercise Rehabilitation Science Laboratory, Beijing Sport University, Beijing, China
- School of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing, China
| | - Wanrongyu Su
- Exercise Rehabilitation Science Laboratory, Beijing Sport University, Beijing, China
- School of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing, China
| | - Bing Ruan
- Exercise Rehabilitation Science Laboratory, Beijing Sport University, Beijing, China
- School of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing, China
| | - Qi Gao
- Exercise Rehabilitation Science Laboratory, Beijing Sport University, Beijing, China
- School of Sport Medicine and Rehabilitation, Beijing Sport University, Beijing, China
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Zhang Y, Tang H, Zi M, Zhang Z, Gao Q, Tian S. CCDC71L as a novel prognostic marker and immunotherapy target via lipid metabolism in head and neck squamous cell carcinoma. J Stomatol Oral Maxillofac Surg 2024; 125:101799. [PMID: 38367702 DOI: 10.1016/j.jormas.2024.101799] [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] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 02/11/2024] [Accepted: 02/15/2024] [Indexed: 02/19/2024]
Abstract
BACKGROUND Head and neck squamous cell carcinoma (HNSCC) is the sixth most widespread cancer globally with high rate and poor prognosis. Coiled-coil domain containing 71 like (CCDC71L) exerts an important role in cellular lipid metabolic process. However, its function in HNSCC remains unclear. To this end, we examined the CCDC71L implications for prognosis and tumor microenvironment in HNSCC. MATERIALS AND METHODS First, CCDC71L expression was explored through The Cancer Genome Atlas (TCGA), Human Protein Atlas (HPA), the Gene Expression Profiling Interactive Analysis 2 (GEPIA2), Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Gene Expression Omnibus (GEO) databases. The clinicpathological information were obtained from the dataset of TCGA. The Kaplan-Meier Plotter databases and Cox model were performed for the determination of prognostic values of CCDC71L, including the overall survival (OS), progress free interval (PFI), recurrence-free survival (RFS) and disease specific survival (DSS). Then, the potential mechanism of CCDC71L in HNSCC development was elucidated by means of Gene set enrichment analysis (GSEA) and Metascape databases. Furthermore, the relevance of CCDC71L to immune cells infiltration and immune checkpoints was assessed. The correlations among CCDC71L expression, mutational landscape and genome heterogeneity [mutant-allele tumor heterogeneity (MATH) and tumor purity] were detected by the data in TCGA. RESULTS CCDC71L expression was significantly upregulated in HNSCC, and positively associated with age, gender and N stage. Higher CCDC71L expression resulted in poor OS, RFS, DSS and PFI. Multivariate Cox regression analysis showed CCDC71L would be an independent prognostic marker in patients with HNSCC. Moreover, CCDC71L and the level of macrophage and neutrophil cells infiltration were significantly correlated in HNSCC. High expression of CCDC71L was related to immune checkpoint genes, oncogene mutations and genome heterogeneity markers. CONCLUSION These results implied that CCDC71L plays vital roles in HNSCC progression, which could be used as a underlying biomarker for the diagnosis and prognosis of HNSCC. Meanwhile, CCDC71L participates in immune regulation, which has a potential value for the immunotherapy of HNSCC patients.
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Affiliation(s)
- Yu Zhang
- Department of Oral Medicine, the Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Huifang Tang
- Department of Cariology and Endodontics, Affiliated Hospital of Jining Medical University, Jining, Shandong, China
| | - Minghui Zi
- Department of Oral Medicine, the Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Zhiyong Zhang
- Department of Oral Medicine, the Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Qi Gao
- Department of Oral Medicine, the Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Songbo Tian
- Department of Oral Medicine, the Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
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Deng S, Gao Q, Zhang L, Xie J, Chen Y, Peng X. Prefabricated Zirconia Crowns and Preformed Metal Crowns in the Treatment of Severely Childhood Caries and Anterior Crossbite in a Child with Autistic Spectrum Disorder. Case Rep Dent 2024; 2024:5556502. [PMID: 38390344 PMCID: PMC10883740 DOI: 10.1155/2024/5556502] [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: 08/11/2023] [Revised: 11/29/2023] [Accepted: 01/29/2024] [Indexed: 02/24/2024] Open
Abstract
Crowns have been recommended to treat decayed teeth and rebuild teeth function. The dental management of children with autism is a tremendous challenge for pediatric dentists due to the impaired behaviors and communication disorders. In this context, a 5-year-old boy with autism was treated to solve carious lesions under the assistance of general anesthesia. The posterior occlusal function was restored, and the crossbite existing in the primary anterior teeth was approached merely by NuSmile® zirconia crowns (ZCs) rather than orthodontic intervention. We conducted an 18-month period. Throughout the long-term follow-up, the boy's masticatory efficiency was remarkably improved and the anterior teeth had transferred into the correct position with adequate overbite to maintain the new relationship, thus ameliorating the appearance of tissue on the labial surface and enhancing his quality of life and oral health.
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Affiliation(s)
- Shuman Deng
- Shenzhen Children's Hospital of China Medical University (CMU), Shenzhen 518026, China
- Department of Stomatology, Shenzhen Children's Hospital, Shenzhen, Guangdong 518026, China
| | - Qi Gao
- Department of Maxillofacial Surgery, Shenzhen Second People's Hospital, Shenzhen, Guangdong 518029, China
| | - Li Zhang
- Department of Stomatology, Shenzhen Children's Hospital, Shenzhen, Guangdong 518026, China
| | - Jing Xie
- Department of Stomatology, Shenzhen Children's Hospital, Shenzhen, Guangdong 518026, China
| | - Ying Chen
- Department of Stomatology, Shenzhen Children's Hospital, Shenzhen, Guangdong 518026, China
| | - Xuezhen Peng
- Department of Stomatology, Shenzhen Children's Hospital, Shenzhen, Guangdong 518026, China
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Luo F, Lin Y, Zhang X, Li Y, Su L, Zhou S, Xu R, Gao Q, Chen R, Guo Z, Nie S, Xu X. Post-treatment level of LDL cholesterol and all-cause mortality in patients with atherosclerotic cardiovascular disease: evidence from real-world setting. Eur J Prev Cardiol 2024; 31:337-345. [PMID: 37966728 DOI: 10.1093/eurjpc/zwad354] [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: 06/24/2023] [Revised: 09/04/2023] [Accepted: 10/29/2023] [Indexed: 11/16/2023]
Abstract
AIMS This study aimed to evaluate the safety of the currently recommended target of LDL cholesterol (LDL-C) control on mortality in patients with atherosclerotic cardiovascular disease (ASCVD). METHODS AND RESULTS Using deidentified electronic health record data, we conducted a multicentre retrospective cohort study involving individuals with documented ASCVD who had received statin treatment for at least 3 months across China. The primary outcomes assessed encompassed all-cause mortality, CV mortality, and non-CV mortality. Relationships between post-treatment LDL-C concentrations and outcomes were evaluated using restricted cubic spline curves based on Cox proportional hazards regression analyses. Additionally, competitive risk models were employed to explore associations between LDL-C levels and cause-specific mortality. Among 33 968 participants, we identified nearly linear associations of post-treatment LDL-C level with all-cause mortality and CV mortality during a median follow-up of 47 months. Notably, patients who achieved the recommended target of LDL-C (<1.4 mmol/L) were at significantly lower risks of all-cause mortality [hazard ratio (HR), 0.77; 95% confidence interval (CI), 0.69-0.86] and CV mortality (subdistribution HR, 0.68; 95% CI, 0.58-0.79), compared with those with LDL-C ≥ 3.4 mmol/L. This survival benefit was consistent in patients with different intensities of LDL-C reduction and other subgroup analyses. And no correlation was found between post-treatment LDL-C concentration and non-CV mortality. CONCLUSION Our findings supported the safety of currently recommended target of LDL-C control and the 'lower is better' principle in patients with ASCVD.
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Affiliation(s)
- Fan Luo
- Division of Nephrology, National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, 1838 N Guangzhou Ave, Guangzhou 510515, China
| | - Yuxin Lin
- Division of Nephrology, National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, 1838 N Guangzhou Ave, Guangzhou 510515, China
| | - Xiaodong Zhang
- Division of Nephrology, National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, 1838 N Guangzhou Ave, Guangzhou 510515, China
| | - Yanqin Li
- Division of Nephrology, National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, 1838 N Guangzhou Ave, Guangzhou 510515, China
| | - Licong Su
- Division of Nephrology, National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, 1838 N Guangzhou Ave, Guangzhou 510515, China
| | - Shiyu Zhou
- Division of Nephrology, National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, 1838 N Guangzhou Ave, Guangzhou 510515, China
| | - Ruqi Xu
- Division of Nephrology, National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, 1838 N Guangzhou Ave, Guangzhou 510515, China
| | - Qi Gao
- Division of Nephrology, National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, 1838 N Guangzhou Ave, Guangzhou 510515, China
| | - Ruixuan Chen
- Division of Nephrology, National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, 1838 N Guangzhou Ave, Guangzhou 510515, China
| | - Zhixin Guo
- Division of Nephrology, National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, 1838 N Guangzhou Ave, Guangzhou 510515, China
| | - Sheng Nie
- Division of Nephrology, National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, 1838 N Guangzhou Ave, Guangzhou 510515, China
| | - Xin Xu
- Division of Nephrology, National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, Nanfang Hospital, Southern Medical University, 1838 N Guangzhou Ave, Guangzhou 510515, China
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Li Z, She S, Li G, Gao Q, Ju P, Gao W, Sun C, Wang Y. Random laser emission at 1064 and 1550 nm in a Er/Yb co-doped fiber-based dual-wavelength random fiber laser. Opt Express 2024; 32:5737-5747. [PMID: 38439292 DOI: 10.1364/oe.508025] [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: 10/11/2023] [Accepted: 11/16/2023] [Indexed: 03/06/2024]
Abstract
Dual-wavelength fiber lasers operating with a wide spectral separation are of considerable importance for many applications. In this study, we propose and experimentally explore an all-fiberized dual-wavelength random fiber laser with bi-directional laser output operating at 1064 and 1550 nm, respectively. A specially designed Er/Yb co-doped fiber, by optimizing the concentrations of the co-doped Er, Yb, Al and P, was developed for simultaneously providing Er ions gain and Yb ions gain for RFL. Two spans of single mode passive fibers are employed to providing random feedback for 1064 and 1550 nm random lasing, respectively. The RFL generates 5.35 W at 1064 nm and 6.61 W at 1550 nm random lasers. Two power amplifiers (PA) enhance the seed laser to 50 W at 1064 nm with a 3 dB bandwidth of 0.31 nm and 20 W at 1550 nm with a 3 dB bandwidth of 1.18 nm. Both the short- and long-term time domain stabilities are crucial for practical applications. The output lasers of 1064 and 1550 nm PAs are in the single transverse mode operating with a nearly Gaussian profile. To the best of our knowledge, this is the first demonstration of a dual-wavelength RFL, with a spectral separation as far as about 500 nm in an all-fiber configuration.
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Zou J, Luo G, Zhou L, Wang X, Wang T, Gao Q, Lv T, Xu G, Yao Y, Yan M. Nomogram for predicting postoperative pulmonary complications in spinal tumor patients. BMC Anesthesiol 2024; 24:56. [PMID: 38331767 PMCID: PMC10851528 DOI: 10.1186/s12871-024-02443-7] [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] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 02/02/2024] [Indexed: 02/10/2024] Open
Abstract
OBJECTIVES Although several independent risk factors for postoperative pulmonary complications (PPCs) after spinal tumor surgery have been studied, a simple and valid predictive model for PPC occurrence after spinal tumor surgery has not been developed. PATIENTS AND METHODS We collected data from patients who underwent elective spine surgery for a spinal tumor between 2013 and 2020 at a tertiary hospital in China. Data on patient characteristics, comorbidities, preoperative examinations, intraoperative variables, and clinical outcomes were collected. We used univariable and multivariable logistic regression models to assess predictors of PPCs and developed and validated a nomogram for PPCs. We evaluated the performance of the nomogram using the area under the receiver operating characteristic curve (ROC), calibration curves, the Brier Score, and the Hosmer-Lemeshow (H-L) goodness-of-fit test. For clinical use, decision curve analysis (DCA) was conducted to identify the model's performance as a tool for supporting decision-making. RESULTS Among the participants, 61 (12.4%) individuals developed PPCs. Clinically significant variables associated with PPCs after spinal tumor surgery included BMI, tumor location, blood transfusion, and the amount of blood lost. The nomogram incorporating these factors showed a concordance index (C-index) of 0.755 (95% CI: 0.688-0.822). On internal validation, bootstrapping with 1000 resamples yielded a bias-corrected area under the receiver operating characteristic curve of 0.733, indicating the satisfactory performance of the nomogram in predicting PPCs. The calibration curve demonstrated accurate predictions of observed values. The decision curve analysis (DCA) indicated a positive net benefit for the nomogram across most predicted threshold probabilities. CONCLUSIONS We have developed a new nomogram for predicting PPCs in patients who undergo spinal tumor surgery.
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Affiliation(s)
- Jingcheng Zou
- Department of Anesthesiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ge Luo
- Department of Anesthesiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Liwang Zhou
- Department of Anesthesiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xuena Wang
- Department of Anesthesiology, The First People's Hospital of Huzhou, First affiliated Hospital of Huzhou, Huzhou, China
| | - Tingting Wang
- Department of Anesthesiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qi Gao
- Department of Anesthesiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tao Lv
- The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Guangxin Xu
- The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Yuanyuan Yao
- Department of Anesthesiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Min Yan
- Department of Anesthesiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Key Laboratory of The Diagnosis and Treatment of Severe Trauma and Burn of Zhejiang Province, Hangzhou, China.
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Yao N, Zhang W, Gao Q, Lu C, Wang Q. Case Report: Gas in the esophagus, stomach wall and portal vein with congenital hypertrophic pyloric stenosis. Front Pediatr 2024; 12:1348746. [PMID: 38390279 PMCID: PMC10881817 DOI: 10.3389/fped.2024.1348746] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 01/26/2024] [Indexed: 02/24/2024] Open
Abstract
Background CHPS dramatically affects infant growth and development and can even cause aspiration resulting from esophageal reflux. There is potential danger. CHPS is common, while CHPS with gas in the stomach wall and portal vein is rare. Gas in the stomach wall and portal vein are often the key features of more serious disease. It can be easily mistaken as a serious disease when patients with CHPS have gas in the stomach wall and portal vein. Case presentation A 56-day-old baby was hospitalized for aspiration pneumonia after vomiting without bile for 20 days. Compared with vomiting, which is the most common symptom, pneumonia tends to attract more attention. Because of pneumonia, a chest CT scan was performed and revealed massive gas accumulation in the walls of the esophagus, stomach, and portal vein. Therefore, NEC was considered first and was treated conservatively for one week. However, the vomiting continued, and CHPS was confirmed by ultrasound. The delay in CHPS diagnosis was due to insufficient recognition of the signs of gas accumulation. Because of inexperience and lack of knowledge about CHPS with gastrointestinal pneumatosis, physicians failed to make an early accurate diagnosis. Case 2 was a 29-day-old male who was admitted to the hospital with vomiting without bile. He was examined by ultrasound, which revealed gas in the stomach wall and portal vein after admission to the hospital. No peritonitis was found after a detailed and comprehensive physical examination. Emergency life-threatening diseases such as NEC were quickly ruled out. He received surgery as soon as possible and had an uneventful recovery with no complications. Conclusion CHPS may present with gas in the gastric or esophageal wall and portal vein, which is not a contraindication to surgery.
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Affiliation(s)
- Na Yao
- Department of Nursing, The Affiliated Children Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China
| | - Wenxin Zhang
- Department of General Surgery, The Affiliated Children Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China
| | - Qi Gao
- Department of General Surgery, The Affiliated Children Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China
| | - Chaoxiang Lu
- Department of General Surgery, The Affiliated Children Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China
| | - Qi Wang
- Department of General Surgery, The Affiliated Children Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, China
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Ren Y, Li G, Li E, Deng K, Lian J, Gao Q, Wang H, Wang X, Wang Z, Shen T, Jiang Z, Li X, Qiu G. Luteolin blocks the ROS/PI3K/AKT pathway to inhibit mesothelial-mesenchymal transition and reduce abdominal adhesions. Eur J Pharmacol 2024; 964:176272. [PMID: 38110140 DOI: 10.1016/j.ejphar.2023.176272] [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: 07/26/2023] [Revised: 11/21/2023] [Accepted: 12/08/2023] [Indexed: 12/20/2023]
Abstract
BACKGROUND Postoperative abdominal adhesion (PAA) is a common postoperative complication. Clinically, various methods have been used to prevent the occurrence of PAA, such as drugs and physiotherapy; however, no satisfactory results have been obtained. Luteolin (LUT) is a natural flavonoid that reduces inflammation and acts as an antioxidant. This research aimed to examine the impact and mechanism of LUT in reducing PAA. METHODS C57/BL6 mice were used in vivo experiments. PAA model was established using a brush friction method. Visual scoring and hematoxylin and eosin staining were used to score the severity of adhesions. Network pharmacology was used to infer potential targets and core pathways of LUT. Hydrogen peroxide (H2O2) was used to induce oxidative stress in vitro, while the reactive oxygen species (ROS) assay kit was used to evaluate oxidative stress levels. Western blotting, cell immunofluorescence, and multiple immunofluorescence assays were used to detect α-SMA, vimentin, E-cadherin, collagen I, or AKT phosphorylation level. Scratch assay was used to detect cell migration. RESULTS LUT reduced the degree of PAA in mice. It attenuated H2O2-induced ROS production and reversed mesothelial-mesenchymal transition (MMT) in HMrSV5 cells. Network pharmacology analysis showed that LUT likely exerted anti-adhesion activity by regulating the PI3K-Akt signaling pathway. Phosphorylated Akt levels were significantly reduced in LUT-treated HMrSV5 cells. LUT also significantly reduced the expression of vimentin and collagen I in adherent tissues and upregulated E-cadherin expression. CONCLUSION LUT blocks the ROS/PI3K/AKT pathway, thereby inhibiting MMT and reducing PAA. To this end, LUT has potential in PAA therapy.
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Affiliation(s)
- Yiwei Ren
- Department of General Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi, China
| | - Gan Li
- Department of General Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi, China
| | - Enmeng Li
- Department of General Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi, China
| | - Kai Deng
- Department of General Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi, China
| | - Jie Lian
- Department of Pathology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi, China
| | - Qi Gao
- Department of General Surgery, Shaanxi Provincial People's Hospital, Xi'an Medical University, 710061 Xi'an, Shaanxi, China
| | - Huijun Wang
- Department of General Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi, China
| | - Xingjie Wang
- Department of General Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi, China
| | - Zijun Wang
- Department of General Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi, China
| | - Tianli Shen
- Department of General Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi, China
| | - Zhengdong Jiang
- Department of General Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi, China
| | - Xuqi Li
- Department of General Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi, China; Department of Talent Highland, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi, China.
| | - Guanglin Qiu
- Department of General Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi, China.
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Zhang C, Liu Y, Corner L, Gao Q, Kang YT, Shi H, Li JW, Shen J. Interaction between handgrip strength and vitamin D deficiency on all-cause mortality in community-dwelling older adults: a prospective cohort study. Public Health 2024; 227:1-8. [PMID: 38096620 DOI: 10.1016/j.puhe.2023.11.022] [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: 05/11/2023] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 02/18/2024]
Abstract
OBJECTIVE Muscle strength decline and vitamin D deficiency are coexisting conditions associated with multiple adverse health outcomes. This prospective study aimed to investigate the multiplicative and additive interactions between handgrip strength (HS) and serum 25-hydroxyvitamin D [25(OH)D] on all-cause mortality in Chinese community-dwelling older adults. STUDY DESIGN This is a population-based cohort study. METHODS 2635 older adults (85.15 ± 12.01 years) were recruited from the Chinese Longitudinal Healthy Longevity Survey (2012-2018). Low HS was defined according to the Asian Working Group for Sarcopenia 2019 updated consensus (<28 kg for men and <18 kg for women). Serum 25(OH)D < 50 nmol/L were defined as vitamin D deficiency. Cox proportional hazard models were used to examine the association of HS and 25(OH)D with all-cause mortality. Socio-demographics, health status, and clinical characteristics were included as covariates. RESULTS 1715 (65.09 %) and 1885 (71.54 %) participants had low HS and vitamin D deficiency, respectively. During a median follow-up of 3.52 years, 1107 older people died. After multivariable adjustment, both HS and 25(OH)D levels were inversely associated with all-cause mortality risk (Ps < 0.001). The hazard ratios (HRs) of low HS and vitamin D deficiency for all-cause mortality were 1.73 (95 % CI: 1.41-2.13) and 1.61 (95 % CI: 1.32-1.93), respectively. Although significant multiplicative interactions were not found, the association between low HS and all-cause mortality was attenuated in the higher 25(OH)D subgroup than in the lower 25(OH)D subgroup (stratified by 50 nmol/L). The multiple-adjusted HR of mortality for combined low HS and vitamin D deficiency was 2.18 (95 % CI: 1.73-2.56), which was higher than that for these two conditions alone. Significant additive interactions between low HS and vitamin D deficiency on mortality were observed (relative excess risk due to interaction: 0.71, 95 % CI: 0.37-1.05). CONCLUSIONS Low HS and low 25(OH)D levels synergistically increased the risk of all-cause mortality. Our results added new insights to the priority of early detection for older adults with comorbid muscle strength decline and vitamin D deficiency.
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Affiliation(s)
- C Zhang
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, National Center of Gerontology of National Health Commission, Beijing 100730, China
| | - Y Liu
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, National Center of Gerontology of National Health Commission, Beijing 100730, China
| | - L Corner
- UK National Innovation Centre for Ageing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK
| | - Q Gao
- Department of Science Research, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Y T Kang
- Department of Geriatrics, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - H Shi
- Department of Geriatrics, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - J W Li
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital, National Center of Gerontology of National Health Commission, Beijing 100730, China.
| | - J Shen
- Department of Geriatrics, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, China.
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Gao Q, Li Z, Zhang J, Zhang Y, Shan H. CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization. IEEE Trans Med Imaging 2024; 43:745-759. [PMID: 37773896 DOI: 10.1109/tmi.2023.3320812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/01/2023]
Abstract
Low-dose computed tomography (CT) images suffer from noise and artifacts due to photon starvation and electronic noise. Recently, some works have attempted to use diffusion models to address the over-smoothness and training instability encountered by previous deep-learning-based denoising models. However, diffusion models suffer from long inference time due to a large number of sampling steps involved. Very recently, cold diffusion model generalizes classical diffusion models and has greater flexibility. Inspired by cold diffusion, this paper presents a novel COntextual eRror-modulated gEneralized Diffusion model for low-dose CT (LDCT) denoising, termed CoreDiff. First, CoreDiff utilizes LDCT images to displace the random Gaussian noise and employs a novel mean-preserving degradation operator to mimic the physical process of CT degradation, significantly reducing sampling steps thanks to the informative LDCT images as the starting point of the sampling process. Second, to alleviate the error accumulation problem caused by the imperfect restoration operator in the sampling process, we propose a novel ContextuaL Error-modulAted Restoration Network (CLEAR-Net), which can leverage contextual information to constrain the sampling process from structural distortion and modulate time step embedding features for better alignment with the input at the next time step. Third, to rapidly generalize the trained model to a new, unseen dose level with as few resources as possible, we devise a one-shot learning framework to make CoreDiff generalize faster and better using only one single LDCT image (un)paired with normal-dose CT (NDCT). Extensive experimental results on four datasets demonstrate that our CoreDiff outperforms competing methods in denoising and generalization performance, with clinically acceptable inference time. Source code is made available at https://github.com/qgao21/CoreDiff.
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Xu H, Gao Q, Li L, Su T, Ming D. How alginate lyase produces quasi-monodisperse oligosaccharides: A normal-mode-based docking and molecular dynamics simulation study. Carbohydr Res 2024; 536:109022. [PMID: 38242069 DOI: 10.1016/j.carres.2024.109022] [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: 09/24/2023] [Revised: 12/12/2023] [Accepted: 01/04/2024] [Indexed: 01/21/2024]
Abstract
Oligosaccharide degradation products of alginate (AOS) hold significant potential in diverse fields, including pharmaceuticals, health foods, textiles, and agricultural production. Enzymatic alginate degradation is appealing due to its mild conditions, predictable activity, high yields, and controllability. However, the alginate degradation often results in a complex mixture of oligosaccharides, necessitating costly purification to isolate highly active oligosaccharides with a specific degree of polymerization (DP). Addressing this, our study centers on the alginate lyase AlyB from Vibrio Splendidus OU02, which uniquely breaks down alginate into mono-distributed trisaccharides. This enzyme features a polysaccharide lyase family 7 domain (PL-7) and a CBM32 carbohydrate-binding module connected by a helical structure. Through normal-mode-based docking and all-atom molecular simulations, we demonstrate that AlyB's substrate and product specificities are influenced by the spatial conformation of the catalytic pocket and the flexibility of its structure. The helically attached CBM is pivotal in releasing trisaccharides, which is crucial for avoiding further degradation. This study sheds light on AlyB's specificity and efficiency and contributes to the evolving field of enzyme design for producing targeted oligosaccharides, with significant implications for various bioindustries.
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Affiliation(s)
- Hengyue Xu
- College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, 30 South Puzhu Road, Jiangbei New District, Nanjing City, Jiangsu, 211816, PR China; Now Studying in the State Key Laboratory of Chemical Oncogenomics, Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, PR China
| | - Qi Gao
- College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, 30 South Puzhu Road, Jiangbei New District, Nanjing City, Jiangsu, 211816, PR China
| | - Lu Li
- College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, 30 South Puzhu Road, Jiangbei New District, Nanjing City, Jiangsu, 211816, PR China
| | - Ting Su
- College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, 30 South Puzhu Road, Jiangbei New District, Nanjing City, Jiangsu, 211816, PR China
| | - Dengming Ming
- College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, 30 South Puzhu Road, Jiangbei New District, Nanjing City, Jiangsu, 211816, PR China.
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