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Hua W, Liu T, Zheng Z, Yuan H, Xiao L, Feng K, Hui J, Deng Z, Ma M, Cheng J, Song D, Lyu F, Zhong J, Peng Y. Pulse Electrolysis Turns on CO 2 Methanation through N-Confused Cupric Porphyrin. Angew Chem Int Ed Engl 2024; 63:e202315922. [PMID: 38287420 DOI: 10.1002/anie.202315922] [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: 10/20/2023] [Revised: 01/17/2024] [Accepted: 01/29/2024] [Indexed: 01/31/2024]
Abstract
Breaking the D4h symmetry in the square-planar M-N4 configuration of macrocycle molecular catalysts has witnessed enhanced electrocatalytic activity, but at the expense of electrochemical stability. Herein, we hypothesize that the lability of the active Cu-N3 motifs in the N-confused copper (II) tetraphenylporphyrin (CuNCP) could be overcome by applying pulsed potential electrolysis (PPE) during electrocatalytic carbon dioxide reduction. We find that applying PPE can indeed enhance the CH4 selectivity on CuNCP by 3 folds to reach the partial current density of 170 mA cm-2 at >60 % Faradaic efficiency (FE) in flow cell. However, combined ex situ X-ray diffraction (XRD), transmission electron microscope (TEM), and in situ X-ray absorption spectroscopy (XAS), infrared (IR), Raman, scanning electrochemical microscopy (SECM) characterizations reveal that, in a prolonged time scale, the decomplexation of CuNCP is unavoidable, and the promoted water dissociation under high anodic bias with lowered pH and enriched protons facilitates successive hydrogenation of *CO on the irreversibly reduced Cu nanoparticles, leading to the improved CH4 selectivity. As a key note, this study signifies the adaption of electrolytic protocol to the catalyst structure for tailoring local chemical environment towards efficient CO2 reduction.
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Affiliation(s)
- Wei Hua
- Soochow Institute for Energy and Materials Innovations, Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of Jiangsu Province, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
| | - Tingting Liu
- Soochow Institute for Energy and Materials Innovations, Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of Jiangsu Province, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
| | - Zhangyi Zheng
- Soochow Institute for Energy and Materials Innovations, Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of Jiangsu Province, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
| | - Huihong Yuan
- Soochow Institute for Energy and Materials Innovations, Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of Jiangsu Province, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
| | - Long Xiao
- Soochow Institute for Energy and Materials Innovations, Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of Jiangsu Province, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
| | - Kun Feng
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, 215123, P. R. China
| | - Jingshu Hui
- Soochow Institute for Energy and Materials Innovations, Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of Jiangsu Province, College of Energy, Soochow University, Suzhou, 215006, P. R. China
| | - Zhao Deng
- Soochow Institute for Energy and Materials Innovations, Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of Jiangsu Province, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
| | - Mutian Ma
- Soochow Institute for Energy and Materials Innovations, Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of Jiangsu Province, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
| | - Jian Cheng
- Soochow Institute for Energy and Materials Innovations, Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of Jiangsu Province, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
| | - Daqi Song
- Soochow Institute for Energy and Materials Innovations, Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of Jiangsu Province, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
| | - Fenglei Lyu
- Soochow Institute for Energy and Materials Innovations, Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of Jiangsu Province, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
| | - Jun Zhong
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, 215123, P. R. China
| | - Yang Peng
- Soochow Institute for Energy and Materials Innovations, Key Laboratory of Advanced Carbon Materials and Wearable Energy Technologies of Jiangsu Province, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
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Feng Y, Zhang W, Wei J, Jiang D, Tong S, Huang C, Xu Z, Wang X, Tao J, Li Z, Hu J, Zhang Y, Cheng J. Medium-term exposure to size-fractioned particulate matter and asthma exacerbations in China: A longitudinal study of asthmatics with poor medication adherence. Ecotoxicol Environ Saf 2024; 274:116234. [PMID: 38503107 DOI: 10.1016/j.ecoenv.2024.116234] [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: 01/25/2024] [Revised: 03/11/2024] [Accepted: 03/14/2024] [Indexed: 03/21/2024]
Abstract
BACKGROUND Studies have shown that short- and long-term exposure to particulate matter (PM) can increase the risk of asthma morbidity and mortality. However, the effect of medium-term exposure remains unknown. We aim to examine the effect of medium-term exposure to size-fractioned PM on asthma exacerbations among asthmatics with poor medication adherence. METHODS We conducted a longitudinal study in China based on the National Mobile Asthma Management System Project that specifically and routinely followed asthma exacerbations in asthmatics with poor medication adherence from April 2017 to May 2019. High-resolution satellite remote-sensing data were used to estimate each participant's medium-term exposure (on average 90 days) to size-fractioned PM (PM1, PM2.5, and PM10) based on the residential address and the date of the follow-up when asthma exacerbations (e.g., hospitalizations and emergency room visits) occurred or the end of the follow-up. The Cox proportional hazards model was employed to examine the hazard ratio of asthma exacerbations associated with each PM after controlling for sex, age, BMI, education level, geographic region, and temperature. RESULTS Modelling results revealed nonlinear exposure-response associations of asthma exacerbations with medium-term exposure to PM1, PM2.5, and PM10. Specifically, for emergency room visits, we found an increased hazard ratio for PM1 above 22.8 µg/m3 (1.060, 95 % CI: 1.025-1.096, per 1 µg/m3 increase), PM2.5 above 38.2 µg/m3 (1.032, 95 % CI: 1.010-1.054), and PM10 above 78.6 µg/m3 (1.019, 95 % CI: 1.006-1.032). For hospitalizations, we also found an increased hazard ratio for PM1 above 20.3 µg/m3 (1.055, 95 % CI: 1.001-1.111) and PM2.5 above 39.2 µg/m3 (1.038, 95 % CI: 1.003-1.074). Furthermore, the effects of PM were greater for a longer exposure window (90-180 days) and among participants with a high BMI. CONCLUSION This study suggests that medium-term exposure to PM is associated with an increased risk of asthma exacerbations in asthmatics with poor medication adherence, with a higher risk from smaller PM.
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Affiliation(s)
- Yufan Feng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Wenyi Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Chinese PLA Center for Disease Control and Prevention, Beijing, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, USA
| | - Dingyuan Jiang
- Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, National Center for Respiratory Medicine, Beijing, China
| | - Shilu Tong
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | - Cunrui Huang
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Zhiwei Xu
- School of Medicine and Dentistry, Griffith University, Gold Coast, Queensland, Australia
| | - Xiling Wang
- School of Public Health, Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China
| | - Junwen Tao
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Zhiwei Li
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Jihong Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Yongming Zhang
- Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital; National Clinical Research Center for Respiratory Diseases, Beijing, 100029, China.
| | - Jian Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China.
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Liu X, Zhu X, Cheng J, Jiang H. A new era for paclitaxel biosynthesis is coming. Mol Plant 2024; 17:370-371. [PMID: 38243592 DOI: 10.1016/j.molp.2024.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 01/10/2024] [Accepted: 01/12/2024] [Indexed: 01/21/2024]
Affiliation(s)
- Xiaonan Liu
- Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China.
| | - Xiaoxi Zhu
- Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Jian Cheng
- Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Huifeng Jiang
- Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China.
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Wu K, Tao J, Wu Q, Su H, Huang C, Xia Q, Zhu C, Wei J, Yang M, Yan J, Cheng J. A stronger association of mental disorders with smaller particulate matter and a modifying effect of air temperature. Environ Pollut 2024; 347:123677. [PMID: 38447653 DOI: 10.1016/j.envpol.2024.123677] [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: 09/10/2023] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 03/08/2024]
Abstract
Mental disorders (MDs) can be triggered by adverse weather conditions and particulate matter (PM) such as PM2.5 and PM10 (aerodynamic diameter ≤2.5 μm and ≤10 μm). However, there is a dearth of evidence on the role of smaller PM (e.g. PM1, aerodynamic diameter ≤1 μm) and the potential modifying effects of weather conditions. We aimed to collect daily data on emergency department visits and hospitalisations for schizophrenia-, mood-, and stress-related disorders in a densely populated Chinese city (Hefei) between 2016 and 2019. A time-stratified case-crossover analysis was used to examine the short-term association of MDs with PM1, PM2.5, and PM10. The potential modifying effects of air temperature conditions (cold and warm days) were also explored. The three size-fractioned PMs were all associated with an increased risk of MDs; however, the association differed between emergency department visit and hospitalisation. Specifically, PM1 was primarily associated with an increased risk of emergency department visit, whereas PM2.5 was primarily associated with an increased risk of hospitalisation, and PM10 was associated with an increased risk of both emergency department visit and hospitalisation. The PM-MD association appeared to be greatest (although not significant) for PM1 (odds ratio range: 1.014-1.055), followed by PM2.5 (odds ratio range: 1.001-1.009) and PM10 (odds ratio range: 1.001-1.006). Furthermore, the PM-MD association was observed on cold days; notably, the association between PM and schizophrenia-related disorders was significant on both cold and warm days. Our results suggest that the smaller the PM, the greater the risk of MDs, and that the PM-MD association could be determined by air temperature conditions.
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Affiliation(s)
- Keyu Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Junwen Tao
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Qiyue Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Hong Su
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Cunrui Huang
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Qingrong Xia
- Affiliated Psychological Hospital of Anhui Medical University, Hefei, China; Anhui Mental Health Center, Hefei, China; Hefei Fourth People's Hospital, Hefei, China; Anhui Clinical Research Center for Mental Disorders, Hefei, China
| | - Cuizhen Zhu
- Affiliated Psychological Hospital of Anhui Medical University, Hefei, China; Anhui Mental Health Center, Hefei, China; Hefei Fourth People's Hospital, Hefei, China; Anhui Clinical Research Center for Mental Disorders, Hefei, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, USA
| | - Min Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Junwei Yan
- Affiliated Psychological Hospital of Anhui Medical University, Hefei, China; Anhui Mental Health Center, Hefei, China; Hefei Fourth People's Hospital, Hefei, China; Anhui Clinical Research Center for Mental Disorders, Hefei, China
| | - Jian Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China.
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Lu X, Bai J, Tian Z, Li C, Ahmed N, Liu X, Cheng J, Lu L, Cai J, Jiang H, Wang W. Cyclization mechanism of monoterpenes catalyzed by monoterpene synthases in dipterocarpaceae. Synth Syst Biotechnol 2024; 9:11-18. [PMID: 38173809 PMCID: PMC10758623 DOI: 10.1016/j.synbio.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] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 11/07/2023] [Accepted: 11/25/2023] [Indexed: 01/05/2024] Open
Abstract
Monoterpenoids are typically present in the secretory tissues of higher plants, and their biosynthesis is catalyzed by the action of monoterpene synthases (MTSs). However, the knowledge about these enzymes is restricted in a few plant species. MTSs are responsible for the complex cyclization of monoterpene precursors, resulting in the production of diverse monoterpene products. These enzymatic reactions are considered exceptionally complex in nature. Therefore, it is crucial to understand the catalytic mechanism of MTSs to elucidate their ability to produce diverse or specific monoterpenoid products. In our study, we analyzed thirteen genomes of Dipterocarpaceae and identified 38 MTSs that generate a variety of monoterpene products. By focusing on four MTSs with different product spectra and analyzing the formation mechanism of acyclic, monocyclic and bicyclic products in MTSs, we observed that even a single amino acid mutation can change the specificity and diversity of MTS products, which is due to the synergistic effect between the shape of the active cavity and the stabilization of carbon-positive intermediates that the mutation changing. Notably, residues N340, I448, and phosphoric acid groups were found to be significant contributors to the stabilization of intermediate terpinyl and pinene cations. Alterations in these residues, either directly or indirectly, can impact the synthesis of single monoterpenes or their mixtures. By revealing the role of key residues in the catalytic process and establishing the interaction model between specific residues and complex monoterpenes in MTSs, it will be possible to reasonably design and engineer different catalytic activities into existing MTSs, laying a foundation for the artificial design and industrial application of MTSs.
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Affiliation(s)
- Xiaoyun Lu
- School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, Shanxi, 710072, China
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Jie Bai
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Zunzhe Tian
- School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, Shanxi, 710072, China
| | - Congyu Li
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Nida Ahmed
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Xiaonan Liu
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Jian Cheng
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Lina Lu
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Jing Cai
- School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, Shanxi, 710072, China
| | - Huifeng Jiang
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China
| | - Wen Wang
- School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, Shanxi, 710072, China
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Fang K, Wang Z, Xia Q, Liu Y, Wang B, Cheng Z, Cheng J, Jin X, Bai R, Li L. Normalizing Flow-Based Distribution Estimation of Pharmacokinetic Parameters in Dynamic Contrast-Enhanced Magnetic Resonance Imaging. IEEE Trans Biomed Eng 2024; 71:780-791. [PMID: 37738180 DOI: 10.1109/tbme.2023.3318087] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/24/2023]
Abstract
OBJECTIVE The pharmacokinetic (PK) parameters estimated from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provide valuable information for clinical research and diagnosis. However, these estimated PK parameters suffer from many sources of variability. Thus, the estimation of the posterior distributions of these PK parameters could provide a way to simultaneously quantify the values and uncertainties of the PK parameters. Our objective is to develop an efficient and flexible method to more closely approximate and estimate the underlying posterior distributions of the PK parameters. METHODS The normalizing flow model-based parameters distribution estimation neural network (FPDEN) is proposed to adaptively learn and estimate the posterior distributions of the PK parameters. The maximum likelihood estimation (MLE) loss is directly constructed based on the parameter distributions learned by the normalizing flow model, rather than pre-defined distributions. RESULTS Experimental analysis shows that the proposed method can improve parameter estimation accuracy. Moreover, the uncertainty derived from the parameter distribution constitutes an effective indicator to exclude unreliable parametric results. A successful demonstration is the improved classification performance of the glioma World Health Organization (WHO) grading task, specifically in terms of distinguishing between low and high grades, as well as between Grade III and Grade IV. CONCLUSION The FPDEN method offers improved accuracy for estimation of PK parameters and boosts the performance of the glioma grading task. SIGNIFICANCE By enhancing the precision and reliability of DCE-MRI, the proposed method promotes its further applications in clinical practice.
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Tian Y, Li XT, Liu JR, Cheng J, Gao A, Yang NY, Li Z, Guo KX, Zhang W, Wen HT, Li ZL, Gu QS, Hong X, Liu XY. A general copper-catalysed enantioconvergent C(sp 3)-S cross-coupling via biomimetic radical homolytic substitution. Nat Chem 2024; 16:466-475. [PMID: 38057367 DOI: 10.1038/s41557-023-01385-w] [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: 01/30/2023] [Accepted: 10/24/2023] [Indexed: 12/08/2023]
Abstract
Although α-chiral C(sp3)-S bonds are of enormous importance in organic synthesis and related areas, the transition-metal-catalysed enantioselective C(sp3)-S bond construction still represents an underdeveloped domain probably due to the difficult heterolytic metal-sulfur bond cleavage and notorious catalyst-poisoning capability of sulfur nucleophiles. Here we demonstrate the use of chiral tridentate anionic ligands in combination with Cu(I) catalysts to enable a biomimetic enantioconvergent radical C(sp3)-S cross-coupling reaction of both racemic secondary and tertiary alkyl halides with highly transformable sulfur nucleophiles. This protocol not only exhibits a broad substrate scope with high enantioselectivity but also provides universal access to a range of useful α-chiral alkyl organosulfur compounds with different sulfur oxidation states, thus providing a complementary approach to known asymmetric C(sp3)-S bond formation methods. Mechanistic results support a biomimetic radical homolytic substitution pathway for the critical C(sp3)-S bond formation step.
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Affiliation(s)
- Yu Tian
- Shenzhen Grubbs Institute and Department of Chemistry, Southern University of Science and Technology, Shenzhen, China
- Shenzhen Key Laboratory of Cross-Coupling Reactions, Southern University of Science and Technology, Shenzhen, China
| | - Xi-Tao Li
- Shenzhen Grubbs Institute and Department of Chemistry, Southern University of Science and Technology, Shenzhen, China
- School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, P. R. China
| | - Ji-Ren Liu
- Shenzhen Grubbs Institute and Department of Chemistry, Southern University of Science and Technology, Shenzhen, China
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, China
| | - Jian Cheng
- Shenzhen Grubbs Institute and Department of Chemistry, Southern University of Science and Technology, Shenzhen, China
| | - Ang Gao
- Academy for Advanced Interdisciplinary Studies and Department of Chemistry, Southern University of Science and Technology, Shenzhen, China
| | - Ning-Yuan Yang
- Shenzhen Grubbs Institute and Department of Chemistry, Southern University of Science and Technology, Shenzhen, China
| | - Zhuang Li
- Academy for Advanced Interdisciplinary Studies and Department of Chemistry, Southern University of Science and Technology, Shenzhen, China
| | - Kai-Xin Guo
- Academy for Advanced Interdisciplinary Studies and Department of Chemistry, Southern University of Science and Technology, Shenzhen, China
| | - Wei Zhang
- Shenzhen Grubbs Institute and Department of Chemistry, Southern University of Science and Technology, Shenzhen, China
| | - Han-Tao Wen
- Academy for Advanced Interdisciplinary Studies and Department of Chemistry, Southern University of Science and Technology, Shenzhen, China
| | - Zhong-Liang Li
- Academy for Advanced Interdisciplinary Studies and Department of Chemistry, Southern University of Science and Technology, Shenzhen, China
| | - Qiang-Shuai Gu
- Academy for Advanced Interdisciplinary Studies and Department of Chemistry, Southern University of Science and Technology, Shenzhen, China
| | - Xin Hong
- Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, China
| | - Xin-Yuan Liu
- Shenzhen Grubbs Institute and Department of Chemistry, Southern University of Science and Technology, Shenzhen, China.
- Shenzhen Key Laboratory of Cross-Coupling Reactions, Southern University of Science and Technology, Shenzhen, China.
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Huang S, Du B, Chen Z, Cheng J. The government subsidy design considering the reference price effect in a green supply chain. Environ Sci Pollut Res Int 2024:10.1007/s11356-024-32488-7. [PMID: 38409384 DOI: 10.1007/s11356-024-32488-7] [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] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 02/11/2024] [Indexed: 02/28/2024]
Abstract
This paper constructs a green supply chain with a manufacturer and a retailer. Taking into account the reference price effect of consumers based on the mental accounting theory, we investigate the following government incentive policies: R&D (research and development) subsidy, consumption subsidy, and dual subsidy. For manufacturer-led (M-led) and retailer-led (R-led) supply chains, we evaluate the optimal wholesale price, sales price, green degree of product, and the optimal subsidy of the government aiming to improve the environmental benefit or social welfare. We find that the government goal, power structure and reference price effect impact the design of subsidy mechanisms significantly. First, for M-led supply chain, the government concerned with the environmental benefit goal should only provide R&D subsidy for the manufacturer when the reference price effect is low; otherwise, the government would offer subsidy both for the manufacturer and consumers. However, the government will only offer R&D subsidy when the social welfare goal is adopted. Second, for R-led supply chain, the government aiming to improve the environmental benefit prefers dual subsidy when the reference price effect is low; otherwise, consumption subsidy is preferable. Surprisingly, under the social welfare goal, no subsidy for R-led supply chain tends to be the best option. Intriguingly, embracing the social welfare goal can result in more economic and environmental benefits for M-led supply chain, although the subsidy strategy is less effective than the environmental benefit goal. Our research can provide inspirations and references for designing government subsidy mechanisms in practice.
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Affiliation(s)
- Shuai Huang
- Business School, Qingdao University, Qingdao, 266071, China
| | - Bingzhi Du
- Business School, Qingdao University, Qingdao, 266071, China
| | - Zhongwei Chen
- School of Management, Shandong University, Jinan, 250100, China.
| | - Jian Cheng
- Business School, Qingdao University, Qingdao, 266071, China
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Liu Z, Zhang J, Wang S, Geng F, Zhang Q, Cheng J, Chen M, Xu Q. Ultrafast Process Characterization of Laser-Induced Damage in Fused Silica Using Pump-Probe Shadow Imaging Techniques. Materials (Basel) 2024; 17:837. [PMID: 38399088 PMCID: PMC10890167 DOI: 10.3390/ma17040837] [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: 01/03/2024] [Revised: 01/31/2024] [Accepted: 02/03/2024] [Indexed: 02/25/2024]
Abstract
This study delves into the intricate dynamics of laser-induced damage in fused silica using a time-resolved pump-probe (TRPP) shadowgraph. Three typical ultra-fast processes, laser-induced plasma evolution, shockwave propagation and material fracture splashing, were quantitatively investigated. The results indicate that the diameter of plasma is proportional to the pulse laser energy and increases linearly during the pulse laser duration with an expansion rate of approximately 6 km/s. The maximum shockwave velocity on the air side is 9 km/s, occurring at the end of the pulse duration, and then rapidly decreases due to air resistance, reaching approximately 1 km/s around a 300 ns delay. After hundreds of nanoseconds, there is a distinct particle splashing phenomenon, with the splashing particle speed distribution ranging from 0.15 km/s to 2.0 km/s. The particle sizes of the splashing particles range from 4 μm to 15 μm. Additionally, the smaller the delay, the faster the speed of the splashing particles. Overall, TRPP technology provides crucial insights into the temporal evolution of laser-induced damage in fused silica, contributing to a comprehensive understanding essential for optimizing the performance and safety of laser systems.
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Affiliation(s)
- Zhichao Liu
- Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang 621900, China; (Z.L.); (J.Z.); (S.W.); (F.G.); (Q.Z.)
| | - Jian Zhang
- Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang 621900, China; (Z.L.); (J.Z.); (S.W.); (F.G.); (Q.Z.)
| | - Shengfei Wang
- Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang 621900, China; (Z.L.); (J.Z.); (S.W.); (F.G.); (Q.Z.)
| | - Feng Geng
- Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang 621900, China; (Z.L.); (J.Z.); (S.W.); (F.G.); (Q.Z.)
| | - Qinghua Zhang
- Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang 621900, China; (Z.L.); (J.Z.); (S.W.); (F.G.); (Q.Z.)
| | - Jian Cheng
- Center for Precision Engineering, Harbin Institute of Technology, Harbin 150001, China; (J.C.); (M.C.)
| | - Mingjun Chen
- Center for Precision Engineering, Harbin Institute of Technology, Harbin 150001, China; (J.C.); (M.C.)
| | - Qiao Xu
- Research Center of Laser Fusion, China Academy of Engineering Physics, Mianyang 621900, China; (Z.L.); (J.Z.); (S.W.); (F.G.); (Q.Z.)
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10
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Nie X, Yang J, Li X, Zhan T, Liu D, Yan H, Wei Y, Liu X, Chen J, Gong G, Wu Z, Yang Z, Wen M, Gu W, Pan Y, Jiang Y, Meng X, Liu T, Cheng J, Li Z, Miao Z, Liu L. Prediction of futile recanalisation after endovascular treatment in acute ischaemic stroke: development and validation of a hybrid machine learning model. Stroke Vasc Neurol 2024:svn-2023-002500. [PMID: 38336369 DOI: 10.1136/svn-2023-002500] [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: 03/29/2023] [Accepted: 01/23/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND Identification of futile recanalisation following endovascular therapy (EVT) in patients with acute ischaemic stroke is both crucial and challenging. Here, we present a novel risk stratification system based on hybrid machine learning method for predicting futile recanalisation. METHODS Hybrid machine learning models were developed to address six clinical scenarios within the EVT and perioperative management workflow. These models were trained on a prospective database using hybrid feature selection technique to predict futile recanalisation following EVT. The optimal model was validated and compared with existing models and scoring systems in a multicentre prospective cohort to develop a hybrid machine learning-based risk stratification system for futile recanalisation prediction. RESULTS Using a hybrid feature selection approach, we trained and tested multiple classifiers on two independent patient cohorts (n=1122) to develop a hybrid machine learning-based prediction model. The model demonstrated superior discriminative ability compared with other models and scoring systems (area under the curve=0.80, 95% CI 0.73 to 0.87) and was transformed into a web application (RESCUE-FR Index) that provides a risk stratification system for individual prediction (accessible online at fr-index.biomind.cn/RESCUE-FR/). CONCLUSIONS The proposed hybrid machine learning approach could be used as an individualised risk prediction model to facilitate adherence to clinical practice guidelines and shared decision-making for optimal candidate selection and prognosis assessment in patients undergoing EVT.
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Affiliation(s)
- Ximing Nie
- Department of Neurology, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Jinxu Yang
- School of Computer and Communication Engineering, University of Science and Technology, Beijing, China
| | - Xinxin Li
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Tianming Zhan
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Dongdong Liu
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Hongyi Yan
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Yufei Wei
- Department of Neurology, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Xiran Liu
- Department of Neurology, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Jiaping Chen
- Department of Neurology, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Guoyang Gong
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Zhenzhou Wu
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Zhonghua Yang
- Department of Neurology, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Miao Wen
- Department of Neurology, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Weibin Gu
- Department of Radiology, Beijing Tiantan Hospital, Beijing, China
| | - Yuesong Pan
- Department of Neurology, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Yong Jiang
- Department of Neurology, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Xia Meng
- Department of Neurology, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Tao Liu
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, School of Biological Science and Medical Engineering International Research Institute for Multidisciplinary Science, Beihang University, Beijing, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Computer Science and Engineering, Beihang University, Beijing, China
| | - Jian Cheng
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, School of Biological Science and Medical Engineering International Research Institute for Multidisciplinary Science, Beihang University, Beijing, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Computer Science and Engineering, Beihang University, Beijing, China
| | - Zixiao Li
- Department of Neurology, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Zhongrong Miao
- Department of Interventional Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Liping Liu
- Department of Neurology, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
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11
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Wu Q, Xing X, Yang M, Bai Z, He Q, Cheng Q, Hu J, Wang H, Fan Y, Su H, Liu Z, Cheng J. Increased suicide mortality and reduced life expectancy associated with ambient heat exposure. Am J Prev Med 2024:S0749-3797(24)00035-7. [PMID: 38311191 DOI: 10.1016/j.amepre.2024.01.020] [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: 06/07/2023] [Revised: 01/25/2024] [Accepted: 01/28/2024] [Indexed: 02/10/2024]
Abstract
INTRODUCTION Ambient heat exposure is a risk factor for suicide in many regions of the world. However, little is known about the extent to which life expectancy has been shortened by heat-related suicide deaths. This study aimed to evaluate the short-term effects of heat on suicide mortality and quantify the reduced life expectancy associated with heat in China. METHODS A time-stratified, case-crossover analysis in 2023 was performed during the warm season (May to September) from 2016-2020 to assess the short-term association between extreme heat (the 95th percentile of mean temperature) and suicide mortality in Anhui Province, China. A subgroup analysis was performed according to sex, age, marital status, suicide type, and region. The attributable fraction and years of life lost due to heat were calculated, and the heat-related life expectancy loss was estimated. RESULTS This study included 9,642 suicide deaths, with an average age of 62.4 years and 58.8% of suicides in males. Suicide risk was associated with an 80.7% increase [95% confidence interval [CI]: 21.4%-68.9%] after exposure to extreme heat (30.6°C) in comparison to daily minimum temperature (7.9°C). Subgroup analysis revealed that heat-related suicide risk was more prominent in the married population than in the unmarried population. Heat was estimated to be associated with 31.7% (95% CI: 18.0%-43.2%) of the suicides, corresponding to 7.0 years of loss in life expectancy for each decedent. CONCLUSIONS Heat exposure was associated with an increased risk of suicide and reduced life expectancy. However, further prospective studies are required to confirm this relationship.
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Affiliation(s)
- Qiyue Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China.; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Xiuya Xing
- Anhui Provincial Center for Disease Control and Prevention, Hefei, China.; Public Health Research Institute of Anhui Province, Hefei, China
| | - Min Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China.; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Zhongliang Bai
- Department of Health Services Management, School of Health Services Management, Anhui Medical University, Hefei, China
| | - Qin He
- Anhui Provincial Center for Disease Control and Prevention, Hefei, China.; Public Health Research Institute of Anhui Province, Hefei, China
| | - Qianyao Cheng
- Anhui Provincial Center for Disease Control and Prevention, Hefei, China.; Public Health Research Institute of Anhui Province, Hefei, China
| | - Jingyao Hu
- Anhui Provincial Center for Disease Control and Prevention, Hefei, China.; Public Health Research Institute of Anhui Province, Hefei, China
| | - Huadong Wang
- Anhui Provincial Center for Disease Control and Prevention, Hefei, China.; Public Health Research Institute of Anhui Province, Hefei, China
| | - Yinguang Fan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China.; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Hong Su
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China.; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Zhirong Liu
- Anhui Provincial Center for Disease Control and Prevention, Hefei, China.; Public Health Research Institute of Anhui Province, Hefei, China..
| | - Jian Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China.; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China..
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12
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Zhou Y, Jing J, Zhang Z, Pan Y, Cai X, Zhu W, Li Z, Liu C, Liu H, Meng X, Cheng J, Wang Y, Li H, Wang S, Niu H, Wen W, Sachdev PS, Wei T, Liu T, Wang Y. Disrupted pattern of rich-club organization in structural brain network from prediabetes to diabetes: A population-based study. Hum Brain Mapp 2024; 45:e26598. [PMID: 38339955 PMCID: PMC10839741 DOI: 10.1002/hbm.26598] [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: 04/28/2023] [Revised: 12/22/2023] [Accepted: 01/04/2024] [Indexed: 02/12/2024] Open
Abstract
The network nature of the brain is gradually becoming a consensus in the neuroscience field. A set of highly connected regions in the brain network called "rich-club" are crucial high efficiency communication hubs in the brain. The abnormal rich-club organization can reflect underlying abnormal brain function and metabolism, which receives increasing attention. Diabetes is one of the risk factors for neurological diseases, and most individuals with prediabetes will develop overt diabetes within their lifetime. However, the gradual impact of hyperglycemia on brain structures, including rich-club organization, remains unclear. We hypothesized that the brain follows a special disrupted pattern of rich-club organization in prediabetes and diabetes. We used cross-sectional baseline data from the population-based PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events (PRECISE) study, which included 2218 participants with a mean age of 61.3 ± 6.6 years and 54.1% females comprising 1205 prediabetes, 504 diabetes, and 509 normal control subjects. The rich-club organization and network properties of the structural networks derived from diffusion tensor imaging data were investigated using a graph theory approach. Linear mixed models were used to assess associations between rich-club organization disruptions and the subjects' glucose status. Based on the graphical analysis methods, we observed the disrupted pattern of rich-club organization was from peripheral regions mainly located in frontal areas to rich-club regions mainly located in subcortical areas from prediabetes to diabetes. The rich-club organization disruptions were associated with elevated glucose levels. These findings provided more details of the process by which hyperglycemia affects the brain, contributing to a better understanding of the potential neurological consequences. Furthermore, the disrupted pattern observed in rich-club organization may serve as a potential neuroimaging marker for early detection and monitoring of neurological disorders in individuals with prediabetes or diabetes.
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Affiliation(s)
- Yijun Zhou
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Jing Jing
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Zhe Zhang
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Yuesong Pan
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Xueli Cai
- Department of Neurology, Lishui HospitalZhejiang University School of MedicineLishuiZhejiangChina
| | - Wanlin Zhu
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Zixiao Li
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Chang Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Hao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Xia Meng
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Jian Cheng
- School of Computer Science and Engineering, Beihang UniversityBeijingChina
| | - Yilong Wang
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Hao Li
- China National Clinical Research Center for Neurological DiseasesBeijingChina
| | - Suying Wang
- Cerebrovascular Research Lab, Lishui Hospital, Zhejiang University School of MedicineLishuiZhejiangChina
| | - Haijun Niu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Wei Wen
- Division of Psychiatry and Mental Health, Faculty of Medicine and Health, Centre for Healthy Brain Ageing (CHeBA)UNSWSydneyNew South WalesAustralia
- Neuropsychiatric Institute, Prince of Wales HospitalSydneyNew South WalesAustralia
| | - Perminder S. Sachdev
- Division of Psychiatry and Mental Health, Faculty of Medicine and Health, Centre for Healthy Brain Ageing (CHeBA)UNSWSydneyNew South WalesAustralia
- Neuropsychiatric Institute, Prince of Wales HospitalSydneyNew South WalesAustralia
| | - Tiemin Wei
- Department of Cardiology, Lishui HospitalZhejiang University School of MedicineZhejiangChina
| | - Tao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Yongjun Wang
- Department of Neurology, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
- China National Clinical Research Center for Neurological DiseasesBeijingChina
- Research Unit of Artificial Intelligence in Cerebrovascular DiseaseChinese Academy of Medical Sciences, 2019RU018BeijingChina
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Shi Y, Luo S, Wang H, Yao Q, Shi Y, Cheng J. Three-dimensional bone remodelling of glenoid fossa in patients with skeletal Class III malocclusion after bimaxillary orthognathic surgery. Int J Oral Maxillofac Surg 2024; 53:133-140. [PMID: 37442687 DOI: 10.1016/j.ijom.2023.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 06/20/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023]
Abstract
This study aimed to characterize three-dimensional quantitative morphological changes of glenoid fossa in patients with skeletal Class III malocclusion treated with bimaxillary orthognathic surgery. Ninety-five eligible patients (50 male, 45 female; mean age 22.09 years) were enrolled retrospectively. Cone beam computed tomography obtained at 1 week preoperatively (T0), immediately after surgery (T1), and at ≥ 12 months postoperatively (T2) were registered based on cranial base using voxel-based registration in 3D Slicer. Glenoid fossa surface was divided spatially into four regions, and bone modelling in these regions was visualized with color maps. Our data revealed that the mean surface variations of glenoid fossa were small, with modest bone formation as a whole. No significant associations between anteroposterior or vertical mandibular displacement and overall glenoid fossa remodeling were found (P > 0.05). Moreover, bone deposition was frequently observed in the anterior-lateral region of glenoid fossa in patients with a larger mandibular movement during T0-T1 (P < 0.001). Paired bone formation in the anterior-lateral region of glenoid fossa and bone resorption in the anterior-lateral region of condylar head was frequently observed. Collectively, our results revealed that glenoid fossa underwent complex but modest bone remodeling after bimaxillary surgery in skeletal Class III patients.
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Affiliation(s)
- Y Shi
- Department of Oral and Maxillofacial Surgery, The Affiliated Stomatological Hospital, Nanjing Medical University, Nanjing, PR China; Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Jiangsu, PR China
| | - S Luo
- Department of Oral and Maxillofacial Surgery, The Affiliated Stomatological Hospital, Nanjing Medical University, Nanjing, PR China; Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Jiangsu, PR China; Jiangsu Key Laboratory of Oral Disease, Nanjing Medical University, Jiangsu, PR China
| | - H Wang
- Department of Oral and Maxillofacial Surgery, The Affiliated Stomatological Hospital, Nanjing Medical University, Nanjing, PR China
| | - Q Yao
- Department of Oral and Maxillofacial Surgery, The Affiliated Stomatological Hospital, Nanjing Medical University, Nanjing, PR China
| | - Y Shi
- Department of Oral and Maxillofacial Surgery, The Affiliated Stomatological Hospital, Nanjing Medical University, Nanjing, PR China
| | - J Cheng
- Department of Oral and Maxillofacial Surgery, The Affiliated Stomatological Hospital, Nanjing Medical University, Nanjing, PR China; Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Jiangsu, PR China.
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Cai F, Li D, Xie Y, Wang X, Ma H, Xu H, Cheng J, Zhuang H, Hua ZC. Sulfide:quinone oxidoreductase alleviates ferroptosis in acute kidney injury via ameliorating mitochondrial dysfunction of renal tubular epithelial cells. Redox Biol 2024; 69:102973. [PMID: 38052107 PMCID: PMC10746537 DOI: 10.1016/j.redox.2023.102973] [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: 09/20/2023] [Revised: 11/23/2023] [Accepted: 11/23/2023] [Indexed: 12/07/2023] Open
Abstract
Ferroptosis is iron-dependent and regulates necrosis caused by lipid peroxidation and mitochondrial damage. Recent evidence has revealed an emerging role for ferroptosis in the pathophysiology of acute kidney injury (AKI). Sulfide:quinone oxidoreductase (SQOR) is a mitochondrial inner membrane protein highly expressed in the renal cortex. However, the effects of SQOR on ferroptosis and AKI have not been elucidated. In this study, we evaluated the effects of SQOR in several AKI models. We observed a rapid decrease in SQOR expression after cisplatin stimulation in both in vivo and in vitro models. SQOR-deletion mice exhibit exacerbated kidney impairment and ferroptosis in renal tubular epithelial cells following cisplatin injury. Additionally, our results showed that the overexpression of SQOR or ADT-OH (the slow-releasing H2S donor) preserved renal function in the three AKI mouse models. These effects were evidenced by lower levels of serum creatinine (SCr), blood urea nitrogen (BUN), renal neutrophil gelatinase-associated lipocalin (NGAL), and kidney injury molecule 1 (KIM-1). Importantly, SQOR knockout significantly aggravates cisplatin-induced ferroptosis by promoting mitochondrial dysfunction in renal tubular epithelial cells (RTECs). Moreover, online database analysis combined with our study revealed that SYVN1, an upregulated E3 ubiquitin ligase, may mediate the ubiquitin-mediated degradation of SQOR in AKI. Consequently, our results suggest that SYVN1-mediated ubiquitination degradation of SQOR may induce mitochondrial dysfunction in RTECs, exacerbating ferroptosis and thereby promoting the occurrence and development of AKI. Hence, targeting the SYVN1-SQOR axis could be a potential therapeutic strategy for AKI treatment.
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Affiliation(s)
- Fangfang Cai
- The State Key Laboratory of Pharmaceutical Biotechnology, College of Life Sciences, Nanjing University, Nanjing, PR China; School of Biopharmacy, China Pharmaceutical University, Nanjing, PR China
| | - Dangran Li
- The State Key Laboratory of Pharmaceutical Biotechnology, College of Life Sciences, Nanjing University, Nanjing, PR China
| | - Yawen Xie
- The State Key Laboratory of Pharmaceutical Biotechnology, College of Life Sciences, Nanjing University, Nanjing, PR China
| | - Xiaoyang Wang
- The State Key Laboratory of Pharmaceutical Biotechnology, College of Life Sciences, Nanjing University, Nanjing, PR China
| | - Hailin Ma
- The State Key Laboratory of Pharmaceutical Biotechnology, College of Life Sciences, Nanjing University, Nanjing, PR China
| | - Huangru Xu
- The State Key Laboratory of Pharmaceutical Biotechnology, College of Life Sciences, Nanjing University, Nanjing, PR China
| | - Jian Cheng
- Jiangsu Key Laboratory of Neuropsychiatric Diseases & Institute of Neuroscience, Soochow University, Suzhou, PR China.
| | - Hongqin Zhuang
- The State Key Laboratory of Pharmaceutical Biotechnology, College of Life Sciences, Nanjing University, Nanjing, PR China.
| | - Zi-Chun Hua
- The State Key Laboratory of Pharmaceutical Biotechnology, College of Life Sciences, Nanjing University, Nanjing, PR China; School of Biopharmacy, China Pharmaceutical University, Nanjing, PR China; Changzhou High-Tech Research Institute of Nanjing University and Jiangsu TargetPharma Laboratories Inc., Changzhou 213164, PR China; Faculty of Pharmaceutical Sciences, Xinxiang Medical University, Xinxiang, PR China.
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15
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Han Y, Li B, Cheng J, Zhou D, Yuan X, Zhao W, Zhang D, Zhang J. Construction of methylation driver gene-related prognostic signature and development of a new prognostic stratification strategy in neuroblastoma. Genes Genomics 2024; 46:171-185. [PMID: 38180715 DOI: 10.1007/s13258-023-01483-6] [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: 06/21/2023] [Accepted: 12/17/2023] [Indexed: 01/06/2024]
Abstract
BACKGROUND Aberrant DNA methylation is one of the major epigenetic alterations in neuroblastoma. OBJECTIVE Exploring the prognostic significance of methylation driver genes in neuroblastoma could help to comprehensively assess patient prognosis. METHODS After identifying methylation driver genes (MDGs), we used the LASSO algorithm and stepwise Cox regression to construct methylation driver gene-related risk score (MDGRS), and evaluated its predictive performance by multiple methods. By combining risk grouping and MDGRS grouping, we developed a new prognostic stratification strategy and explored the intrinsic differences between the different groupings. RESULTS We identified 44 stably expressed MDGs in neuroblastoma. MDGRS showed superior predictive performance in both internal and external cohorts and was strongly correlated with immune-related scores. MDGRS can be an independent prognostic factor for neuroblastoma, and we constructed the nomogram to facilitate clinical application. Based on the new prognostic stratification strategy, we divided the patients into three groups and found significant differences in overall prognosis, clinical characteristics, and immune infiltration between the different subgroups. CONCLUSION MDGRS was an accurate and promising tool to facilitate comprehensive pre-treatment assessment. And the new prognostic stratification strategy could be helpful for clinical decision making.
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Affiliation(s)
- Yahui Han
- Department of Pediatric Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Biyun Li
- Department of Pediatric Hematology Oncology, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Jian Cheng
- Department of Pediatric Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Diming Zhou
- Department of Pediatric Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Xiafei Yuan
- Department of Pediatric Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Wei Zhao
- Department of Pediatric Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Da Zhang
- Department of Pediatric Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Jiao Zhang
- Department of Pediatric Surgery, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
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16
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Pan R, Song J, Yi W, Liu J, Song R, Li X, Liu L, Yuan J, Wei N, Cheng J, Huang Y, Zhang X, Su H. Heatwave characteristics complicate the association between PM 2.5 components and schizophrenia hospitalizations in a changing climate: Leveraging of the individual residential environment. Ecotoxicol Environ Saf 2024; 271:115973. [PMID: 38219619 DOI: 10.1016/j.ecoenv.2024.115973] [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: 11/05/2023] [Revised: 01/07/2024] [Accepted: 01/08/2024] [Indexed: 01/16/2024]
Abstract
BACKGROUND In the era characterized by global environmental and climatic changes, understanding the effects of PM2.5 components and heatwaves on schizophrenia (SCZ) is essential for implementing environmental interventions at the population level. However, research in this area remains limited, which highlights the need for further research and effort. We aim to assess the association between exposure to PM2.5 components and hospitalizations for SCZ under different heatwave characteristics. METHODS We conducted a 16 municipalities-wide, individual exposure-based, time-stratified, case-crossover study from January 1, 2017, to December 31, 2020, encompassing 160736 hospitalizations in Anhui Province, China. Daily concentrations of PM2.5 components were obtained from the Tracking Air Pollution in China dataset. Conditional logistic regression models were used to investigate the association between PM2.5 components and hospitalizations. Additionally, restricted cubic spline models were used to identify protective thresholds of residential environment in response to environmental and climate change. RESULTS Our findings indicate a positive correlation between PM2.5 and its components and hospitalizations. Significantly, a 1 μg/m3 increase in black carbon (BC) was associated with the highest risk, at 1.58% (95%CI: 0.95-2.25). Exposure to heatwaves synergistically enhanced the impact of PM2.5 components on hospitalization risks, and the interaction varied with the intensity and duration of heatwaves. Under the 99th percentile heatwave events, the impact of PM2.5 and its components on hospitalizations was most pronounced, which were PM2.5 (2-4d: 4.59%, 5.09%, and 5.09%), sulfate (2-4d: 21.73%, 23.23%, and 25.25%), nitrate (2-4d: 17.51%, 16.93%, and 20.31%), ammonium (2-4d: 27.49%, 31.03%, and 32.41%), organic matter (2-4d: 32.07%, 25.42%, and 24.48%), and BC (2-4d: 259.36%, 288.21%, and 152.52%), respectively. Encouragingly, a protective effect was observed when green and blue spaces comprised more than 17.6% of the residential environment. DISCUSSION PM2.5 components and heatwave exposure were positively associated with an increased risk of hospitalizations, although green and blue spaces provided a mitigating effect.
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Affiliation(s)
- Rubing Pan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Jian Song
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Weizhuo Yi
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Jintao Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Rong Song
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Xuanxuan Li
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Li Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Jiajun Yuan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Ning Wei
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Jian Cheng
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China
| | - Yuee Huang
- Department of Epidemiology and Health Statistics, School of Public Health, Wannan Medical College, 241002 Wuhu, Anhui, China
| | - Xulai Zhang
- Anhui Mental Health Center (Affiliated Psychological Hospital of Anhui Medical University), Hefei, Anhui, China
| | - Hong Su
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China; Center for Big Data and Population Health of IHM, Hefei, Anhui, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China.
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Zhang M, Cheng J, Shen Z, He K, Zheng B. Red light-triggered release of ROS and carbon monoxide for combinational antibacterial application. J Mater Chem B 2024; 12:1077-1086. [PMID: 38168810 DOI: 10.1039/d3tb01829f] [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: 01/05/2024]
Abstract
The abuse of antibiotics has led to the emergence of a wide range of drug-resistant bacteria. To address the challenge of drug-resistant bacterial infections and related infectious diseases, several effective antibacterial strategies have been developed. To achieve enhanced therapeutic effects, combinational treatment approaches should be employed. With this in mind, a metal-organic framework (MOF) based nanoreactor with integrated photodynamic therapy (PDT) and gas therapy which can release reactive oxygen species (ROS) and carbon monoxide (CO) under red light irradiation has been developed. The release of ROS and CO under red light irradiation exerts a preferential antibacterial effect on Gram-positive/Gram-negative bacteria. The bactericidal effects of ROS and CO on Staphylococcus aureus (S. aureus) and methicillin-resistant S. aureus (MRSA) are better than ROS only, showing a combinational antibacterial effect. Furthermore, the fluorescence emission properties of porphyrin moieties can be leveraged for real-time tracking and imaging of the nanoreactors. The simple preparation procedures of this material further enhance its potential as a versatile and effective antibacterial candidate, thereby presenting a new strategy for PDT and gas combinational treatment.
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Affiliation(s)
- Mengdan Zhang
- Department of Polymer Science and Engineering, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Jian Cheng
- Department of Polymer Science and Engineering, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Zhiqiang Shen
- Department of Polymer Science and Engineering, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Kewu He
- Imaging Center of the Third Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230031, China.
| | - Bin Zheng
- School of Chemistry and Pharmaceutical Engineering, Hefei Normal University, Hefei, Anhui, 230061, China.
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Cheng J, Li Z, Liu Y, Li C, Huang X, Tian Y, Shen F. [Bioinformatics analysis and validation of the interaction between PML protein and TAB1 protein]. Nan Fang Yi Ke Da Xue Xue Bao 2024; 44:179-186. [PMID: 38293990 PMCID: PMC10878890 DOI: 10.12122/j.issn.1673-4254.2024.01.21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Indexed: 02/01/2024]
Abstract
OBJECTIVE To analyze the interaction between PML protein and TAB1 protein using bioinformatic approaches and experimentally verify the results. METHODS Using Rosetta software, a 3D model of TAB1 protein was constructed through a comparative modeling approach; the secondary structure of PML protein was retrieved in the PDB database and its crystal structure and 3D structure were resolved. Zdock 3.0.2 software was used to perform protein-protein docking of PML and TAB1, and the best conformation was extracted for molecular structure analysis of the docking model. The interaction between the two proteins was detected using immunoprecipitation in α-MMC-treated M1 inflammatory macrophages. RESULTS When 6IMQ of PML was used as the docking site, PML protein formed 3 salt bridges, 6 hydrogen bonds and 6 hydrophobic interactions with TAB1 proteins; when 5YUF of PML was used as the docking site, PML protein formed 1 hydrogen bond, 3 electrostatic interactions and 9 hydrophobic interactions with TAB1 proteins, and both of the docking modes formed good molecular docking and interactions. In the M1 inflammatory macrophages treated with α-MMC for 4 h, positive protein bands of PML and TAB1 were detected in the cell lysates in PML-IP group. CONCLUSION PML protein can interact strongly with TAB1 protein.
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Affiliation(s)
- J Cheng
- School of Laboratory Medicine, Chengdu Medical College, Chengdu 610500, China
| | - Z Li
- School of Laboratory Medicine, Chengdu Medical College, Chengdu 610500, China
| | - Y Liu
- School of Laboratory Medicine, Chengdu Medical College, Chengdu 610500, China
| | - C Li
- School of Pharmacy, Chengdu Medical College, Chengdu 610500, China
| | - X Huang
- School of Laboratory Medicine, Chengdu Medical College, Chengdu 610500, China
| | - Y Tian
- School of Laboratory Medicine, Chengdu Medical College, Chengdu 610500, China
| | - F Shen
- School of Laboratory Medicine, Chengdu Medical College, Chengdu 610500, China
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Xiao M, Zhang P, Chen Z, Liu X, Wei W, He Z, Wang Y, Cheng J, Zhu Z, Wen J, Yang H. Adenosine diphosphate ribosylation factor 6 inhibition protects burn sepsis induced lung injury through preserving vascular integrity and suppressing ASC inflammasome transmission. Burns 2024:S0305-4179(24)00010-X. [PMID: 38267288 DOI: 10.1016/j.burns.2024.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 12/27/2023] [Accepted: 01/10/2024] [Indexed: 01/26/2024]
Abstract
BACKGROUND Severe burns are devastating injuries with significant immune dysfunction and result in substantial mortality and morbidity due to sepsis induced organ failure. Acute lung injury is the most common type of organ injury in sepsis, however, the mechanisms of which are poorly understood and effective therapeutic measures are limited. This study is aimed to investigate the effect of a small Guanosine triphosphatase (GTPase), Adenosine diphosphate ribosylation factor 6 (ARF6), on burn sepsis induced lung injury, and discuss the possible mechanisms. METHODS Burn sepsis was established in male C57BL/6 mice. Mice were anesthetised by intramuscular injection of ketamine and xylazine hydrochloride, then 30% TBSA full thickness burn followed by sub-eschar injection of lipopolysaccharide. Animals were treated with intraperitoneal injection of a small molecule inhibitor of ARF6: NAV-2729, or vehicle, right after the burn and sepsis stimuli were inflicted. Lung tissues were harvested for histopathological observation and the acute lung injury scores were calculated. Organ permeability, Vascular Endothelial Cadherin (VE-cadherin) expression, inflammatory cytokine levels and myeloperoxidase activity in lung tissues were detected. Rat pulmonary microvascular endothelial cells (PMVECs) were stimulated by burn sepsis serum with or without 10 μM NAV-2729. The ARF6 activation, VE-cadherin expression, inflammasome activity, adapter protein apoptosis speck-like protein containing a caspase recruiting domain (ASC) specks and cytokines secretion were determined. Student's t test was used for comparison between two groups. Multiple comparisons among groups were performed by using analysis of variance, with Tukey's test for the post hoc test. RESULTS NAV-2729 treatment attenuated burn sepsis induced lung injury and promoted survival of burn septic mice by preserving VE-cadherin expression in endothelial cell adherent junction and limited vascular hyperpermeability in lung tissues. Moreover, inflammatory cytokine expression and inflammatory injury in lung tissues were alleviated. Mechanistically, NAV-2729 enhanced vascular integrity by inhibiting ARF6 activation and restoring VE-cadherin expression in PMVECs. In addition, NAV-2729 inhibited ARF6-dependent phagocytosis of ASC specks, thus preventing inflammation propagation mediated by cell-to-cell transmission of ASC specks. CONCLUSIONS ARF6 inhibition preserved vascular integrity by restoring expression of VE-cadherin and suppressed the spread of inflammation by affecting phagocytosis of ASC specks, thus protected against sepsis induced lung injury and improve survival of burn septic animals. The findings of this study implied potential therapeutics by which ARF6 inhibition can protect lung function from septic induced lung injury and improve outcomes in burn sepsis.
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Affiliation(s)
- Mengjing Xiao
- Department of Burn Plastic and Cosmetic Surgery, South China Hospital Affiliated to Shenzhen University, No. 1, Fuxin Road, Longgang District, Shenzhen 518111, PR China.
| | - Peirong Zhang
- Inpatient Ward 1, Songhe Nursing Home, 3 Yuenan Street, Huangsha Avenue, Liwan District, Guangzhou 510145, PR China.
| | - Zimiao Chen
- Department of Burn Plastic and Cosmetic Surgery, South China Hospital Affiliated to Shenzhen University, No. 1, Fuxin Road, Longgang District, Shenzhen 518111, PR China.
| | - Xiaojie Liu
- Department of Burn Plastic and Cosmetic Surgery, South China Hospital Affiliated to Shenzhen University, No. 1, Fuxin Road, Longgang District, Shenzhen 518111, PR China.
| | - Wei Wei
- Department of Burn Plastic and Cosmetic Surgery, South China Hospital Affiliated to Shenzhen University, No. 1, Fuxin Road, Longgang District, Shenzhen 518111, PR China.
| | - Zhihao He
- Department of Burn Plastic and Cosmetic Surgery, South China Hospital Affiliated to Shenzhen University, No. 1, Fuxin Road, Longgang District, Shenzhen 518111, PR China.
| | - Yao Wang
- Department of Burn Plastic and Cosmetic Surgery, South China Hospital Affiliated to Shenzhen University, No. 1, Fuxin Road, Longgang District, Shenzhen 518111, PR China.
| | - Jian Cheng
- Department of Burn Plastic and Cosmetic Surgery, South China Hospital Affiliated to Shenzhen University, No. 1, Fuxin Road, Longgang District, Shenzhen 518111, PR China.
| | - Zhen Zhu
- Department of Burn Plastic and Cosmetic Surgery, South China Hospital Affiliated to Shenzhen University, No. 1, Fuxin Road, Longgang District, Shenzhen 518111, PR China.
| | - Jing Wen
- Department of Burn Plastic and Cosmetic Surgery, South China Hospital Affiliated to Shenzhen University, No. 1, Fuxin Road, Longgang District, Shenzhen 518111, PR China.
| | - Hongming Yang
- Department of Burn Plastic and Cosmetic Surgery, South China Hospital Affiliated to Shenzhen University, No. 1, Fuxin Road, Longgang District, Shenzhen 518111, PR China.
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Song J, Pan T, Xu Z, Yi W, Pan R, Cheng J, Hu W, Su H. A systematic analysis of chronic kidney disease burden attributable to lead exposure based on the global burden of disease study 2019. Sci Total Environ 2024; 908:168189. [PMID: 37907111 DOI: 10.1016/j.scitotenv.2023.168189] [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: 08/07/2023] [Revised: 10/07/2023] [Accepted: 10/27/2023] [Indexed: 11/02/2023]
Abstract
AIM As an important toxic heavy metal, lead exposure can lead to the occurrence of chronic kidney disease (CKD). However, the analysis of its disease burden pattern on a global scale is lacking. This study aimed to analyze the CKD burden attributable to lead exposure globally, regionally and temporally, as well as to examine the role of socio-economic factors. METHOD This study used data from the Global Burden of Disease (GBD) study 2019. We obtained the global burden of CKD caused by lead exposure between 1990 and 2019, and stratified this burden according to factors such as gender, age, GBD regions, and countries. From 1990 to 2019, the changing trend of the disease burden of CKD attributed to lead exposure was estimated using Joinpoint regression model with the average annual percent change (AAPC) estimated. Finally, the relationship between country-level socio-economic factors and lead exposure related CKD burden was explored using a panel data model analysis. RESULTS In 2019, worldwide, there were 52.94 thousand deaths (95 % uncertainty interval (UI): 31.64, 76.23) and 1225.2 thousand disability-adjusted life years (DALYs) (95 % UI: 707.88, 1818) of CKD caused by lead exposure, accounting for 3.71 % of total CKD deaths and 2.95 % of total CKD DALYs. The age-standardized death and DALY rates per 100,000 population were 0.68 (95 % UI: 0.40, 0.98) and 15.02 (95 % UI: 8.68, 22.26) respectively, indicating an upward trend and stable trend between 1990 and 2019. However, the age-standardized rates attributed to lead exposure showed a wide variability across regions, with the highest rates in Central Latin America and the lowest in Eastern Europe. Moreover, the results of panel model analysis indicated that GDP growth was positively associated with lead exposure related CKD death rate and DALY rate. However, there were inverse associations between life expectancy at birth and hospital beds (per 1000 people) with lead exposure-related CKD DALY rate. CONCLUSION In summary, a significant burden of CKD can be attributed to lead exposure, with noticeable regional discrepancies. Findings here are valuable to deploy efficient measures at curbing lead exposure worldwide.
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Affiliation(s)
- Jian Song
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No.81 Meishan road, Shushan District, Hefei, Anhui 230031, China; Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Australia
| | - TianRong Pan
- Department of Endocrinology, The Second Affiliated Hospital of Anhui Medical University, No. 678 Furong Road, Jingkai District, Hefei 230061, Anhui Province, China; Research Center for Translational Medicine, The Second Affiliated Hospital of Anhui Medical University, No. 678 Furong Road, Jingkai District, Hefei 230061, Anhui Province, China
| | - Zhiwei Xu
- School of Medicine and Dentistry, Griffith University, Gold Coast Campus, QLD 4222, Australia
| | - Weizhuo Yi
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No.81 Meishan road, Shushan District, Hefei, Anhui 230031, China; School of Medicine and Dentistry, Griffith University, Gold Coast Campus, QLD 4222, Australia
| | - Rubing Pan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No.81 Meishan road, Shushan District, Hefei, Anhui 230031, China
| | - Jian Cheng
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No.81 Meishan road, Shushan District, Hefei, Anhui 230031, China
| | - Wenbiao Hu
- Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Australia.
| | - Hong Su
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, China; Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No.81 Meishan road, Shushan District, Hefei, Anhui 230031, China.
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Feng G, Chen R, Zhao R, Li Y, Ma L, Wang Y, Men W, Gao J, Tan S, Cheng J, He Y, Qin S, Dong Q, Tao S, Shu N. Author Correction: Longitudinal development of the human white matter structural connectome and its association with brain transcriptomic and cellular architecture. Commun Biol 2024; 7:85. [PMID: 38212455 PMCID: PMC10784457 DOI: 10.1038/s42003-024-05771-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2024] Open
Affiliation(s)
- Guozheng Feng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- BABRI Centre, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Rui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Rui Zhao
- College of Life Sciences, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Gene Resource and Molecular Development, Beijing, China
| | - Yuanyuan Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Leilei Ma
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Jiahong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Jian Cheng
- School of Computer Science and Engineering, Beihang University, Beijing, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
- BABRI Centre, Beijing Normal University, Beijing, China.
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
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Zhang R, Chen X, Miao C, Chen Y, Li Y, Shen J, Yuan M, Chen M, Cheng J, Liu S, Sun Q, Wu J. Tumor-associated macrophage-derived exosomal miR-513b-5p is a target of jianpi yangzheng decoction for inhibiting gastric cancer. J Ethnopharmacol 2024; 318:117013. [PMID: 37572927 DOI: 10.1016/j.jep.2023.117013] [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: 01/22/2023] [Revised: 07/16/2023] [Accepted: 08/07/2023] [Indexed: 08/14/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Jianpi Yangzheng decoction (JPYZ) possesses a potential anti-tumor activity in gastric cancer. However, potential effect of JPYZ on regulating tumor-associated macrophage (TAM)-derived exosomes to affect gastric cancer is still unclear. AIM OF STUDY We aimed to clarify the role of tumor-associated macrophage derived exosomes (TAM-exos) in invasive and metastasis of gastric cancer and the mechanism of JPYZ regulate TAM-exos against gastric cancer. MATERIALS AND METHODS Flow cytometry was performed to demonstrate whether JPYZ involved in TAM polarization. After JPYZ treatment, TAM conditioned medium (TAM-CM)/TAM-exos were co-cultured with gastric cancer cells and were detected by wound healing and transwell assay. Transcriptome sequencing and bioinformatics analysis predicted the exosomal miRNA after JPYZ intervention in TAM. miRNA mimic and inhibitor were used to verify the effect of miRNA in exosomes on gastric cancer cells. Q-PCR and luciferase reporter assay were employed to clarify the targeting relationship between miRNA and target gene. Western blot assay detected the expression levels of epithelial-mesenchymal transition (EMT) markers and related signaling pathways proteins. RESULTS We firstly demonstrated that TAM-CM intervened by JPYZ significantly inhibited the invasion and migration of gastric cancer. Furthermore, exosomes in TAM supernatants play a key role in migration of gastric cancer. Meanwhile, transcriptome sequencing and q-PCR revealed that miR-513b-5p expression was significantly reduced in TAM-exos intervened by JPYZ. And miR-513b-5p in TAM aggravated TAM-exos mediated invasion and migration of gastric cancer cells, the inhibitor of miR-513b-5p reversed TAM-exos mediated promotion. Bioinformatics analysis and luciferase reporter assay confirmed that PTEN was a direct target of miR-513b-5p in gastric cancer. MiR-513b-5p inhibited PTEN to activate AKT/mTOR signaling pathway thus promoting gastric cancer invasion and metastasis in vivo and in vitro. Importantly, JPYZ inhibited TAM derived exosomal miR-513b-5p, and alleviated AKT/mTOR activation by PTEN depended manner in gastric cancer. CONCLUSION TAM-exos containing miR-513b-5p lead to gastric cancer invasion and migration. Our findings clarify a novel TAM-exos mechanism of JPYZ for inhibiting gastric cancer progression.
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Affiliation(s)
- Ruijuan Zhang
- Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210029, China; No.1 Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210023, China
| | - Xu Chen
- Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210029, China; No.1 Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210023, China
| | - Chunrun Miao
- Department of Gastroenterology, Dongtai Hospital of Traditional Chinese Medicine, Dongtai, Jiangsu, 224299, China
| | - Yuxuan Chen
- Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210029, China; No.1 Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210023, China
| | - Yaqi Li
- Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210029, China; No.1 Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210023, China
| | - Junyu Shen
- Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210029, China; No.1 Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210023, China
| | - Mengyun Yuan
- Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210029, China; No.1 Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210023, China
| | - Menglin Chen
- Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210029, China; No.1 Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210023, China
| | - Jian Cheng
- BD Bioscience, Becton, Dickinson and Company, Shanghai, 201200, China
| | - Shenlin Liu
- Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210029, China
| | - Qingmin Sun
- Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210029, China.
| | - Jian Wu
- Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210029, China.
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Sheng B, Chen X, Cheng J, Zhang Y, Xie SSQ, Tao J, Duan C. A novel scoring approach for the Wolf Motor Function Test in stroke survivors using motion-sensing technology and machine learning: A preliminary study. Comput Methods Programs Biomed 2024; 243:107887. [PMID: 37913714 DOI: 10.1016/j.cmpb.2023.107887] [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: 12/08/2022] [Revised: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 11/03/2023]
Abstract
BACKGROUND AND OBJECTIVE Human-administered clinical scales, such as the Functional Ability Scale of the Wolf Motor Function Test (WMFT-FAS), are widely utilized to evaluate upper-limb motor function in stroke survivors. However, these scales are generally subjective and labor-intensive. To end this, we proposed a novel scoring approach for the motor function assessment. METHODS The proposed novel scoring approach mainly contained one Microsoft Kinect v2, one customized motion tracking system, and one customized intelligent scoring system. Specifically, the Kinect v2 was used to capture stroke survivors' functional movements, the motion tracking system was developed for recording the gathered movement data, and the intelligent scoring system (kernel: feed-forward neural network, FFNN) was developed to evaluate movement quality and provide corresponding WMFT-FAS scores. Several methods have been applied to enhance the approach's usability, such as singular spectrum analysis and multi-ReliefF method. RESULTS Sixteen stroke survivors and ten healthy subjects were recruited for validation. Inspiring results of the proposed approach were achieved when compared with the clinical scores provided by a physiotherapist: 0.924 ± 0.027 for accuracy, 0.875 ± 0.063 for F1-score, 0.915 ± 0.051 for sensitivity, 0.969 ± 0.013 for specificity, 0.952 ± 0.038 for AUC, 0.098 ± 0.037 for mean absolute error, and 0.214 ± 0.078 for root mean squared error. CONCLUSIONS The results indicate that the proposed novel scoring approach can provide objective and accurate assessment scores, which can help physiotherapists make individualized treatment decisions.
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Affiliation(s)
- Bo Sheng
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China
| | - Xiaohui Chen
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China
| | - Jian Cheng
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China
| | - Yanxin Zhang
- Department of Exercise Sciences, The University of Auckland, Auckland, 1010, New Zealand
| | - Shane Sheng Quan Xie
- School of Electronic and Electrical Engineering, University of Leeds, Leeds, LS2 9JT, United Kingdom
| | - Jing Tao
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China
| | - Chaoqun Duan
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China.
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Ni H, Liu C, Kong L, Zhai L, Chen J, Liu Q, Chen Z, Wu M, Chen J, Guo Y, Bai W, Zhang D, Xia K, Huang G, Pan S, Liao B, Ma K, Zhang LK, Cheng J, Guan YQ. Preparation of injectable porcine skin-derived collagen and its application in delaying skin aging by promoting the adhesion and chemotaxis of skin fibroblasts. Int J Biol Macromol 2023; 253:126718. [PMID: 37673166 DOI: 10.1016/j.ijbiomac.2023.126718] [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: 06/12/2023] [Revised: 08/18/2023] [Accepted: 09/03/2023] [Indexed: 09/08/2023]
Abstract
Collagen, as the main component of human skin, plays a vital role in maintaining dermal integrity. Its loss will lead to dermis destruction and collapse, resulting in skin aging. At present, injection of exogenous collagen is an important means to delay skin aging. In this study, high-purity collagen was extracted from porcine skin. Our research revealed that it can effectively promote the adhesion and chemotaxis of HSF cells. It can also reduce the expression of β-galactosidase, decrease ROS levels, and increase the expression of the collagen precursors, p53 and p16 in HSF cells during senescence. After local injection into the aging skin of rats, it was found that the number of cells and type I collagen fibers in the dermis increased significantly, and the arrangement of these fibers became more uniform and orderly. Moreover, the important thing is that it is biocompatible. To sum up, the porcine skin collagen we extracted is an anti-aging biomaterial with application potential.
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Affiliation(s)
- He Ni
- School of Life Science, South China Normal University, Guangzhou 510631, China; Chongqing Fanghe Biotechnology Co., LTD, Chongqing 400000, China
| | - Chao Liu
- School of Life Science, South China Normal University, Guangzhou 510631, China
| | - Lili Kong
- Chongqing Fanghe Biotechnology Co., LTD, Chongqing 400000, China
| | - Limin Zhai
- School of Life Science, South China Normal University, Guangzhou 510631, China
| | - Jiapeng Chen
- School of Life Science, South China Normal University, Guangzhou 510631, China
| | - Qingpeng Liu
- School of Life Science, South China Normal University, Guangzhou 510631, China
| | - Zhendong Chen
- School of Life Science, South China Normal University, Guangzhou 510631, China
| | - Mengdie Wu
- School of Life Science, South China Normal University, Guangzhou 510631, China
| | - Jie Chen
- School of Life Science, South China Normal University, Guangzhou 510631, China
| | - Yiyan Guo
- School of Life Science, South China Normal University, Guangzhou 510631, China
| | - Weiwei Bai
- School of Life Science, South China Normal University, Guangzhou 510631, China
| | - Dandan Zhang
- School of Life Science, South China Normal University, Guangzhou 510631, China
| | - Kunwen Xia
- School of Life Science, South China Normal University, Guangzhou 510631, China
| | - Guowei Huang
- School of Life Science, South China Normal University, Guangzhou 510631, China
| | - Shengjun Pan
- School of Life Science, South China Normal University, Guangzhou 510631, China
| | - Beining Liao
- School of Life Science, South China Normal University, Guangzhou 510631, China
| | - Kuo Ma
- School of Life Science, South China Normal University, Guangzhou 510631, China
| | - Ling-Kun Zhang
- School of Life Science, South China Normal University, Guangzhou 510631, China; South China Normal University-Panyu Central Hospital Joint Laboratory of Translational Medical Research, Panyu Central Hospital, Guangzhou 511400, China; MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, Guangdong Provincial Key Laboratory of Laser Life Science, Guangzhou Key Laboratory of Spectral Analysis and Functional Probes, College of Biophotonics, South China Normal University, Guangzhou 510631, China.
| | - Jian Cheng
- Chongqing Fanghe Biotechnology Co., LTD, Chongqing 400000, China.
| | - Yan-Qing Guan
- School of Life Science, South China Normal University, Guangzhou 510631, China; South China Normal University-Panyu Central Hospital Joint Laboratory of Translational Medical Research, Panyu Central Hospital, Guangzhou 511400, China; MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, Guangdong Provincial Key Laboratory of Laser Life Science, Guangzhou Key Laboratory of Spectral Analysis and Functional Probes, College of Biophotonics, South China Normal University, Guangzhou 510631, China.
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25
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Zhang M, Cheng J, Shen Z, Lin P, Ding S, Hu J. A Single-Component Dual Donor Enables Ultrasound-Triggered Co-release of Carbon Monoxide and Hydrogen Sulfide. Angew Chem Int Ed Engl 2023; 62:e202314563. [PMID: 37964723 DOI: 10.1002/anie.202314563] [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: 09/28/2023] [Revised: 10/30/2023] [Accepted: 11/15/2023] [Indexed: 11/16/2023]
Abstract
The development of dual gasotransmitter donors can not only provide robust tools to investigate their subtle interplay under pathophysiological conditions but also optimize therapeutic efficacy. While conventional strategies are heavily dependent on multicomponent donors, we herein report an ultrasound-responsive water-soluble copolymer (PSHF) capable of releasing carbon monoxide (CO) and hydrogen sulfide (H2 S) based on single-component sulfur-substituted 3-hydroxyflavone (SHF) derivatives. Interestingly, sulfur substitution can not only greatly improve the ultrasound sensitivity but also enable the co-release of CO/H2 S under mild ultrasound irradiation. The co-release of CO/H2 S gasotransmitters exerts a bactericidal effect against Staphylococcus aureus and demonstrates anti-inflammatory activity in lipopolysaccharide-challenged macrophages. Moreover, the excellent tissue penetration of ultrasound irradiation enables the local release of CO/H2 S in the joints of septic arthritis rats, exhibiting superior therapeutic efficacy without the need for any antibiotics.
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Affiliation(s)
- Mengdan Zhang
- Department of Pharmacy, The First Affiliated Hospital of University of Science and Technology of China (USTC), and Key Laboratory of Precision and Intelligent Chemistry, Department of Polymer Science and Engineering, University of Science and Technology of China, Hefei, Anhui Province, 230026, China
| | - Jian Cheng
- Department of Pharmacy, The First Affiliated Hospital of University of Science and Technology of China (USTC), and Key Laboratory of Precision and Intelligent Chemistry, Department of Polymer Science and Engineering, University of Science and Technology of China, Hefei, Anhui Province, 230026, China
| | - Zhiqiang Shen
- Department of Pharmacy, The First Affiliated Hospital of University of Science and Technology of China (USTC), and Key Laboratory of Precision and Intelligent Chemistry, Department of Polymer Science and Engineering, University of Science and Technology of China, Hefei, Anhui Province, 230026, China
| | - Paiyu Lin
- Department of Pediatrics, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230022, China
| | - Shenggang Ding
- Department of Pediatrics, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230022, China
| | - Jinming Hu
- Department of Pharmacy, The First Affiliated Hospital of University of Science and Technology of China (USTC), and Key Laboratory of Precision and Intelligent Chemistry, Department of Polymer Science and Engineering, University of Science and Technology of China, Hefei, Anhui Province, 230026, China
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Chen G, Zhao L, Cheng J, Chen M, Wang J, Ding W, Lei H. Prediction of Nanoscale Water Meniscus Shape between Deliquescent KDP Crystal Optics and AFM Probe for Water-Dissolution Repairing. Langmuir 2023; 39:18548-18557. [PMID: 38054931 DOI: 10.1021/acs.langmuir.3c02889] [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] [Indexed: 12/07/2023]
Abstract
KDP (KH2PO4) crystal optics are the key elements for megajoule laser facilities. Nanoscale surface defects would cause laser-induced damage when the optics are irradiated by a high-fluence laser (over 10 J/cm2). Dip-pen nanolithography (DPN) could be used to repair the nanoscale surface defects in the KDP optics by the water meniscus. The high humidity required for high-efficiency and soft KDP surfaces penetrated by the AFM probe brings challenges for accurately predicting the water meniscus shape to evaluate the effectiveness of the DPN water-dissolution repairing. The multisolutions of the Young-Laplace and Kelvin equations also lead to the wrong water meniscus shape. A theoretical model that takes the high humidity and the penetration of the AFM probes into account is developed. The parametrization Young-Laplace equations are adopted for the zero contact angle of the water films, and the AFM probe is treated as the combination of the cone and sphere for the water meniscus whose size is larger than the AFM tip radius under high humidity. The penetration of the AFM probe is modeled by Hertz theory. Both the water films (3.3 nm thickness at 99% relative humidity) and indentations (1.46 nm depth at 300 nN contact force) are non-negligible for the nanoscale water meniscus between the KDP surface and the AFM probe. Moreover, the rough-fine two-step method is proposed to lock the correct solution of the Young-Laplace and Kelvin equations. The effectiveness of the proposed model is verified by comparison with reported ESEM images and pull-off forces. In addition, the overgrowth dots on the KDP surface are compared with the water meniscus. The linear growth of the water meniscus would cause the linear growth of the overgrowth dot, which proves the proposed model could be used to guide the DPN water-dissolution repairing for the nanoscale surface defects in the KDP optics.
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Affiliation(s)
- Guang Chen
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China
| | - Linjie Zhao
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China
| | - Jian Cheng
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China
| | - Mingjun Chen
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China
| | - Jinghe Wang
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China
| | - Wenyu Ding
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China
| | - Hongqin Lei
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China
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Hu Y, Liu C, Li K, Cheng J, Zhang Z, Han E. An Efficient Laser Decontamination Process Based on Non-Radioactive Specimens of Nuclear Power Materials. Materials (Basel) 2023; 16:7643. [PMID: 38138785 PMCID: PMC10745034 DOI: 10.3390/ma16247643] [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: 11/12/2023] [Revised: 12/09/2023] [Accepted: 12/12/2023] [Indexed: 12/24/2023]
Abstract
Nuclear power components contain radioactivity on their surfaces after long-term service, which can be harmful to personnel and the environment during maintenance, dismantling, and decommissioning. In this experiment, laser decontamination technology is utilized to remove radioactivity from their surfaces. In order to meet the actual needs, a laser decontamination process without spot overlapping has been studied. Under the same equipment conditions, the decontamination efficiency of the non-spot overlapping process is 10 times higher than that of the spot overlapping process. Alloy 690 is used as the test substrate, and non-radioactive specimens are prepared by simulating primary-circuit hydrochemical conditions. The surface morphology, elemental composition, and phase composition of the specimens before and after laser decontamination are investigated with SEM and XRD using the single-pulse experiment and power single-factor experiment methods, and the laser decontamination effect was evaluated. The results show that the decontamination efficiency reached 10.8 m2/h under the conditions of a pulse width of 500 ns, a laser repetition frequency of 40 kHz, a scanning speed of 15,000 mm/s, and a line spacing of 0.2 mm, according to which the removal effect was achieved when the laser power was 160 W and the oxygen content on the surface was 6.29%; additionally, there were no oxide phases in the XRD spectra after decontamination. Therefore, the laser cleaning process without spot overlap can provide reference for future practical operations to achieve efficient removal of radioactivity from nuclear power components.
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Affiliation(s)
- Yang Hu
- Key Laboratory for Anisotropy and Texture of Materials (Ministry of Education), School of Materials Science and Engineering, Northeastern University, Shenyang 110819, China; (Y.H.); (K.L.)
- Institute of Corrosion Science and Technology, Guangzhou 510530, China; (Z.Z.); (E.H.)
| | - Changsheng Liu
- Key Laboratory for Anisotropy and Texture of Materials (Ministry of Education), School of Materials Science and Engineering, Northeastern University, Shenyang 110819, China; (Y.H.); (K.L.)
| | - Kangte Li
- Key Laboratory for Anisotropy and Texture of Materials (Ministry of Education), School of Materials Science and Engineering, Northeastern University, Shenyang 110819, China; (Y.H.); (K.L.)
| | - Jian Cheng
- Laser Group, School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China;
| | - Zhiming Zhang
- Institute of Corrosion Science and Technology, Guangzhou 510530, China; (Z.Z.); (E.H.)
| | - Enhou Han
- Institute of Corrosion Science and Technology, Guangzhou 510530, China; (Z.Z.); (E.H.)
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28
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Lim KS, Cheng J, Tuggle C, Dyck M, Canada P, Fortin F, Harding J, Plastow G, Dekkers J. Genetic analysis of the blood transcriptome of young healthy pigs to improve disease resilience. Genet Sel Evol 2023; 55:90. [PMID: 38087235 PMCID: PMC10714454 DOI: 10.1186/s12711-023-00860-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 11/22/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Disease resilience is the ability of an animal to maintain productive performance under disease conditions and is an important selection target. In pig breeding programs, disease resilience must be evaluated on selection candidates without exposing them to disease. To identify potential genetic indicators for disease resilience that can be measured on selection candidates, we focused on the blood transcriptome of 1594 young healthy pigs with subsequent records on disease resilience. Transcriptome data were obtained by 3'mRNA sequencing and genotype data were from a 650 K genotyping array. RESULTS Heritabilities of the expression of 16,545 genes were estimated, of which 5665 genes showed significant estimates of heritability (p < 0.05), ranging from 0.05 to 0.90, with or without accounting for white blood cell composition. Genes with heritable expression levels were spread across chromosomes, but were enriched in the swine leukocyte antigen region (average estimate > 0.2). The correlation of heritability estimates with the corresponding estimates obtained for genes expressed in human blood was weak but a sizable number of genes with heritable expression levels overlapped. Genes with heritable expression levels were significantly enriched for biological processes such as cell activation, immune system process, stress response, and leukocyte activation, and were involved in various disease annotations such as RNA virus infection, including SARS-Cov2, as well as liver disease, and inflammation. To estimate genetic correlations with disease resilience, 3205 genotyped pigs, including the 1594 pigs with transcriptome data, were evaluated for disease resilience following their exposure to a natural polymicrobial disease challenge. Significant genetic correlations (p < 0.05) were observed with all resilience phenotypes, although few exceeded expected false discovery rates. Enrichment analysis of genes ranked by estimates of genetic correlations with resilience phenotypes revealed significance for biological processes such as regulation of cytokines, including interleukins and interferons, and chaperone mediated protein folding. CONCLUSIONS These results suggest that expression levels in the blood of young healthy pigs for genes in biological pathways related to immunity and endoplasmic reticulum stress have potential to be used as genetic indicator traits to select for disease resilience.
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Affiliation(s)
- Kyu-Sang Lim
- Department of Animal Science, Iowa State University, Ames, IA, USA
- Department of Animal Resource Science, Kongju National University, Yesan, Chungnam, Republic of Korea
| | - Jian Cheng
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | | | - Michael Dyck
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - PigGen Canada
- PigGen Canada Research Consortium, Guelph, ON, Canada
| | - Frederic Fortin
- Centre de Développement du Porc du Québec Inc. (CDPQ), Québec City, QC, Canada
| | - John Harding
- Department of Large Animal Clinical Sciences, University of Saskatchewan, Saskatoon, SK, Canada
| | - Graham Plastow
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - Jack Dekkers
- Department of Animal Science, Iowa State University, Ames, IA, USA.
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Feng G, Chen R, Zhao R, Li Y, Ma L, Wang Y, Men W, Gao J, Tan S, Cheng J, He Y, Qin S, Dong Q, Tao S, Shu N. Longitudinal development of the human white matter structural connectome and its association with brain transcriptomic and cellular architecture. Commun Biol 2023; 6:1257. [PMID: 38087047 PMCID: PMC10716168 DOI: 10.1038/s42003-023-05647-8] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023] Open
Abstract
From childhood to adolescence, the spatiotemporal development pattern of the human brain white matter connectome and its underlying transcriptomic and cellular mechanisms remain largely unknown. With a longitudinal diffusion MRI cohort of 604 participants, we map the developmental trajectory of the white matter connectome from global to regional levels and identify that most brain network properties followed a linear developmental trajectory. Importantly, connectome-transcriptomic analysis reveals that the spatial development pattern of white matter connectome is potentially regulated by the transcriptomic architecture, with positively correlated genes involve in ion transport- and development-related pathways expressed in excitatory and inhibitory neurons, and negatively correlated genes enriches in synapse- and development-related pathways expressed in astrocytes, inhibitory neurons and microglia. Additionally, the macroscale developmental pattern is also associated with myelin content and thicknesses of specific laminas. These findings offer insights into the underlying genetics and neural mechanisms of macroscale white matter connectome development from childhood to adolescence.
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Affiliation(s)
- Guozheng Feng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- BABRI Centre, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Rui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Rui Zhao
- College of Life Sciences, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Gene Resource and Molecular Development, Beijing, China
| | - Yuanyuan Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Leilei Ma
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Jiahong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Jian Cheng
- School of Computer Science and Engineering, Beihang University, Beijing, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
- BABRI Centre, Beijing Normal University, Beijing, China.
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
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Shi G, Wen L, Zhang S, Cheng J, Chen X, Zhou Y, Xu Z, Xin B. Facile manufacture of high-purity CuSO 4 from waste Cu-containing paint residue using combined processes of H 2SO 4 leaching and extraction stripping. Water Sci Technol 2023; 88:2974-2985. [PMID: 38096082 PMCID: wst_2023_388 DOI: 10.2166/wst.2023.388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Waste copper-containing paint residue (WCPR) represents a typical hazardous waste containing both toxic organic substances and toxic heavy metals, but there are few reports on the recycling of heavy metals. The recovery of Cu from WCPR by H2SO4 leaching-extraction-stripping has the advantages of eco-friendliness, simplicity of operation, and high value-added product. The results show that under the optimal conditions, the leaching rate of Cu in WCPR is 94.31% (18.02 g/L), while the extraction and stripping rates of Cu in the leaching solution are 99.46 and 95.32%, respectively. Due to the high concentration of Cu2+ with fewer impurities in the stripping solution, the stripping solution is heated, evaporated, cooled, and crystallized to successfully produce high-purity dark blue CuSO4 crystal, accomplishing the high-value recycling of Cu in WCPR. In addition, the leach residue of WCPR contains acrylic resin and SiO2, which can be used in cement kilns for incineration, thus realizing the overall recycling and utilization of WCPR.
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Affiliation(s)
- Gongchu Shi
- School of Material Science and Engineering, Beijing Institute of Technology, Beijing 100081, China E-mail:
| | - Lingkai Wen
- School of Material Science and Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Shihao Zhang
- School of Material Science and Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Jian Cheng
- School of Material Science and Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Xiaohui Chen
- School of Material Science and Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Yanyu Zhou
- School of Material Science and Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Zhikai Xu
- School of Material Science and Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Baoping Xin
- School of Material Science and Engineering, Beijing Institute of Technology, Beijing 100081, China; Tangshan Research Institute, Beijing Institute of Technology, Tangshan 063000, China
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Wang Y, Cheng J, Chen Y, Shao S, Zhu L, Wu Z, Liu T, Zhu H. FVP: Fourier Visual Prompting for Source-Free Unsupervised Domain Adaptation of Medical Image Segmentation. IEEE Trans Med Imaging 2023; 42:3738-3751. [PMID: 37590107 DOI: 10.1109/tmi.2023.3306105] [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] [Indexed: 08/19/2023]
Abstract
Medical image segmentation methods normally perform poorly when there is a domain shift between training and testing data. Unsupervised Domain Adaptation (UDA) addresses the domain shift problem by training the model using both labeled data from the source domain and unlabeled data from the target domain. Source-Free UDA (SFUDA) was recently proposed for UDA without requiring the source data during the adaptation, due to data privacy or data transmission issues, which normally adapts the pre-trained deep model in the testing stage. However, in real clinical scenarios of medical image segmentation, the trained model is normally frozen in the testing stage. In this paper, we propose Fourier Visual Prompting (FVP) for SFUDA of medical image segmentation. Inspired by prompting learning in natural language processing, FVP steers the frozen pre-trained model to perform well in the target domain by adding a visual prompt to the input target data. In FVP, the visual prompt is parameterized using only a small amount of low-frequency learnable parameters in the input frequency space, and is learned by minimizing the segmentation loss between the predicted segmentation of the prompted target image and reliable pseudo segmentation label of the target image under the frozen model. To our knowledge, FVP is the first work to apply visual prompts to SFUDA for medical image segmentation. The proposed FVP is validated using three public datasets, and experiments demonstrate that FVP yields better segmentation results, compared with various existing methods.
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Zhai C, Bai L, Xu Y, Liu Y, Sun H, Gong X, Yu G, Zong Q, Hu W, Wang F, Cheng J, Zou Y. Temperature variability associated with respiratory disease hospitalisations, hospital stays and hospital expenses the warm temperate sub-humid monsoon climate. Public Health 2023; 225:206-217. [PMID: 37939462 DOI: 10.1016/j.puhe.2023.10.013] [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: 05/19/2023] [Revised: 08/25/2023] [Accepted: 10/06/2023] [Indexed: 11/10/2023]
Abstract
OBJECTIVES The abrupt change of climate has led to an increasing trend of hospitalised patients in recent years. This study aimed to analyse the temperature variability (TV) associated with respiratory disease (RD) hospitalisations, hospital stays and hospital expenses. STUDY DESIGN The generalized linear model combined with distributed lag non-linear model was used to investigate the association between TV and RD hospitalisations. METHODS TV was determined by measuring the standard deviation of maximum and minimum temperatures for the current day and the previous 7 days. RD hospitalisations data were obtained from three major tertiary hospitals in Huaibei City, namely, the Huaibei People's Hospital, the Huaibei Hospital Of Traditional Chinese Medicine and the Huaibei Maternal and Child Health Care Hospital. First, using a time series decomposition model, the seasonality and long-term trend of hospitalisations, hospital stays and hospital expenses for RD were explored in this warm temperate sub-humid monsoon climate. Second, robust models were used to analyse the association between TV and RD hospitalisations, hospital stays and hospital expenses. In addition, this study stratified results by sex, age and season. Third, using the attributable fraction (AF) and attributable number (AN), hospitalisations, hospital stays and hospital expenses for RD attributed to TV were quantified. RESULTS Overall, 0.013% of hospitalisations were attributed to TV0-1 (i.e. TV at the current day and previous 1 day), corresponding to 220 cases, 1603 days of hospital stays and 1,308,000 RMB of hospital expenses. Females were more susceptible to TV than males, and the risk increased with longer exposure (the highest risk was seen at TV0-7 [i.e. TV at the current day and previous 7 days] exposure). Higher AF and AN were observed at ages 0-5 years and ≥65 years. In addition, it was also found that TV was more strongly linked to RD in the cool season. The hot season was positively associated with hospital stays and hospital expenses at TV0-3 to TV0-7 exposure. CONCLUSIONS Exposure to TV increased the risk of hospitalisations, longer hospital stays and higher hospital expenses for RD. The findings suggested that more attention should be paid to unstable weather conditions in the future to protect the health of vulnerable populations.
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Affiliation(s)
- Chunxia Zhai
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Liangliang Bai
- School of Biomedical Engineering, Anhui Medical University, Hefei, Anhui, China
| | - Ying Xu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Yuqi Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Hongyu Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - XingYu Gong
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Guanghui Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Qiqun Zong
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Wanqin Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Fang Wang
- Department of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Jian Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China
| | - Yanfeng Zou
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China.
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Yang D, Zhao L, Cheng J, Chen M, Liu H, Wang J, Han C, Sun Y. Unveiling sub-bandgap energy-level structures on machined optical surfaces based on weak photo-luminescence. Nanoscale 2023; 15:18250-18264. [PMID: 37800341 DOI: 10.1039/d3nr03488g] [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] [Indexed: 10/07/2023]
Abstract
Sub-bandgap defect energy levels (SDELs) introduced by the point defects located in surface defect areas are considered the main factors in decreasing laser-induced damage thresholds (LIDTs). The suppression of SDELs could greatly increase LIDTs. However, no available method could detect SDELs, limiting the characterization and suppression of SDELs. Herein, a self-designed photo-luminescence detection system is developed to explore the weak transient-steady photo-luminescence properties of machined surfaces. Based on the excitation laser wavelength dependence of photo-luminescence properties, a sub-bandgap energy-level structure (SELS) containing SDELs is unveiled for the first time. Based on the developed mathematical model for predicting LIDTs, the feasibility of the detection method was verified. In summary, this work provides a novel approach to characterize SDELs on machined surfaces. This work could construct electronic structures and explore the transition behaviors of electrons, which is vital to laser-induced damage. Besides, this work could predict the LIDTs of the machined surfaces based on their PL properties, which provides convenience for evaluating the LIDTs of various optical elements in industrial production. Moreover, this work provides a convenient method for raising the LIDTs of various optical elements through monitoring and suppressing the SDELs on machined surfaces.
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Affiliation(s)
- Dinghuai Yang
- State Key Laboratory of Robotics and System, School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China.
| | - Linjie Zhao
- State Key Laboratory of Robotics and System, School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China.
| | - Jian Cheng
- State Key Laboratory of Robotics and System, School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China.
| | - Mingjun Chen
- State Key Laboratory of Robotics and System, School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China.
| | - Henan Liu
- State Key Laboratory of Robotics and System, School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China.
| | - Jinghe Wang
- Center for Precision Engineering, School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China
| | - Chengshun Han
- State Key Laboratory of Robotics and System, School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China.
| | - Yazhou Sun
- State Key Laboratory of Robotics and System, School of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China.
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Li J, Cheng J, Yang L, Niu Q, Zhang Y, Palaniyappan L. Association of cortical gyrification, white matter microstructure, and phenotypic profile in medication-naïve obsessive-compulsive disorder. Psychol Med 2023:1-7. [PMID: 37994452 DOI: 10.1017/s0033291723003422] [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] [Indexed: 11/24/2023]
Abstract
BACKGROUND Obsessive-compulsive disorder (OCD) is thought to arise from dysconnectivity among interlinked brain regions resulting in a wide spectrum of clinical manifestations. Cortical gyrification, a key morphological feature of human cerebral cortex, has been considered associated with developmental connectivity in early life. Monitoring cortical gyrification alterations may provide new insights into the developmental pathogenesis of OCD. METHODS Sixty-two medication-naive patients with OCD and 59 healthy controls (HCs) were included in this study. Local gyrification index (LGI) was extracted from T1-weighted MRI data to identify the gyrification changes in OCD. Total distortion (splay, bend, or twist of fibers) was calculated using diffusion-weighted MRI data to examine the changes in white matter microstructure in patients with OCD. RESULTS Compared with HCs, patients with OCD showed significantly increased LGI in bilateral medial frontal gyrus and the right precuneus, where the mean LGI was positively correlated with anxiety score. Patients with OCD also showed significantly decreased total distortion in the body, genu, and splenium of the corpus callosum (CC), where the average distortion was negatively correlated with anxiety scores. Intriguingly, the mean LGI of the affected cortical regions was significantly correlated with the mean distortion of the affected white matter tracts in patients with OCD. CONCLUSIONS We demonstrated associations among increased LGI, aberrant white matter geometry, and higher anxiety in patients with OCD. Our findings indicate that developmental dysconnectivity-driven alterations in cortical folding are one of the neural substrates underlying the clinical manifestations of OCD.
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Affiliation(s)
- Jianyu Li
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jian Cheng
- School of Computer Science and Engineering, Beihang University, Beijing, China
| | - Lei Yang
- Department of Psychiatry, The First Affiliated Hospital, Zhengzhou University, Zhengzhou, China
| | - Qihui Niu
- Department of Psychiatry, The First Affiliated Hospital, Zhengzhou University, Zhengzhou, China
| | - Yuanchao Zhang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Lena Palaniyappan
- Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
- Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
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Cheng J, Gan G, Zheng S, Zhang G, Zhu C, Liu S, Hu J. Biofilm heterogeneity-adaptive photoredox catalysis enables red light-triggered nitric oxide release for combating drug-resistant infections. Nat Commun 2023; 14:7510. [PMID: 37980361 PMCID: PMC10657346 DOI: 10.1038/s41467-023-43415-8] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 11/09/2023] [Indexed: 11/20/2023] Open
Abstract
The formation of biofilms is closely associated with persistent and chronic infections, and physiological heterogeneity such as pH and oxygen gradients renders biofilms highly resistant to conventional antibiotics. To date, effectively treating biofilm infections remains a significant challenge. Herein, we report the fabrication of micellar nanoparticles adapted to heterogeneous biofilm microenvironments, enabling nitric oxide (NO) release through two distinct photoredox catalysis mechanisms. The key design feature involves the use of tertiary amine (TA) moieties, which function as sacrificial agents to avoid the quenching of photocatalysts under normoxic and neutral pH conditions and proton acceptors at acidic pH to allow deep biofilm penetration. This biofilm-adaptive NO-releasing platform shows excellent antibiofilm activity against ciprofloxacin-resistant Pseudomonas aeruginosa (CRPA) biofilms both in vitro and in a mouse skin infection model, providing a strategy for combating biofilm heterogeneity and biofilm-related infections.
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Affiliation(s)
- Jian Cheng
- Department of Orthopedics, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui Province, 230001, China
| | - Guihai Gan
- Department of Pharmacy, The First Affiliated Hospital of University of Science and Technology of China (USTC), and Key Laboratory of Precision and Intelligent Chemistry, Department of Polymer Science and Engineering, University of Science and Technology of China, Hefei, Anhui Province, 230026, China
| | - Shaoqiu Zheng
- Department of Pharmacy, The First Affiliated Hospital of University of Science and Technology of China (USTC), and Key Laboratory of Precision and Intelligent Chemistry, Department of Polymer Science and Engineering, University of Science and Technology of China, Hefei, Anhui Province, 230026, China
| | - Guoying Zhang
- Department of Pharmacy, The First Affiliated Hospital of University of Science and Technology of China (USTC), and Key Laboratory of Precision and Intelligent Chemistry, Department of Polymer Science and Engineering, University of Science and Technology of China, Hefei, Anhui Province, 230026, China
| | - Chen Zhu
- Department of Orthopedics, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui Province, 230001, China.
| | - Shiyong Liu
- Department of Pharmacy, The First Affiliated Hospital of University of Science and Technology of China (USTC), and Key Laboratory of Precision and Intelligent Chemistry, Department of Polymer Science and Engineering, University of Science and Technology of China, Hefei, Anhui Province, 230026, China.
| | - Jinming Hu
- Department of Pharmacy, The First Affiliated Hospital of University of Science and Technology of China (USTC), and Key Laboratory of Precision and Intelligent Chemistry, Department of Polymer Science and Engineering, University of Science and Technology of China, Hefei, Anhui Province, 230026, China.
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Abdulhamid MI, Aboona BE, Adam J, Adams JR, Agakishiev G, Aggarwal I, Aggarwal MM, Ahammed Z, Aitbaev A, Alekseev I, Anderson DM, Aparin A, Aslam S, Atchison J, Averichev GS, Bairathi V, Baker W, Cap JGB, Barish K, Bhagat P, Bhasin A, Bhatta S, Bordyuzhin IG, Brandenburg JD, Brandin AV, Cai XZ, Caines H, Sánchez MCDLB, Cebra D, Ceska J, Chakaberia I, Chan BK, Chang Z, Chatterjee A, Chen D, Chen J, Chen JH, Chen Z, Cheng J, Cheng Y, Choudhury S, Christie W, Chu X, Crawford HJ, Dale-Gau G, Das A, Daugherity M, Dedovich TG, Deppner IM, Derevschikov AA, Dhamija A, Di Carlo L, Dixit P, Dong X, Drachenberg JL, Duckworth E, Dunlop JC, Engelage J, Eppley G, Esumi S, Evdokimov O, Ewigleben A, Eyser O, Fatemi R, Fazio S, Feng CJ, Feng Y, Finch E, Fisyak Y, Flor FA, Fu C, Gao T, Geurts F, Ghimire N, Gibson A, Gopal K, Gou X, Grosnick D, Gupta A, Hamed A, Han Y, Harasty MD, Harris JW, Harrison-Smith H, He W, He XH, He Y, Hu C, Hu Q, Hu Y, Huang H, Huang HZ, Huang SL, Huang T, Huang X, Huang Y, Huang Y, Humanic TJ, Isenhower D, Isshiki M, Jacobs WW, Jalotra A, Jena C, Ji Y, Jia J, Jin C, Ju X, Judd EG, Kabana S, Kabir ML, Kalinkin D, Kang K, Kapukchyan D, Kauder K, Keane D, Kechechyan A, Kelsey M, Kimelman B, Kiselev A, Knospe AG, Ko HS, Kochenda L, Korobitsin AA, Kravtsov P, Kumar L, Kumar S, Elayavalli RK, Lacey R, Landgraf JM, Lebedev A, Lednicky R, Lee JH, Leung YH, Lewis N, Li C, Li W, Li X, Li Y, Li Y, Li Z, Liang X, Liang Y, Lin T, Liu C, Liu F, Liu G, Liu H, Liu H, Liu L, Liu T, Liu X, Liu Y, Liu Z, Ljubicic T, Llope WJ, Lomicky O, Longacre RS, Loyd EM, Lu T, Lukow NS, Luo XF, Luong VB, Ma L, Ma R, Ma YG, Magdy N, Mallick D, Margetis S, Matis HS, Mazer JA, McNamara G, Mi K, Minaev NG, Mohanty B, Mondal MM, Mooney I, Morozov DA, Mudrokh A, Nagy MI, Nain AS, Nam JD, Nasim M, Neff D, Nelson JM, Nemes DB, Nie M, Nigmatkulov G, Niida T, Nishitani R, Nogach LV, Nonaka T, Odyniec G, Ogawa A, Oh S, Okorokov VA, Okubo K, Page BS, Pak R, Pan J, Pandav A, Pandey AK, Panebratsev Y, Pani T, Parfenov P, Paul A, Perkins C, Pokhrel BR, Posik M, Protzman T, Pruthi NK, Putschke J, Qin Z, Qiu H, Quintero A, Racz C, Radhakrishnan SK, Raha N, Ray RL, Ritter HG, Robertson CW, Rogachevsky OV, Aguilar MAR, Roy D, Ruan L, Sahoo AK, Sahoo NR, Sako H, Salur S, Samigullin E, Sato S, Schmidke WB, Schmitz N, Seger J, Seto R, Seyboth P, Shah N, Shahaliev E, Shanmuganathan PV, Shao T, Sharma M, Sharma N, Sharma R, Sharma SR, Sheikh AI, Shen D, Shen DY, Shen K, Shi SS, Shi Y, Shou QY, Si F, Singh J, Singha S, Sinha P, Skoby MJ, Söhngen Y, Song Y, Srivastava B, Stanislaus TDS, Stewart DJ, Strikhanov M, Stringfellow B, Su Y, Sun C, Sun X, Sun Y, Sun Y, Surrow B, Svirida DN, Sweger ZW, Tamis A, Tang AH, Tang Z, Taranenko A, Tarnowsky T, Thomas JH, Tlusty D, Todoroki T, Tokarev MV, Tomkiel CA, Trentalange S, Tribble RE, Tribedy P, Tsai OD, Tsang CY, Tu Z, Tyler J, Ullrich T, Underwood DG, Upsal I, Van Buren G, Vasiliev AN, Verkest V, Videbæk F, Vokal S, Voloshin SA, Wang F, Wang G, Wang JS, Wang J, Wang X, Wang Y, Wang Y, Wang Y, Wang Z, Webb JC, Weidenkaff PC, Westfall GD, Wieman H, Wilks G, Wissink SW, Wu J, Wu J, Wu X, Wu X, Wu Y, Xi B, Xiao ZG, Xie G, Xie W, Xu H, Xu N, Xu QH, Xu Y, Xu Y, Xu Z, Xu Z, Yan G, Yan Z, Yang C, Yang Q, Yang S, Yang Y, Ye Z, Ye Z, Yi L, Yip K, Yu Y, Zha W, Zhang C, Zhang D, Zhang J, Zhang S, Zhang W, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhang Y, Zhang ZJ, Zhang Z, Zhang Z, Zhao F, Zhao J, Zhao M, Zhou C, Zhou J, Zhou S, Zhou Y, Zhu X, Zurek M, Zyzak M. Hyperon Polarization along the Beam Direction Relative to the Second and Third Harmonic Event Planes in Isobar Collisions at sqrt[s_{NN}]=200 GeV. Phys Rev Lett 2023; 131:202301. [PMID: 38039468 DOI: 10.1103/physrevlett.131.202301] [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: 03/16/2023] [Revised: 07/07/2023] [Accepted: 10/03/2023] [Indexed: 12/03/2023]
Abstract
The polarization of Λ and Λ[over ¯] hyperons along the beam direction has been measured relative to the second and third harmonic event planes in isobar Ru+Ru and Zr+Zr collisions at sqrt[s_{NN}]=200 GeV. This is the first experimental evidence of the hyperon polarization by the triangular flow originating from the initial density fluctuations. The amplitudes of the sine modulation for the second and third harmonic results are comparable in magnitude, increase from central to peripheral collisions, and show a mild p_{T} dependence. The azimuthal angle dependence of the polarization follows the vorticity pattern expected due to elliptic and triangular anisotropic flow, and qualitatively disagrees with most hydrodynamic model calculations based on thermal vorticity and shear induced contributions. The model results based on one of existing implementations of the shear contribution lead to a correct azimuthal angle dependence, but predict centrality and p_{T} dependence that still disagree with experimental measurements. Thus, our results provide stringent constraints on the thermal vorticity and shear-induced contributions to hyperon polarization. Comparison to previous measurements at RHIC and the LHC for the second-order harmonic results shows little dependence on the collision system size and collision energy.
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Affiliation(s)
| | - B E Aboona
- Texas A&M University, College Station, Texas 77843
| | - J Adam
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - J R Adams
- The Ohio State University, Columbus, Ohio 43210
| | - G Agakishiev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - I Aggarwal
- Panjab University, Chandigarh 160014, India
| | | | - Z Ahammed
- Variable Energy Cyclotron Centre, Kolkata 700064, India
| | - A Aitbaev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - I Alekseev
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute," Moscow 117218
- National Research Nuclear University MEPhI, Moscow 115409
| | - D M Anderson
- Texas A&M University, College Station, Texas 77843
| | - A Aparin
- Joint Institute for Nuclear Research, Dubna 141 980
| | - S Aslam
- Indian Institute Technology, Patna, Bihar 801106, India
| | - J Atchison
- Abilene Christian University, Abilene, Texas 79699
| | | | - V Bairathi
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
| | - W Baker
- University of California, Riverside, California 92521
| | | | - K Barish
- University of California, Riverside, California 92521
| | - P Bhagat
- University of Jammu, Jammu 180001, India
| | - A Bhasin
- University of Jammu, Jammu 180001, India
| | - S Bhatta
- State University of New York, Stony Brook, New York 11794
| | - I G Bordyuzhin
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute," Moscow 117218
| | | | - A V Brandin
- National Research Nuclear University MEPhI, Moscow 115409
| | - X Z Cai
- Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800
| | - H Caines
- Yale University, New Haven, Connecticut 06520
| | | | - D Cebra
- University of California, Davis, California 95616
| | - J Ceska
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - I Chakaberia
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - B K Chan
- University of California, Los Angeles, California 90095
| | - Z Chang
- Indiana University, Bloomington, Indiana 47408
| | - A Chatterjee
- National Institute of Technology Durgapur, Durgapur-713209, India
| | - D Chen
- University of California, Riverside, California 92521
| | - J Chen
- Shandong University, Qingdao, Shandong 266237
| | - J H Chen
- Fudan University, Shanghai, 200433
| | - Z Chen
- Shandong University, Qingdao, Shandong 266237
| | - J Cheng
- Tsinghua University, Beijing 100084
| | - Y Cheng
- University of California, Los Angeles, California 90095
| | | | - W Christie
- Brookhaven National Laboratory, Upton, New York 11973
| | - X Chu
- Brookhaven National Laboratory, Upton, New York 11973
| | - H J Crawford
- University of California, Berkeley, California 94720
| | - G Dale-Gau
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - A Das
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
| | - M Daugherity
- Abilene Christian University, Abilene, Texas 79699
| | - T G Dedovich
- Joint Institute for Nuclear Research, Dubna 141 980
| | - I M Deppner
- University of Heidelberg, Heidelberg 69120, Germany
| | - A A Derevschikov
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - A Dhamija
- Panjab University, Chandigarh 160014, India
| | - L Di Carlo
- Wayne State University, Detroit, Michigan 48201
| | - P Dixit
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - X Dong
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | | | | | - J C Dunlop
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Engelage
- University of California, Berkeley, California 94720
| | - G Eppley
- Rice University, Houston, Texas 77251
| | - S Esumi
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - O Evdokimov
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - A Ewigleben
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - O Eyser
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Fatemi
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - S Fazio
- University of Calabria & INFN-Cosenza, Rende 87036, Italy
| | - C J Feng
- National Cheng Kung University, Tainan 70101
| | - Y Feng
- Purdue University, West Lafayette, Indiana 47907
| | - E Finch
- Southern Connecticut State University, New Haven, Connecticut 06515
| | - Y Fisyak
- Brookhaven National Laboratory, Upton, New York 11973
| | - F A Flor
- Yale University, New Haven, Connecticut 06520
| | - C Fu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - T Gao
- Shandong University, Qingdao, Shandong 266237
| | - F Geurts
- Rice University, Houston, Texas 77251
| | - N Ghimire
- Temple University, Philadelphia, Pennsylvania 19122
| | - A Gibson
- Valparaiso University, Valparaiso, Indiana 46383
| | - K Gopal
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - X Gou
- Shandong University, Qingdao, Shandong 266237
| | - D Grosnick
- Valparaiso University, Valparaiso, Indiana 46383
| | - A Gupta
- University of Jammu, Jammu 180001, India
| | - A Hamed
- American University in Cairo, New Cairo 11835, Egypt
| | - Y Han
- Rice University, Houston, Texas 77251
| | - M D Harasty
- University of California, Davis, California 95616
| | - J W Harris
- Yale University, New Haven, Connecticut 06520
| | | | - W He
- Fudan University, Shanghai, 200433
| | - X H He
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y He
- Shandong University, Qingdao, Shandong 266237
| | - C Hu
- University of Chinese Academy of Sciences, Beijing 101408
| | - Q Hu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Hu
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - H Huang
- National Cheng Kung University, Tainan 70101
| | - H Z Huang
- University of California, Los Angeles, California 90095
| | - S L Huang
- State University of New York, Stony Brook, New York 11794
| | - T Huang
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - X Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Tsinghua University, Beijing 100084
| | - Y Huang
- Central China Normal University, Wuhan, Hubei 430079
| | - T J Humanic
- The Ohio State University, Columbus, Ohio 43210
| | - D Isenhower
- Abilene Christian University, Abilene, Texas 79699
| | - M Isshiki
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - W W Jacobs
- Indiana University, Bloomington, Indiana 47408
| | - A Jalotra
- University of Jammu, Jammu 180001, India
| | - C Jena
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - Y Ji
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - J Jia
- Brookhaven National Laboratory, Upton, New York 11973
- State University of New York, Stony Brook, New York 11794
| | - C Jin
- Rice University, Houston, Texas 77251
| | - X Ju
- University of Science and Technology of China, Hefei, Anhui 230026
| | - E G Judd
- University of California, Berkeley, California 94720
| | - S Kabana
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile
| | - M L Kabir
- University of California, Riverside, California 92521
| | - D Kalinkin
- University of Kentucky, Lexington, Kentucky 40506-0055
| | - K Kang
- Tsinghua University, Beijing 100084
| | - D Kapukchyan
- University of California, Riverside, California 92521
| | - K Kauder
- Brookhaven National Laboratory, Upton, New York 11973
| | - D Keane
- Kent State University, Kent, Ohio 44242
| | - A Kechechyan
- Joint Institute for Nuclear Research, Dubna 141 980
| | - M Kelsey
- Wayne State University, Detroit, Michigan 48201
| | - B Kimelman
- University of California, Davis, California 95616
| | - A Kiselev
- Brookhaven National Laboratory, Upton, New York 11973
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- Lehigh University, Bethlehem, Pennsylvania 18015
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- Lawrence Berkeley National Laboratory, Berkeley, California 94720
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- National Research Nuclear University MEPhI, Moscow 115409
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- National Research Nuclear University MEPhI, Moscow 115409
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- Panjab University, Chandigarh 160014, India
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- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | | | - R Lacey
- State University of New York, Stony Brook, New York 11794
| | - J M Landgraf
- Brookhaven National Laboratory, Upton, New York 11973
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- Brookhaven National Laboratory, Upton, New York 11973
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- Joint Institute for Nuclear Research, Dubna 141 980
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- Brookhaven National Laboratory, Upton, New York 11973
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- University of Heidelberg, Heidelberg 69120, Germany
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- Brookhaven National Laboratory, Upton, New York 11973
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- Shandong University, Qingdao, Shandong 266237
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- Rice University, Houston, Texas 77251
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- University of Science and Technology of China, Hefei, Anhui 230026
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- University of Science and Technology of China, Hefei, Anhui 230026
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- Tsinghua University, Beijing 100084
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- University of Science and Technology of China, Hefei, Anhui 230026
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- University of California, Riverside, California 92521
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- Kent State University, Kent, Ohio 44242
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- Shandong University, Qingdao, Shandong 266237
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- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
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- Central China Normal University, Wuhan, Hubei 430079
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- South China Normal University, Guangzhou, Guangdong 510631
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- Indiana University, Bloomington, Indiana 47408
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- Central China Normal University, Wuhan, Hubei 430079
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- Central China Normal University, Wuhan, Hubei 430079
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- Yale University, New Haven, Connecticut 06520
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- The Ohio State University, Columbus, Ohio 43210
| | - Y Liu
- Texas A&M University, College Station, Texas 77843
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- Central China Normal University, Wuhan, Hubei 430079
| | - T Ljubicic
- Brookhaven National Laboratory, Upton, New York 11973
| | - W J Llope
- Wayne State University, Detroit, Michigan 48201
| | - O Lomicky
- Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
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- Brookhaven National Laboratory, Upton, New York 11973
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- University of California, Riverside, California 92521
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- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - N S Lukow
- Temple University, Philadelphia, Pennsylvania 19122
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- Central China Normal University, Wuhan, Hubei 430079
| | - V B Luong
- Joint Institute for Nuclear Research, Dubna 141 980
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- Fudan University, Shanghai, 200433
| | - R Ma
- Brookhaven National Laboratory, Upton, New York 11973
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- Fudan University, Shanghai, 200433
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- State University of New York, Stony Brook, New York 11794
| | - D Mallick
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
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- Lawrence Berkeley National Laboratory, Berkeley, California 94720
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- Rutgers University, Piscataway, New Jersey 08854
| | - G McNamara
- Wayne State University, Detroit, Michigan 48201
| | - K Mi
- Central China Normal University, Wuhan, Hubei 430079
| | - N G Minaev
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - B Mohanty
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
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- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - I Mooney
- Yale University, New Haven, Connecticut 06520
| | - D A Morozov
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
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- Joint Institute for Nuclear Research, Dubna 141 980
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- ELTE Eötvös Loránd University, Budapest, Hungary H-1117
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- Panjab University, Chandigarh 160014, India
| | - J D Nam
- Temple University, Philadelphia, Pennsylvania 19122
| | - M Nasim
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - D Neff
- University of California, Los Angeles, California 90095
| | - J M Nelson
- University of California, Berkeley, California 94720
| | - D B Nemes
- Yale University, New Haven, Connecticut 06520
| | - M Nie
- Shandong University, Qingdao, Shandong 266237
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- University of Illinois at Chicago, Chicago, Illinois 60607
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- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
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- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - L V Nogach
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - T Nonaka
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - G Odyniec
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - A Ogawa
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Oh
- Sejong University, Seoul 05006, South Korea
| | - V A Okorokov
- National Research Nuclear University MEPhI, Moscow 115409
| | - K Okubo
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - B S Page
- Brookhaven National Laboratory, Upton, New York 11973
| | - R Pak
- Brookhaven National Laboratory, Upton, New York 11973
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- Texas A&M University, College Station, Texas 77843
| | - A Pandav
- National Institute of Science Education and Research, HBNI, Jatni 752050, India
| | - A K Pandey
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | | | - T Pani
- Rutgers University, Piscataway, New Jersey 08854
| | - P Parfenov
- National Research Nuclear University MEPhI, Moscow 115409
| | - A Paul
- University of California, Riverside, California 92521
| | - C Perkins
- University of California, Berkeley, California 94720
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- Temple University, Philadelphia, Pennsylvania 19122
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- Temple University, Philadelphia, Pennsylvania 19122
| | - T Protzman
- Lehigh University, Bethlehem, Pennsylvania 18015
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- Panjab University, Chandigarh 160014, India
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- Wayne State University, Detroit, Michigan 48201
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- Tsinghua University, Beijing 100084
| | - H Qiu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
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- Temple University, Philadelphia, Pennsylvania 19122
| | - C Racz
- University of California, Riverside, California 92521
| | | | - N Raha
- Wayne State University, Detroit, Michigan 48201
| | - R L Ray
- University of Texas, Austin, Texas 78712
| | - H G Ritter
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
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- Rutgers University, Piscataway, New Jersey 08854
| | - L Ruan
- Brookhaven National Laboratory, Upton, New York 11973
| | - A K Sahoo
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
| | - N R Sahoo
- Texas A&M University, College Station, Texas 77843
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- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
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- Rutgers University, Piscataway, New Jersey 08854
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- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute," Moscow 117218
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- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - W B Schmidke
- Brookhaven National Laboratory, Upton, New York 11973
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- Max-Planck-Institut für Physik, Munich 80805, Germany
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- Creighton University, Omaha, Nebraska 68178
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- University of California, Riverside, California 92521
| | - P Seyboth
- Max-Planck-Institut für Physik, Munich 80805, Germany
| | - N Shah
- Indian Institute Technology, Patna, Bihar 801106, India
| | - E Shahaliev
- Joint Institute for Nuclear Research, Dubna 141 980
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- Fudan University, Shanghai, 200433
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- University of Jammu, Jammu 180001, India
| | - N Sharma
- Indian Institute of Science Education and Research (IISER), Berhampur 760010, India
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- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - S R Sharma
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
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- Shandong University, Qingdao, Shandong 266237
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- Fudan University, Shanghai, 200433
| | - K Shen
- University of Science and Technology of China, Hefei, Anhui 230026
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- Central China Normal University, Wuhan, Hubei 430079
| | - Y Shi
- Shandong University, Qingdao, Shandong 266237
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- Fudan University, Shanghai, 200433
| | - F Si
- University of Science and Technology of China, Hefei, Anhui 230026
| | - J Singh
- Panjab University, Chandigarh 160014, India
| | - S Singha
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - P Sinha
- Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
| | - M J Skoby
- Ball State University, Muncie, Indiana 47306
- Purdue University, West Lafayette, Indiana 47907
| | - Y Söhngen
- University of Heidelberg, Heidelberg 69120, Germany
| | - Y Song
- Yale University, New Haven, Connecticut 06520
| | - B Srivastava
- Purdue University, West Lafayette, Indiana 47907
| | | | - D J Stewart
- Wayne State University, Detroit, Michigan 48201
| | - M Strikhanov
- National Research Nuclear University MEPhI, Moscow 115409
| | | | - Y Su
- University of Science and Technology of China, Hefei, Anhui 230026
| | - C Sun
- State University of New York, Stony Brook, New York 11794
| | - X Sun
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Sun
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Sun
- Huzhou University, Huzhou, Zhejiang 313000
| | - B Surrow
- Temple University, Philadelphia, Pennsylvania 19122
| | - D N Svirida
- Alikhanov Institute for Theoretical and Experimental Physics NRC "Kurchatov Institute," Moscow 117218
| | - Z W Sweger
- University of California, Davis, California 95616
| | - A Tamis
- Yale University, New Haven, Connecticut 06520
| | - A H Tang
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Tang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - A Taranenko
- National Research Nuclear University MEPhI, Moscow 115409
| | - T Tarnowsky
- Michigan State University, East Lansing, Michigan 48824
| | - J H Thomas
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
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- Creighton University, Omaha, Nebraska 68178
| | - T Todoroki
- University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
| | - M V Tokarev
- Joint Institute for Nuclear Research, Dubna 141 980
| | - C A Tomkiel
- Lehigh University, Bethlehem, Pennsylvania 18015
| | - S Trentalange
- University of California, Los Angeles, California 90095
| | - R E Tribble
- Texas A&M University, College Station, Texas 77843
| | - P Tribedy
- Brookhaven National Laboratory, Upton, New York 11973
| | - O D Tsai
- Brookhaven National Laboratory, Upton, New York 11973
- University of California, Los Angeles, California 90095
| | - C Y Tsang
- Brookhaven National Laboratory, Upton, New York 11973
- Kent State University, Kent, Ohio 44242
| | - Z Tu
- Brookhaven National Laboratory, Upton, New York 11973
| | - J Tyler
- Texas A&M University, College Station, Texas 77843
| | - T Ullrich
- Brookhaven National Laboratory, Upton, New York 11973
| | - D G Underwood
- Argonne National Laboratory, Argonne, Illinois 60439
- Valparaiso University, Valparaiso, Indiana 46383
| | - I Upsal
- University of Science and Technology of China, Hefei, Anhui 230026
| | - G Van Buren
- Brookhaven National Laboratory, Upton, New York 11973
| | - A N Vasiliev
- National Research Nuclear University MEPhI, Moscow 115409
- NRC "Kurchatov Institute," Institute of High Energy Physics, Protvino 142281
| | - V Verkest
- Wayne State University, Detroit, Michigan 48201
| | - F Videbæk
- Brookhaven National Laboratory, Upton, New York 11973
| | - S Vokal
- Joint Institute for Nuclear Research, Dubna 141 980
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- Purdue University, West Lafayette, Indiana 47907
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- University of California, Los Angeles, California 90095
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- Huzhou University, Huzhou, Zhejiang 313000
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- Shandong University, Qingdao, Shandong 266237
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- Shandong University, Qingdao, Shandong 266237
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- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Wang
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Wang
- Tsinghua University, Beijing 100084
| | - Z Wang
- Shandong University, Qingdao, Shandong 266237
| | - J C Webb
- Brookhaven National Laboratory, Upton, New York 11973
| | | | - G D Westfall
- Michigan State University, East Lansing, Michigan 48824
| | - H Wieman
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - G Wilks
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - S W Wissink
- Indiana University, Bloomington, Indiana 47408
| | - J Wu
- Central China Normal University, Wuhan, Hubei 430079
| | - J Wu
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - X Wu
- University of California, Los Angeles, California 90095
| | - X Wu
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Wu
- University of California, Riverside, California 92521
| | - B Xi
- Fudan University, Shanghai, 200433
| | - Z G Xiao
- Tsinghua University, Beijing 100084
| | - G Xie
- University of Chinese Academy of Sciences, Beijing 101408
| | - W Xie
- Purdue University, West Lafayette, Indiana 47907
| | - H Xu
- Huzhou University, Huzhou, Zhejiang 313000
| | - N Xu
- Lawrence Berkeley National Laboratory, Berkeley, California 94720
| | - Q H Xu
- Shandong University, Qingdao, Shandong 266237
| | - Y Xu
- Shandong University, Qingdao, Shandong 266237
| | - Y Xu
- Central China Normal University, Wuhan, Hubei 430079
| | - Z Xu
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Xu
- University of California, Los Angeles, California 90095
| | - G Yan
- Shandong University, Qingdao, Shandong 266237
| | - Z Yan
- State University of New York, Stony Brook, New York 11794
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- Shandong University, Qingdao, Shandong 266237
| | - Q Yang
- Shandong University, Qingdao, Shandong 266237
| | - S Yang
- South China Normal University, Guangzhou, Guangdong 510631
| | - Y Yang
- National Cheng Kung University, Tainan 70101
| | - Z Ye
- Rice University, Houston, Texas 77251
| | - Z Ye
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - L Yi
- Shandong University, Qingdao, Shandong 266237
| | - K Yip
- Brookhaven National Laboratory, Upton, New York 11973
| | - Y Yu
- Shandong University, Qingdao, Shandong 266237
| | - W Zha
- University of Science and Technology of China, Hefei, Anhui 230026
| | - C Zhang
- State University of New York, Stony Brook, New York 11794
| | - D Zhang
- Central China Normal University, Wuhan, Hubei 430079
| | - J Zhang
- Shandong University, Qingdao, Shandong 266237
| | - S Zhang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - W Zhang
- South China Normal University, Guangzhou, Guangdong 510631
| | - X Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Zhang
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - Y Zhang
- University of Science and Technology of China, Hefei, Anhui 230026
| | - Y Zhang
- Shandong University, Qingdao, Shandong 266237
| | - Y Zhang
- Central China Normal University, Wuhan, Hubei 430079
| | - Z J Zhang
- National Cheng Kung University, Tainan 70101
| | - Z Zhang
- Brookhaven National Laboratory, Upton, New York 11973
| | - Z Zhang
- University of Illinois at Chicago, Chicago, Illinois 60607
| | - F Zhao
- Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
| | - J Zhao
- Fudan University, Shanghai, 200433
| | - M Zhao
- Brookhaven National Laboratory, Upton, New York 11973
| | - C Zhou
- Fudan University, Shanghai, 200433
| | - J Zhou
- University of Science and Technology of China, Hefei, Anhui 230026
| | - S Zhou
- Central China Normal University, Wuhan, Hubei 430079
| | - Y Zhou
- Central China Normal University, Wuhan, Hubei 430079
| | - X Zhu
- Tsinghua University, Beijing 100084
| | - M Zurek
- Argonne National Laboratory, Argonne, Illinois 60439
- Brookhaven National Laboratory, Upton, New York 11973
| | - M Zyzak
- Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
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Zhong ZH, Liang L, Fu TW, Dai MG, Cheng J, Liu SY, Ye TW, Shen GL, Zhang CW, Huang DS, Liu JW. Prognostic value of platelet distribution width to lymphocyte ratio in patients with hepatocellular carcinoma following hepatectomy. BMC Cancer 2023; 23:1116. [PMID: 37974129 PMCID: PMC10655313 DOI: 10.1186/s12885-023-11621-8] [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: 09/02/2023] [Accepted: 11/07/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND Platelet distribution width (PDW), but not platelet count, was found to more comprehensively reflect platelet activity. The present study, thus, aimed to evaluate the prognostic value of PDW to lymphocyte ratio (PDWLR) in patients with hepatocellular carcinoma (HCC) following hepatectomy. METHODS Patients following hepatectomy were analyzed retrospectively. The Kaplan-Meier survival curves and Cox regression model were used to determine the prognostic value of PDWLR. RESULTS 241 patients were analyzed eventually, and stratified into low and high PDWLR groups (≤ 9.66 vs. > 9.66). Results of comparing the baseline characteristics showed that high PDWLR was significantly associated with cirrhosis, and intraoperative blood loss (all P < 0.05). In multivariate COX regression analysis, PDWLR was demonstrated as an independent risk factor for OS (HR: 1.549, P = 0.041) and RFS (HR: 1.655, P = 0.005). Moreover, PDWLR demonstrated a superior capacity for predicting prognosis compared to other indicators. CONCLUSION Preoperative PDWLR has a potential value in predicting the prognosis of HCC patients following hepatectomy, which may help in clinical decision-making for individual treatment.
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Affiliation(s)
- Zhi-Han Zhong
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Affiliated People's Hospital, Zhejiang Provincial People's Hospital, Hangzhou Medical College, Hangzhou, 310014, Zhejiang, China
| | - Lei Liang
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Affiliated People's Hospital, Zhejiang Provincial People's Hospital, Hangzhou Medical College, Hangzhou, 310014, Zhejiang, China.
| | - Tian-Wei Fu
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Affiliated People's Hospital, Zhejiang Provincial People's Hospital, Hangzhou Medical College, Hangzhou, 310014, Zhejiang, China
| | - Mu-Gen Dai
- Department of Gastroenterology, The Fifth Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Jian Cheng
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Affiliated People's Hospital, Zhejiang Provincial People's Hospital, Hangzhou Medical College, Hangzhou, 310014, Zhejiang, China
| | - Si-Yu Liu
- Department of Laboratory Medicine, The Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province, Zhejiang University Lishui Hospital, Lishui, Zhejiang, China
| | - Tai-Wei Ye
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Affiliated People's Hospital, Zhejiang Provincial People's Hospital, Hangzhou Medical College, Hangzhou, 310014, Zhejiang, China
- Department of the Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Guo-Liang Shen
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Affiliated People's Hospital, Zhejiang Provincial People's Hospital, Hangzhou Medical College, Hangzhou, 310014, Zhejiang, China
| | - Cheng-Wu Zhang
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Affiliated People's Hospital, Zhejiang Provincial People's Hospital, Hangzhou Medical College, Hangzhou, 310014, Zhejiang, China
| | - Dong-Sheng Huang
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Affiliated People's Hospital, Zhejiang Provincial People's Hospital, Hangzhou Medical College, Hangzhou, 310014, Zhejiang, China
| | - Jun-Wei Liu
- General Surgery, Cancer Center, Department of Hepatobiliary & Pancreatic Surgery and Minimally Invasive Surgery, Affiliated People's Hospital, Zhejiang Provincial People's Hospital, Hangzhou Medical College, Hangzhou, 310014, Zhejiang, China.
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Hu J, Feng Y, Su H, Xu Z, Ho HC, Zheng H, Zhang W, Tao J, Wu K, Hossain MZ, Zhang Y, Hu K, Huang C, Cheng J. Seasonal peak and the role of local weather in schizophrenia occurrence: A global analysis of epidemiological evidence. Sci Total Environ 2023; 899:165658. [PMID: 37478950 DOI: 10.1016/j.scitotenv.2023.165658] [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: 04/25/2023] [Revised: 07/07/2023] [Accepted: 07/17/2023] [Indexed: 07/23/2023]
Abstract
BACKGROUND Many studies have shown that the onset of schizophrenia peaked in certain months within a year and the local weather conditions could affect the morbidity risk of schizophrenia. This study aimed to conduct a systematic analysis of schizophrenia seasonality in different countries of the world and to explore the effects of weather factors globally. METHODS We searched three databases (PubMed, Web of Science, and China National Knowledge Infrastructure) for eligible studies published up to September 2022. Schizophrenia seasonality was compared between hemispheres and within China. A meta-analysis was conducted to pool excess risk (ER, absolute percentage increase in risk) of the onset of schizophrenia associated with various weather factors including temperature (an increase or decrease of temperature as a reflection of high or low temperature; heatwave; temperature variation), precipitation, etc. RESULTS: We identified 84 relevant articles from 22 countries, mainly in China. The seasonality analysis found that the onset of schizophrenia mostly peaked in the cold season in the southern hemisphere but in the warm season in the northern hemisphere. Interestingly in China, schizophrenia seasonality presented two peaks, respectively in the late cold and warm seasons. The meta-analysis further revealed an increased risk of schizophrenia after short-term exposure to high temperature [ER%: 0.45 % (95 % confidence interval (CI): 0.14 % to 0.76 %)], low temperature [ER%: 0.52 % (95%CI: 0.29 % to 0.75 %)], heatwave [ER%: 7.26 % (95%CI: 4.45 % to 10.14 %)], temperature variation [ER%: 1.02 % (95%CI: 0.55 % to 1.50 %)], extreme precipitation [ER%: 3.96 % (95%CI: 2.29 % to 5.67 %)]. The effect of other weather factors such as sunlight on schizophrenia was scarcely investigated with inconsistent findings. CONCLUSION This study provided evidence of intra- and inter-country variations in schizophrenia seasonality, especially the double-peak seasons in China. Exposure to local weather conditions mainly temperature changes and precipitation could affect the onset risk of schizophrenia.
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Affiliation(s)
- Jihong Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Yufan Feng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Hong Su
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Zhiwei Xu
- School of Medicine and Dentistry, Griffith University, Gold Coast, Queensland, Australia
| | - Hung Chak Ho
- Department of Public and International Affairs, City University of Hong Kong, Hong Kong, China
| | - Hao Zheng
- Department of Environmental Health, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Wenyi Zhang
- Chinese PLA Center for Disease Control and Prevention, Beijing, China
| | - Junwen Tao
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Keyu Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Mohammad Zahid Hossain
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr, b), Dhaka, Bangladesh
| | - Yunquan Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University of Science and Technology, Wuhan, China
| | - Kejia Hu
- Department of Big Data in Health Science, School of Public Health, Zhejiang University, Hangzhou, China
| | - Cunrui Huang
- Vanke School of Public Health, Tsinghua University, Beijing, China.
| | - Jian Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China.
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Zhang CY, Chen H, Zhang H, Cheng J, Zhao YL. [Analysis of the reported incidence and epidemiological characteristics of pulmonary tuberculosis among health-care workers in China,2011-2020]. Zhonghua Jie He He Hu Xi Za Zhi 2023; 46:1103-1109. [PMID: 37914421 DOI: 10.3760/cma.j.cn112147-20230825-00107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/03/2023]
Abstract
Objective: To analyze the reported incidence and epidemiological characteristics of pulmonary tuberculosis (PTB) among healthcare workers (HCWs) nationwide from 2011 to 2020. Methods: The national surveillance data of PTB from 2011 to 2020 were used to analyze the reported incidence and epidemiological characteristics of PTB among HCWs, and the average annual change trends were calculated. Results: The reported incidence of PTB among HCWs in China first decreased and then increased, with an average annual percentage change (AAPC) of -1.1%, from 37.0/100 000 in 2011 to 30.0/100 000 in 2015, and then to 33.4/100 000 in 2020. From 2011 to 2019, the risk of PTB in males was 1.02-1.37 times higher than that in females, and in 2020, the risk of PTB in males was 0.86 times higher than that in females. The risk of pulmonary tuberculosis in males showed a rapid downward trend, and the AAPC was -3.8%. Taking the 45-<55 age group as a reference, the risk of PTB in the <25, 25-<35, 55-<60 and≥60 age groups was 4.64, 1.97, 1.28 and 1.47 times, respectively. There was no significant difference between the 35-<45 age group and the 44-<55 age group. The reported incidence rates in the eastern, central and western China were 25.0/100 000, 33.2/100 000 and 44.0/100 000, respectively. The incidence rates in the central and western China were 1.33 and 1.76 times higher than that in the eastern China, and the AAPCs were -1.2%, -0.2%, and -1.6% in the eastern, central, and western China, respectively. Conclusions: From 2011 to 2020, the reported incidence of PTB among HCWs in China was generally at a low level, but there was an upward trend since 2015. It is necessary to strengthen TB prevention and control among this group, especially focusing on key provinces in the central and western China. At the same time, it is necessary to strengthen the entry-level and routine training for young HCWs in TB infection control.
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Affiliation(s)
- C Y Zhang
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - H Chen
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - H Zhang
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - J Cheng
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
| | - Y L Zhao
- National Center for Tuberculosis Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
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Tao J, Zhang Y, Li Z, Yang M, Huang C, Hossain MZ, Xu Y, Wei X, Su H, Cheng J, Zhang W. Daytime and nighttime high temperatures differentially increased the risk of cardiovascular disease: A nationwide hospital-based study in China. Environ Res 2023; 236:116740. [PMID: 37495061 DOI: 10.1016/j.envres.2023.116740] [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: 04/19/2023] [Revised: 07/01/2023] [Accepted: 07/24/2023] [Indexed: 07/28/2023]
Abstract
Short-term exposure to ambient high temperature (heat) could increase the risk of cardiovascular disease (CVD). However, available evidence on the burden of daytime and nighttime heat on CVD is limited and vulnerable populations remain unknown so far. We aimed to examine and differentiate the impact of daytime and nighttime heat on CVD in China. Data on daily outpatient visits for CVD were collected from 15 Chinese cities spanning multiple geographical regions, climates, and socio-economic conditions. The population-weighted temperature was used to calculate excess heat exposure in warm seasons (June-September) from 2011 to 2015. Hot day excess (HDE) and hot night excess (HNE), the sum of temperature above the heat threshold during daytime and nighttime respectively, were used to represent daytime and nighttime excess heat. A distributed lag non-linear model was employed to estimate the city-level association between HDE/HNE and daily CVD cases. The city-level association was then pooled by multivariate meta-analysis. We further estimated the disease burden of CVD attributable to HDE and HNE by geographical regions, gender, and age. A total of 729,409 cases of CVD were included in this study. Both HDE and HNE were associated with an increased risk of CVD, with greater effects from nighttime heat (relative risk (RR): 1.38; 95% confidence interval (CI): 1.18-1.61) than daytime heat (RR: 1.10; 95% CI: 1.05-1.15). The proportion of CVD cases attributable to HNE was 15.7%, which was almost three times as high as HDE (4.6%, p for difference <0.05). Males, people living in northern cities, and those aged less than 45 years were more vulnerable to HNE. Our findings for the first time revealed an intra-day difference in the heat effect on CVD, with a greater impact from nighttime heat exposure, which should be considered to protect vulnerable populations on hot days.
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Affiliation(s)
- Junwen Tao
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Yongming Zhang
- Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, National Clinical Research Center for Respiratory Diseases, Beijing, China
| | - Zhiwei Li
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Min Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Cunrui Huang
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Mohammad Zahid Hossain
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh
| | - Yuanyong Xu
- Chinese PLA Center for Disease Control and Prevention, Beijing, China
| | - Xianyu Wei
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Chinese PLA Center for Disease Control and Prevention, Beijing, China
| | - Hong Su
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Jian Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China.
| | - Wenyi Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Chinese PLA Center for Disease Control and Prevention, Beijing, China.
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Yang Z, Zhao L, Chen M, Yang D, Yin Z, Yang W, Cheng J, Xu Q, Liu Z, Geng F, Xu H. Evolution of intrinsic defects and ring structures on the surface of fused silica optics after CO 2 laser conditioning. Opt Lett 2023; 48:5727-5730. [PMID: 37910744 DOI: 10.1364/ol.500368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 10/12/2023] [Indexed: 11/03/2023]
Abstract
Recently and interestingly, experiments show that the CO2 laser conditioning can significantly increase the laser-induced damage threshold (LIDT) of fused silica optics, but its underlying mechanism has not been clearly revealed. This Letter reports the experimental studies on the evolution of the intrinsic point defects and intrinsic ring structures on the surface of fused silica optics under the CO2 laser irradiation. The laser conditioning can effectively reduce the intrinsic defect contents in the surface layer of mechanically processed fused silica. However, the suppression effect of defects can be affected by the initial surface state. If there are micro-cracks on the component surface, the effect of the laser conditioning would be limited. The evolution of the intrinsic ring structures indicate that most of the intrinsic defects tend to recombine as short (Si-O)n ring structures during the laser healing of the micro-fractures. The observed recombination behavior and suppression of the intrinsic defects can help find out the reason for the increase of the LIDT of the fused silica optics.
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Zhang Y, Zhu H, Cheng J, Wang J, Gu X, Han J, Zhang Y, Zhao Y, He Y, Zhang H. Improving the Quality of Fetal Heart Ultrasound Imaging With Multihead Enhanced Self-Attention and Contrastive Learning. IEEE J Biomed Health Inform 2023; 27:5518-5529. [PMID: 37556337 DOI: 10.1109/jbhi.2023.3303573] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/11/2023]
Abstract
Fetal congenital heart disease (FCHD) is a common, serious birth defect affecting ∼1% of newborns annually. Fetal echocardiography is the most effective and important technique for prenatal FCHD diagnosis. The prerequisites for accurate ultrasound FCHD diagnosis are accurate view recognition and high-quality diagnostic view extraction. However, these manual clinical procedures have drawbacks such as, varying technical capabilities and inefficiency. Therefore, the automatic identification of high-quality multiview fetal heart scan images is highly desirable to improve prenatal diagnosis efficiency and accuracy of FCHD. Here, we present a framework for multiview fetal heart ultrasound image recognition and quality assessment that comprises two parts: a multiview classification and localization network (MCLN) and an improved contrastive learning network (ICLN). In the MCLN, a multihead enhanced self-attention mechanism is applied to construct the classification network and identify six accurate and interpretable views of the fetal heart. In the ICLN, anatomical structure standardization and image clarity are considered. With contrastive learning, the absolute loss, feature relative loss and predicted value relative loss are combined to achieve favorable quality assessment results. Experiments show that the MCLN outperforms other state-of-the-art networks by 1.52-13.61% when determining the F1 score in six standard view recognition tasks, and the ICLN is comparable to the performance of expert cardiologists in the quality assessment of fetal heart ultrasound images, reaching 97% on a test set within 2 points for the four-chamber view task. Thus, our architecture offers great potential in helping cardiologists improve quality control for fetal echocardiographic images in clinical practice.
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Cheng J, Chen L, Xie X, Feng K, Sun H, Qin Y, Hua W, Zheng Z, He Y, Pan W, Yang W, Lyu F, Zhong J, Deng Z, Jiao Y, Peng Y. Proton Shuttling by Polyaniline of High Brønsted Basicity for Improved Electrocatalytic Ethylene Production from CO 2. Angew Chem Int Ed Engl 2023; 62:e202312113. [PMID: 37671746 DOI: 10.1002/anie.202312113] [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: 08/18/2023] [Revised: 09/03/2023] [Accepted: 09/04/2023] [Indexed: 09/07/2023]
Abstract
Hybrid organic/inorganic composites with the organic phase tailored to modulate local chemical environment at the Cu surface arise as an enchanting category of catalysts for electrocatalytic CO2 reduction reaction (CO2 RR). A fundamental understanding on how the organics of different functionality, polarity, and hydrophobicity affect the reaction path is, however, still lacking to guide rational catalyst design. Herein, polypyrrole (PPy) and polyaniline (PANI) manifesting different Brønsted basicity are compared for their regulatory roles on the CO2 RR pathways regarding *CO coverage, proton source and interfacial polarity. Concerted efforts from in situ IR, Raman and operando modelling unveil that at the PPy/Cu interface with limited *CO coverage, hydridic *H produced by the Volmer step favors the carbon hydrogenation of *CO to form *CHO through a Tafel process; Whereas at the PANI/Cu interface with concentrated CO2 and high *CO coverage, protonic H+ shuttled through the benzenoid -NH- protonates the oxygen of *CO, yielding *COH for asymmetric coupling with nearby *CO to form *OCCOH under favored energetics. As a result of the tailored chemical environment, the restructured PANI/Cu composite demonstrates a high partial current density of 0.41 A cm-2 at a maximal Faraday efficiency of 67.5 % for ethylene production, ranking among states of the art.
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Affiliation(s)
- Jian Cheng
- Soochow Institute for Energy and Materials Innovations, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
| | - Ling Chen
- School of Chemical Engineering, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Xulan Xie
- Soochow Institute for Energy and Materials Innovations, College of Energy, Soochow University, Suzhou, 215006, P. R. China
| | - Kun Feng
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, 215123, China
| | - Hao Sun
- Soochow Institute for Energy and Materials Innovations, College of Energy, Soochow University, Suzhou, 215006, P. R. China
| | - Yongze Qin
- Soochow Institute for Energy and Materials Innovations, College of Energy, Soochow University, Suzhou, 215006, P. R. China
| | - Wei Hua
- Soochow Institute for Energy and Materials Innovations, College of Energy, Soochow University, Suzhou, 215006, P. R. China
| | - Zhangyi Zheng
- Soochow Institute for Energy and Materials Innovations, College of Energy, Soochow University, Suzhou, 215006, P. R. China
| | - Ying He
- Soochow Institute for Energy and Materials Innovations, College of Energy, Soochow University, Suzhou, 215006, P. R. China
| | - Weiyi Pan
- Soochow Institute for Energy and Materials Innovations, College of Energy, Soochow University, Suzhou, 215006, P. R. China
| | - Wenjun Yang
- Soochow Institute for Energy and Materials Innovations, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
| | - Fenglei Lyu
- Soochow Institute for Energy and Materials Innovations, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
| | - Jun Zhong
- Institute of Functional Nano & Soft Materials (FUNSOM), Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, 215123, China
| | - Zhao Deng
- Soochow Institute for Energy and Materials Innovations, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
| | - Yan Jiao
- School of Chemical Engineering, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Yang Peng
- Soochow Institute for Energy and Materials Innovations, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
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Cheng J, Gao D, Dong J, Zhang X. Ultra-efficient second harmonic generation via mode phase matching in integrated lithium niobate racetrack resonators. Opt Express 2023; 31:36736-36744. [PMID: 38017817 DOI: 10.1364/oe.503988] [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: 08/24/2023] [Accepted: 10/02/2023] [Indexed: 11/30/2023]
Abstract
High-efficiency second harmonic generation (SHG) relying solely on intermodal dispersion engineering remains a challenge. Here, we realize highly efficient SHG using a double-waveguide coupled racetrack microring resonator on X-cut lithium niobate on insulator (LNOI), where both pump and second harmonic (SH) approach critical coupling. Through precise temperature tuning, simultaneous pump and SH resonance is attained in the resonator, dramatically enhancing SHG efficiency. With low pump power, a normalized conversion efficiency of 9972%/W is achieved. Moreover, the resonator provides a 25.73 dB enhancement in SHG efficiency compared to a 4 mm straight waveguide with identical phase matching in our experiment. This work enables efficient wavelength conversion and quantum state generation on integrated X-cut LNOI platforms.
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Liang J, Wang L, Wang X, Cui G, Zhou J, Xing T, Du K, Xu J, Wang L, Liang R, Chen B, Cheng J, Shen H, Li J, Xu W. Chidamide plus prednisone, cyclophosphamide, and thalidomide for relapsed or refractory peripheral T-cell lymphoma: A multicenter phase II trial. Chin Med J (Engl) 2023:00029330-990000000-00806. [PMID: 37839894 DOI: 10.1097/cm9.0000000000002836] [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: 06/27/2023] [Indexed: 10/17/2023] Open
Abstract
BACKGROUND Although the treatment of peripheral T-cell lymphoma (PTCL) has undergone advancements during the past several years, the response rate and long-term effects with respect to patients with PTCL remain unsatisfactory-particularly for relapsed or refractory (R/R) patients. This phase II trial was designed to explore the efficacy and safety of an all-oral regimen of chidamide plus prednisone, cyclophosphamide, and thalidomide (CPCT) for R/R PTCL patients who could not tolerate the standard chemotherapy for a variety of reasons. METHODS We conducted a multicenter phase II clinical trial in which we combined chidamide (30 mg twice weekly) with prednisone (20 mg daily after breakfast), cyclophosphamide (50 mg daily after lunch), and thalidomide (100 mg daily at bedtime) (the CPCT regimen) for a total of fewer than 12 cycles as an induction-combined treatment period, and then applied chidamide as single-drug maintenance. Forty-five patients were ultimately enrolled from August 2016 to April 2021 with respect to Chinese patients at nine centers. Our primary objective was to assess the overall response rate (ORR) after the treatment with CPCT. RESULTS Of the 45 enrolled patients, the optimal ORR and complete response (CR)/CR unconfirmed (CRu) were 71.1% (32/45) and 28.9% (13/45), respectively, and after a median follow-up period of 56 months, the median progression-free survival (PFS) and overall survival (OS) were 8.5 months and 17.2 months, respectively. The five-year PFS and OS rates were 21.2% (95% confidence interval [CI], 7.9-34.5 %) and 43.8% (95% CI, 28.3-59.3 %), respectively. The most common adverse event was neutropenia (20/45, 44.4%), but we observed no treatment-related death. CONCLUSION The all-oral CPCT regimen was an effective and safety regimen for R/R PTCL patients who could not tolerate standard chemotherapy for various reasons. TRIAL REGISTRATION ClinicalTrials.gov, NCT02879526.
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Affiliation(s)
- Jinhua Liang
- Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, Jiangsu 210029, China
- Key Laboratory of Hematology of Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, Jiangsu 210029, China
| | - Li Wang
- Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, Jiangsu 210029, China
- Key Laboratory of Hematology of Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, Jiangsu 210029, China
| | - Xiaodong Wang
- Department of Hematology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine of University of Electronic Science and Technology of China, Chengdu, Sichuan 610000, China
| | - Guohui Cui
- Department of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
| | - Jianfeng Zhou
- Department of Hematology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Tongyao Xing
- Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, Jiangsu 210029, China
- Key Laboratory of Hematology of Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, Jiangsu 210029, China
| | - Kaixin Du
- Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, Jiangsu 210029, China
- Key Laboratory of Hematology of Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, Jiangsu 210029, China
| | - Jingyan Xu
- Department of Hematology, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing, Jiangsu 210008, China
| | - Luqun Wang
- Department of Hematology, Qilu Hospital of Shandong University, Jinan, Shandong 250000, China
| | - Rong Liang
- Department of Hematology, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi 710032, China
| | - Biyun Chen
- Department of Hematology, Fujian Provincial Hospital, Fuzhou, Fujian 350001, China
| | - Jian Cheng
- Department of Hematology, Zhongda Hospital, Medical School of Southeast University, Nanjing, Jiangsu 210009, China
| | - Haorui Shen
- Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, Jiangsu 210029, China
- Key Laboratory of Hematology of Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, Jiangsu 210029, China
| | - Jianyong Li
- Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, Jiangsu 210029, China
- Key Laboratory of Hematology of Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, Jiangsu 210029, China
| | - Wei Xu
- Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, Jiangsu 210029, China
- Key Laboratory of Hematology of Nanjing Medical University, Nanjing, Jiangsu 210029, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, Jiangsu 210029, China
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He Y, Ding L, Cheng J, Mei S, Xie X, Zheng Z, Pan W, Qin Y, Huang F, Peng Y, Deng Z. A "Trinity" Design of Li-O 2 Battery Engaging the Slow-Release Capsule of Redox Mediators. Adv Mater 2023:e2308134. [PMID: 37823718 DOI: 10.1002/adma.202308134] [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: 08/11/2023] [Revised: 09/30/2023] [Indexed: 10/13/2023]
Abstract
Nonaqueous Li-O2 battery (LOB) represents one of the promising next-gen energy storage solutions owing to its ultrahigh energy density but suffers from problems such as high charging overpotential, slow redox kinetics, Li anode corrosion, etc., calling for a systemic optimization of the battery configuration and structural components. Herein, an ingenious "trinity" design of LOB is initiated by implementing a hollowed cobalt metal organic framework (MOF) impregnating iodized polypyrrole simultaneously as the cathode catalyst, anode protection layer, and slow-release capsule of redox mediators, so as to systemically address issues of impeded mass transport and redox kinetics on the cathode, dendrite growth, and surface corrosion on the anode, as well as limited intermediate solubility in the low donor-number (DN) solvent. As a result of the systemic effort, the LOB constructed demonstrates an ultralow discharge/charge polarization of 0.2 V, prolonged cycle life of 1244 h and total discharge capacity of 28.41 mAh cm-2 . Mechanistic investigations attribute the superb LOB performance to the redox-mediated solution growth mechanism of crystalline Li2 O2 with both enhanced reaction kinetics and reversibility. This study offers a paradigm in designing smart materials to raise the performance bar of Li-O2 battery toward realistic applications.
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Affiliation(s)
- Ying He
- Soochow Institute for Energy and Materials Innovations, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
| | - Leyu Ding
- Soochow Institute for Energy and Materials Innovations, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
| | - Jian Cheng
- Soochow Institute for Energy and Materials Innovations, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
| | - Shiwei Mei
- Soochow Institute for Energy and Materials Innovations, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
| | - Xulan Xie
- Soochow Institute for Energy and Materials Innovations, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
| | - Zhangyi Zheng
- Soochow Institute for Energy and Materials Innovations, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
| | - Weiyi Pan
- Soochow Institute for Energy and Materials Innovations, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
| | - Yongze Qin
- Soochow Institute for Energy and Materials Innovations, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
| | - Fangding Huang
- Soochow Institute for Energy and Materials Innovations, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
| | - Yang Peng
- Soochow Institute for Energy and Materials Innovations, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
| | - Zhao Deng
- Soochow Institute for Energy and Materials Innovations, College of Energy, Soochow University, Suzhou, 215006, P. R. China
- Jiangsu Key Laboratory of Advanced Negative Carbon Technologies, Soochow University, Suzhou, 215006, P. R. China
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Ming Y, Luo C, Ji B, Cheng J. ARPC5 acts as a potential prognostic biomarker that is associated with cell proliferation, migration and immune infiltrate in gliomas. BMC Cancer 2023; 23:937. [PMID: 37789267 PMCID: PMC10548738 DOI: 10.1186/s12885-023-11433-w] [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: 03/27/2023] [Accepted: 09/21/2023] [Indexed: 10/05/2023] Open
Abstract
BACKGROUND Gliomas are the most common malignant brain tumors, with powerful invasiveness and an undesirable prognosis. Actin related protein 2/3 complex subunit 5 (ARPC5) encodes a component of the Arp2/3 protein complex, which plays a significant role in regulating the actin cytoskeleton. However, the prognostic values and biological functions of ARPC5 in gliomas remain unclear. METHODS Based on the TCGA, GEO, HPA, and UALCAN database, we determined the expression of ARPC5 in glioma. The results were verified by immunohistochemistry and Western blot analysis of glioma samples. Moreover, Kaplan-Meier curves, ROC curves, Cox regression analyses, and prognostic nomograms were used to observe the correlation between the ARPC5 expression and the prognosis of glioma patients. GO and KEGG enrichment analyses were conducted to identify immune-related pathways involved with the differential expression of ARPC5. Subsequently, the TCGA database was used to estimate the relationship between ARPC5 expression and immunity-related indexes, such as immune scores, infiltrating immune cells, and TMB. The TCIA database was used to assess the correlation between ARPC5 with immunotherapy. The association between ARPC5 and T cells marker CD3 was also evaluated through immunohistochemistry methods. The correlation between ARPC5 and T cell, as well as the prognosis of patients, was also evaluated using immunological methods. Moreover, the effect of ARPC5 on the biological characteristics of LN229 and U251 cells was determined by MTT, clone formation, and transwell migration assay. RESULTS The high degree of ARPC5 was correlated with worse prognosis and unfavorable clinical characteristics of glioma patients. In the analysis of GO and KEGG, it is shown that ARPC5 was strongly correlated with multiple immune-related signaling pathways. The single-cell analysis revealed that ARPC5 expression was increased in astrocytes, monocytes and T cells. In addition, ARPC5 expression was strongly associated with immune scores, infiltrating immune cells, TMB, MSI, immune biomarkers, and immunotherapy. In experimental analysis, we found that ARPC5 was significantly overexpressed in gliomas and closely correlated with patient prognosis and CD3 expression. Functionally, the knockout of ARPC5 significantly reduced the proliferation and invasion of LN229 and U251 cells. CONCLUSIONS Our study revealed that the high expression level of ARPC5 may serve as a promising prognostic biomarker and be associated with tumor immunity in glioma.
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Affiliation(s)
- Yue Ming
- Laboratory of Molecular Oncology, Frontiers Science Center for Disease-related Molecular Networks, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Chunyuan Luo
- Laboratory of Molecular Oncology, Frontiers Science Center for Disease-related Molecular Networks, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Beihong Ji
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pennsylvania, USA
| | - Jian Cheng
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
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Pan G, Cheng J, Pan HF, Fan YG, Ye DQ. Global Chronic obstructive pulmonary disease burden attributable to air pollution from 1990 to 2019. Int J Biometeorol 2023; 67:1543-1553. [PMID: 37522974 DOI: 10.1007/s00484-023-02504-5] [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: 12/30/2021] [Revised: 08/26/2022] [Accepted: 10/07/2022] [Indexed: 08/01/2023]
Abstract
BACKGROUND The disease burden attributable to chronic obstructive pulmonary disease (COPD) is significant worldwide. Some studies have linked exposure to air pollution to COPD, but there has been little research on this. METHODS We aimed to assess the COPD-related disease burden attributable to air pollution from multiple epidemiological perspectives. This study conducted a three-stage analysis. Firstly, we reported on the burden of disease worldwide in 2019 by different subgroups including sex, age, region, and country. Secondly, we studied the trends in disease burden from 1990 to 2019. Finally, we explored the association of some national indicators with disease burden to look for risk factors. RESULTS In 2019, the death number of COPD associated with air pollution accounted for 2.32% of the total global death, and the number of DALY accounted for 1.12% of the global DALY. From 1990 to 2019, the death number of COPD associated with air pollution increased peaked at 1.41 million in 1993, fluctuated, and then declined. We found the same temporal pattern of DALY. The corresponding age-standardized rates had been falling. At the same time, the burden of COPD associated with air pollution was also affected by some national indicators. CONCLUSIONS This study indicated that air pollution-related COPD contributed to a significant global disease burden. We called for health policymakers to take action and interventions targeting vulnerable countries and susceptible populations.
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Affiliation(s)
- Guixia Pan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
| | - Jian Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
| | - Hai-Feng Pan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
| | - Yin-Guang Fan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China
| | - Dong-Qing Ye
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, 230032, Anhui, China.
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Qiu Z, Huang Z, Zhu L, Huang X, Wang WH, Tie J, Shen L, Shi M, Chen J, Liu M, Cheng J, Zhang J, Li Y, Wang S. A Nomogram to Predict Pathological Axillary Status in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy. Int J Radiat Oncol Biol Phys 2023; 117:e202. [PMID: 37784855 DOI: 10.1016/j.ijrobp.2023.06.1080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) This study aimed to identify factors influencing axillary pathological complete response (pCR) and to develop a predictive nomogram to evaluate axillary pCR rate in breast cancer patients treated with neoadjuvant chemotherapy (NAC). MATERIALS/METHODS A total of 2368 patients who received NAC and mastectomy between 2000 and 2014 from 12 grade A tertiary hospitals in China were analyzed retrospectively. The patients treated in three cancer hospitals (training set, n = 1629) were used to construct the nomogram based on multivariate logistic regression analyses. The nomograph was validated by the area under the receiver operating characteristic curve (AUC) and calibration curve in patients from 9 other general hospitals (validation set, n = 739). RESULTS The nomogram incorporated seven predicting factors including NACT cycles, response to NACT, clinical T stage, clinical N stage, grade, LVI, and molecular subtype. The AUC for the training set and validation set were 0.762 and 0.802, respectively. In addition, the calibration curve also showed good agreement between the nomogram-based predictions and the actual observations. CONCLUSION A nomogram was established to predict the status of axillary lymph nodes in breast cancer patients after NAC. The predictive model performed well both in the training set and external validation set.
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Affiliation(s)
- Z Qiu
- Department of Radiation Oncology, National Cancer Center/ National Clinical Research Center for Cancer/ Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Z Huang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - L Zhu
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - X Huang
- Department of Radiation Oncology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - W H Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - J Tie
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - L Shen
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, China
| | - M Shi
- Department of Radiation Oncology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - J Chen
- Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - M Liu
- Department of Radiation Oncology, the First Hospital, Jilin University, Changchun, China
| | - J Cheng
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - J Zhang
- Department of Radiation Oncology, Forth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Y Li
- Department of Radiation Oncology, National Cancer Center/ National Clinical Research Center for Cancer/ Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - S Wang
- Department of Radiation Oncology, National Cancer Center/ National Clinical Research Center for Cancer/ Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Tao J, Yan J, Su H, Huang C, Tong S, Ho HC, Xia Q, Zhu C, Zheng H, Hossain MZ, Cheng J. Impacts of PM 2.5 before and after COVID-19 outbreak on emergency mental disorders: A population-based quasi-experimental and case-crossover study. Environ Pollut 2023; 334:122175. [PMID: 37437758 DOI: 10.1016/j.envpol.2023.122175] [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: 12/29/2022] [Revised: 06/04/2023] [Accepted: 07/09/2023] [Indexed: 07/14/2023]
Abstract
The ongoing COVID-19 pandemic is a great challenge to mental health, but fine particulate matter (PM2.5), an increasingly reported risk factor for mental disorders, has been greatly alleviated during the pandemic in many countries. It remains unknown whether COVID-19 outbreak can affect the association between PM2.5 exposure and the risk of mental disorders. This study aimed to investigate the associations of total and cause-specific mental disorders with PM2.5 exposure before and after the COVID-19 outbreak in China. Data on daily emergency department visits (EDVs) and hospitalizations of mental disorders from 2016 to 2021 were obtained from Anhui Mental Health Center for Hefei city. An interrupted time series analysis was used to quantify the impact of COVID-19 outbreak on EDVs and hospitalizations of mental disorders. A time-stratified case-crossover analysis was employed to evaluate the association of mental disorders with PM2.5 exposure before and after the COVID-19 outbreak, especially in the three months following the COVID-19 outbreak. After COVID-19 outbreak, there was an immediate and significant decrease in total mental disorders, including a reduction of 15% (95% CI: 3%-26%) in EDVs and 44% (95% CI: 36%-51%) in hospitalizations. PM2.5 exposure was associated with increased risk of EDVs and hospitalizations for total and cause-specific mental disorders (schizophrenia, schizotypal and delusional disorders; neurotic, stress-related, and somatoform disorders) before COVID-19 outbreak, but this PM2.5-related risk elevation significantly decreased after COVID-19 outbreak, with greater risk reduction at the first month after the outbreak. However, young people (0-45 years) were still vulnerable to PM2.5 exposure after the COVID-19 outbreak. This study first reveals that the risk of PM2.5-related emergency mental disorders decreased after the COVID-19 outbreak in China. The low concentration of PM2.5 might benefit mental health and greater efforts are required to mitigate air pollution in the post-COVID-19 era.
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Affiliation(s)
- Junwen Tao
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Junwei Yan
- Affiliated Psychological Hospital of Anhui Medical University, Hefei, China; Anhui Mental Health Center, Hefei, China; Hefei Fourth People's Hospital, Hefei, China; Anhui Clinical Research Center for Mental Disorders, Hefei, China
| | - Hong Su
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China
| | - Cunrui Huang
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Shilu Tong
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China; School of Public Health, Institute of Environment and Population Health, Anhui Medical University, Hefei, China; Centre of Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia
| | - Hung Chak Ho
- Department of Public and International Affairs, City University of Hong Kong, Hong Kong, China
| | - Qingrong Xia
- Affiliated Psychological Hospital of Anhui Medical University, Hefei, China; Anhui Mental Health Center, Hefei, China; Hefei Fourth People's Hospital, Hefei, China; Anhui Clinical Research Center for Mental Disorders, Hefei, China
| | - Cuizhen Zhu
- Affiliated Psychological Hospital of Anhui Medical University, Hefei, China; Anhui Mental Health Center, Hefei, China; Hefei Fourth People's Hospital, Hefei, China; Anhui Clinical Research Center for Mental Disorders, Hefei, China
| | - Hao Zheng
- Department of Environmental Health, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Mohammad Zahid Hossain
- International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh
| | - Jian Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China; Anhui Province Key Laboratory of Major Autoimmune Disease, Hefei, China.
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