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卫生经济学评价报告标准共识2022(CHEERS 2022):卫生经济学评价报告指导意见更新版

英国医学杂志中文版 2022年第8期25卷 460-465页

作者： Don Husereau Michael Drummond Federico Augustovski Esther de Bekker-Grob Andrew H Briggs Chris Carswell Lisa Caulley Nathorn Chaiyakunapruk Dan Greenberg Elizabeth Loder Josephine Mauskopf C Daniel Mullins Stavros Petrou Raoh-Fang Pwu Sophie Staniszewska on behalf of CHEERSISPOR Good Research Practices Task Force 肖月(译) 邱英鹏(译) 史黎炜(译) 张治国(译) 张歆(译) 王海银(译) 翟铁民(译) School of Epidemiology and Public Health University of OttawaOntarioCanada Institute of Health EconomicsAlbertaCanada Centre for Health Economics University of YorkYorkUK Health Technology Assessment and Health Economics Department of the Institute for Clinical Effectiveness and Health Policy(IECS-CONICET) Buenos Aires University of Buenos AiresBuenos Aires CONICET(National Scientific and Technical Research Council)Buenos AiresArgentina Erasmus School of Health Policy&Management Erasmus University RotterdamRotterdamThe Netherlands London School of Hygiene and Tropical Medicine LondonEnglandUK Adis Journals Springer NatureAucklandNew Zealand Department of Otolaryngology-Head&Neck Surgery University of OttawaOntarioCanada Clinical Epidemiology Program and Center for JournalologyOttawa Hospital Research InstituteOntarioCanada Department of EpidemiologyErasmus University Medical Center RotterdamRotterdamThe Netherlands Department of Pharmacotherapy College of PharmacyUniversity of UtahSalt Lake CityUtahUSA Department of Health Policy and Management School of Public HealthFaculty of Health SciencesBen-Gurion University of the NegevBe′er-ShevaIsrael Harvard Medical School BostonMAUSA The BMJLondonUK RTI Health Solutions RTI InternationalResearch Triangle ParkNCUSA School of Pharmacy University of Maryland BaltimoreBaltimoreMDUSA Nuffield Department of Primary Care Health Sciences University of OxfordOxfordUK National Hepatitis C Program Office Ministry of Health and WelfareTaipei CityTaiwanChina Warwick Research in Nursing University of Warwick Warwick Medical SchoolWarwickUK 不详国家卫生健康委卫生发展研究中心北京100043 国家药品和卫生技术综合评估中心北京00043 英国谢菲尔德大学(The University of Sheffield) 谢菲尔德S102TN 华中科技大学武汉430074 哈尔滨医科大学哈尔滨150086 上海市卫生和健康发展研究中心上海200031

卫生经济学评价是对备选行动方案的成本和结果的比较分析。2013年发表的《卫生经济学评价报告标准共识》(CHEERS)提出了卫生经济学评价的特点,确保结果正确阐释,以支持决策制订。2013版CHEERS旨在指导研究报告撰写者准确报告卫生干预和... 详细信息

卫生经济学评价是对备选行动方案的成本和结果的比较分析。2013年发表的《卫生经济学评价报告标准共识》(CHEERS)提出了卫生经济学评价的特点,确保结果正确阐释,以支持决策制订。2013版CHEERS旨在指导研究报告撰写者准确报告卫生干预和对照措施、背景、过程、结果等评价细节,使审稿人理解文章内容,帮助读者使用研究结果。本版共识将取代2013版共识,其更加适用于各类卫生经济学评价,结合学科发展和方法更新的需要,更注重患者、公众等相关利益方的参与。本共识适用多种个体或人群健康干预措施(简单或复杂)在不同政策场景的应用分析,包括医疗服务、公共卫生、教育、社会服务等。本文概要介绍了CHEERS 2022共识中包含的28个检查项及每一个检查项的相关建议。CHEERS 2022共识主要用于指导研究人员撰写拟投递同行评议学术期刊的卫生经济学评价文章,亦可用于期刊编辑和审稿专家对待发表文章的评价。熟悉了解本共识要求,还有助于研究人员规划评价研究。随着决策透明度要求提高,本共识可支持卫生技术评估机构建立评价报告标准。

关键词：期刊编辑审稿专家检查项学术期刊审稿人公共卫生人群健康评价报告

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Biomarkers of oxidative stress in maternal plasma, umbilical cord and placenta of patients with gestational diabetes: a systematic review and meta-analysis

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Free Radical Biology and Medicine 2025年

作者： Karina Etchegaray-Armijo Edson Bustos-Arriagada Deborah Navarro-Rosenblatt Claudio Vera María Luisa Garmendia Luis Sobrevia Camilo López-Alarcón Paola Casanello PhD Program in Nutrition and Food Universidad de Chile Santiago Chile School of Nutrition and Dietetics Faculty of Medicine Universidad Finis Terrae Santiago Chile Health Technology Assessment and Evidence Based Health Evidence Informed Health Policy Unit Ministerio de Salud Santiago Chile Department of Obstetrics School of Medicine Pontificia Universidad Católica de Chile Santiago Chile Institute of Nutrition and Food Technology (INTA) Universidad de Chile Cellular and Molecular Physiology Laboratory (CMPL) Department of Obstetrics Division of Obstetrics and Gynaecology School of Medicine Faculty of Medicine Pontificia Universidad Católica de Chile Santiago Chile. Faculty of Pharmacy Universidad de Sevilla Seville E-41012 Spain Medical School (Faculty of Medicine) Sao Paulo State University (UNESP) Brazil University of Queensland Centre for Clinical Research (UQCCR) Faculty of Medicine and Biomedical Sciences University of Queensland Herston QLD 4029 Queensland Australia Facultad de Química y de Farmacia Escuela de Química Departamento de Química Física Pontificia Universidad Católica de Chile Santiago Chile Department of Neonatology School of Medicine Pontificia Universidad Católica de Chile Santiago Chile

Objective To determine the association between Gestational Diabetes Mellitus (GDM) and oxidative stress biomarkers in the mother, placenta, and newborn. Methods We conducted a systematic review and meta-analysis following the PRISMA guidelines. We searched PubMed/MEDLINE and the Web of Science database without language restrictions. The search was updated on December 28, 2023, and included reports published between 1998 and 2021. Two independent reviewers performed article selection and data extraction. Results Our findings show a significant increase in malondialdehyde and 8-isoprostane levels in the maternal and placenta of women with GDM compared to those without GDM (Smd [95% CI] = 1.99 [1.51, 2.48], Tau 2 = 1.51, I 2 = 94%, p < 0.00001; Smd [95% CI] = 1.90 [0.08, 2.72], Tau 2 = 1.14, I 2 = 93%, p < 0.00001, respectively). Additionally, there was a decrease in superoxide dismutase (SOD) levels in maternal plasma (Smd [95% CI] = -2.80 [-5.23, -0.36], Tau 2 = 7.56, I 2 = 98%, p < 0.00001). However, no significant changes in SOD were observed in the placenta or the umbilical cord blood of offspring from women with GDM (Smd [95% CI] = -1.79 [-4.66, 1.07], Tau 2 = 6.23, I 2 = 97%, p < 0.00001; Smd [95% CI] = -1.07 [-3.37, 1.24], Tau 2 = 5.31, I 2 = 97%, p < 0.00001, respectively). Conclusion These results suggest an association between GDM and increased oxidative stress levels in both maternal and fetal circulation, as well as the placenta. The high heterogeneity in the results of the meta-analysis, which could be due to clinical and methodological factors, is a limitation of this study.

关键词： Antioxidants Biomarkers Gestational Diabetes Oxidative Stress Placenta Umbilical Cord blood

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Systems modeling of developmental vascular toxicity

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Current Opinion in Toxicology 2019年第1期15卷 55-63页

作者： Saili, Katerine S. Franzosa, Jill A. Baker, Nancy C. Ellis-Hutchings, Robert G. Settivari, Raja S. Carney, Edward W. Spencer, Richard M. Zurlinden, Todd J. Kleinstreuer, Nicole C. Li, Shuaizhang Xia, Menghang Knudsen, Thomas B. National Center for Computational Toxicology (NCCT) Office of Research and Development (ORD) U.S. Environmental Protection Agency (USEPA) Research Triangle Park 27711 NC United States Leidos Research Triangle Park 27711 NC United States Toxicology and Environmental Research and Consulting The Dow Chemical Company Midland 48674 MI United States General Dynamics Information Technology (GDIT) Research Triangle Park 27711 NC United States National Toxicology Program (NTP) National Institute of Environmental Health Sciences (NIEHS) Research Triangle Park 27711 NC United States National Center for Advancing Translational Sciences (NCATS) National Institutes of Health Bethesda 20892 MD United States

More than 80,000 chemicals in commerce present a challenge for hazard assessments that toxicity testing in the 21st century strives to address through high-throughput screening (HTS) assays. Assessing chemical effects on human development adds an additional layer of complexity to the screening, with a need to capture complex and dynamic events essential for proper embryo-fetal development. HTS data from ToxCast/Tox21 informs systems toxicology models, which incorporate molecular targets and biological pathways into mechanistic models describing the effects of chemicals on human cells, three-dimensional organotypic culture models, and small model organisms. Adverse outcome pathways (AOPs) provide a useful framework for integrating the evidence derived from these in silico and in vitro systems to inform chemical hazard characterization. To illustrate this formulation, we have built an AOP for developmental toxicity through a mode of action linked to embryonic vascular disruption (Aop43). Here, we review the model for quantitative prediction of developmental vascular toxicity from ToxCast HTS data and compare the HTS results to functional vascular development assays in complex cell systems, virtual tissues, and small model organisms. ToxCast HTS predictions from several published and unpublished assays covering different aspects of the angiogenic cycle were generated for a test set of 38 chemicals representing a range of putative vascular disrupting compounds. Results boost confidence in the capacity to predict adverse developmental outcomes from HTS in vitro data and model computational dynamics for in silico reconstruction of developmental systems biology. Finally, we demonstrate the integration of the AOP and developmental systems toxicology to investigate the unique modes of action of two angiogenesis inhibitors. © 2019

关键词： 5HPP-33 Adverse outcome pathway Angiogenesis TNP-470 Tox21 ToxCast

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The genome-wide association study of serum IgE levels demonstrated a shared genetic background in allergic diseases

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Clinical immunology (Orlando, Fla.) 2024年 260卷 109897页

作者： Hsing-Fang Lu Chen-Hsing Chou Ying-Ju Lin Shunsuke Uchiyama Chikashi Terao Yu-Wen Wang Jai-Sing Yang Ting-Yuan Liu Henry Sung-Ching Wong Sean Chun-Chang Chen Fuu-Jen Tsai Department of Medical Research China Medical University Hospital Taichung 404327 Taiwan Laboratory for Statistical and Translational Genetics RIKEN Center for Integrative Medical Sciences Yokohama Japan. PhD Program for Health Science and Industry College of Health Care China Medical University Taichung Taiwan Department of Health Services Administration China Medical University Taichung Taiwan. Genetic Center Department of Medical Research China Medical University Hospital Taichung Taiwan School of Chinese Medicine China Medical University Taichung Taiwan. Laboratory for Statistical and Translational Genetics RIKEN Center for Integrative Medical Sciences Yokohama Japan Clinical Research Center Shizuoka General Hospital Shizuoka Japan The Department of Applied Genetics The School of Pharmaceutical Sciences University of Shizuoka Shizuoka Japan. Department of Medical Research China Medical University Hospital Taichung 404327 Taiwan. Department of Medical Research China Medical University Hospital China Medical University Taichung 404327 Taiwan. Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan. School of Chinese Medicine China Medical University Taichung Taiwan Department of Biotechnology and Bioinformatics Asia University Taichung Taiwan. Electronic address: d0704@mail.cmuh.org.tw.

Immunoglobulin E (IgE) synthessis is highly related to a variety of atopic diseases, and several genome-wide association studies (GWASs) have demonstrated the association between genes and IgE level. In this study, we conducted the largest genome-wide association study of IgE involving a Taiwanese Han population. Eight independent variants exhibited genome-wide significance. Among them, an intronic SNP of CD28, rs1181388, and an intergenic SNP, rs1002957030, on 11q23.2 were identified as novel signals for IgE. Seven of the loci were replicated successfully in a meta-analysis using data on Japanese population. Among all the human leukocyte antigen (HLA) regions, HLA-DQA1*03:02 - HLA-DQB1*03:03 was the most significant haplotype (OR = 1.25, SE = 0.02, FDR = 1.6 × 10), corresponding to HLA-DQA1 Asp160 and HLA-DQB1 Leu87 amino acid residues. The genetic correlation showed significance between IgE and allergic diseases including asthma, atopic dermatitis, and pollinosis. IgE PRS was significantly correlated with total IgE levels. Furthermore, the top decile IgE polygenic risk score (PRS) group had the highest risk of asthma for the Taiwan Biobank and Biobank Japan cohorts. IgE PRS may be used to aid in predicting the occurrence of allergic reactions before symptoms occur and biomarkers are detectable. Our study provided a more comprehensive understanding of the impact of genomic variants, including complex HLA alleles, on serum IgE levels.

关键词： Allergy genome-wide association study human leukocyte antigen immunoglobulin E

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Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM_2·5 air pollution, 1990–2019: an analysis of data from the Global Burden of Disease Study 2019

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The Lancet Planetary health 2022年第7期6卷 e586-e600页

作者： Burkart, Katrin Causey, Kate Cohen, Aaron J Wozniak, Sarah S Salvi, Devashri Digvijay Abbafati, Cristiana Adekanmbi, Victor Adsuar, Jose C Ahmadi, Keivan Alahdab, Fares Al-Aly, Ziyad Alipour, Vahid Alvis-Guzman, Nelson Amegah, Adeladza Kofi Andrei, Catalina Liliana Andrei, Tudorel Ansari, Fereshteh Arabloo, Jalal Aremu, Olatunde Aripov, Timur Babaee, Ebrahim Banach, MacIej Barnett, Anthony Bärnighausen, Till Winfried Bedi, Neeraj Behzadifar, Masoud Béjot, Yannick Bennett, Derrick A Bensenor, Isabela M Bernstein, Robert S Bhattacharyya, Krittika Bijani, Ali Biondi, Antonio Bohlouli, Somayeh Breitner, Susanne Brenner, Hermann Butt, Zahid A Cámera, Luis Alberto Cantu-Brito, Carlos Carvalho, Felix Cerin, Ester Chattu, Vijay Kumar Chauhan, Bal Govind Choi, Jee-Young Jasmine Chu, Dinh-Toi Dai, Xiaochen Dandona, Lalit Dandona, Rakhi Daryani, Ahmad Davletov, Kairat De Courten, Barbora Demeke, Feleke Mekonnen Denova-Gutiérrez, Edgar Dharmaratne, Samath Dhamminda Dhimal, Meghnath Diaz, Daniel Djalalinia, Shirin Duncan, Bruce B El Sayed Zaki, Maysaa Eskandarieh, Sharareh Fareed, Mohammad Farzadfar, Farshad Fattahi, Nazir Fazlzadeh, Mehdi Fernandes, Eduarda Filip, Irina Fischer, Florian Foigt, Nataliya A Freitas, Marisa Ghashghaee, Ahmad Gill, Paramjit Singh Ginawi, Ibrahim Abdelmageed Gopalani, Sameer Vali Guo, Yuming Gupta, Rajat Das Habtewold, Tesfa Dejenie Hamadeh, Randah R Hamidi, Samer Hankey, Graeme J Hasanpoor, Edris Hassen, Hamid Yimam Hay, Simon I Heibati, Behzad Hole, Michael K Hossain, Naznin Househ, Mowafa Irvani, Seyed Sina Naghibi Jaafari, Jalil Jakovljevic, Mihajlo Jha, Ravi Prakash Jonas, Jost B Jozwiak, Jacek Jerzy Kasaeian, Amir Kaydi, Neda Khader, Yousef Saleh Khafaie, Morteza Abdullatif Khan, Ejaz Ahmad Khan, Junaid Khan, md Nuruzzaman Khatab, Khaled Khater, Amir M Kim, Yun Jin Kimokoti, Ruth W Kisa, Adnan Kivimäki, Mika Knibbs, Luke D Kosen, Soewarta Koul, Parvaiz A Koyanagi, Ai Defo, Barthelemy Kuate Kugbey, Nuworza Lauriola, Paolo Lee, Paul H Leili, Mostafa Lewycka, Sonia Li, Shanshan Lim, Lee-Ling Li Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Plastic and Reconstructive Surgery University of Washington Seattle WA United States Research Insights and Innovation Seattle WA United States Health Effects Institute Boston MA United States School of Public Health Boston University Boston MA United States Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Population Medicine Cardiff University Cardiff United Kingdom Sport Science Department University of Extremadura Badajoz Spain School of Public Health Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States John T. Milliken Department of Internal Medicine Washington University in St. Louis St. Louis MO United States Clinical Epidemiology Center US Department of Veterans Affairs (VA) St Louis MO United States Health Management and Economics Research Center Iran University of Medical Sciences Tehran Iran Department of Health Economics Iran University of Medical Sciences Tehran Iran Preventive Medicine and Public Health Research Center Iran University of Medical Sciences Tehran Iran Pars Advanced and Minimally Invasive Medical Manners Research Center Iran University of Medical Sciences Tehran Iran Department of Community and Family Medicine Iran University of Medical Sciences Tehran Iran Research Group in Hospital Management and Health Policies Universidad de la Costa (University of the Coast) Barranquilla Colombia Research Group in Health Economics University of Cartagena Cartagena Colombia Department of Biomedical Science University of Cape Coast Cape Coast Ghana Cardiology Department Ca

Background: Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM_2·5 originating from ambient and household air pollution. Methods: We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM_2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM_2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM_2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings: In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM_2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South Am

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GenoML: Automated machine learning for genomics

arXiv

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arXiv 2021年

作者： Makarious, Mary B. Leonard, Hampton L. Vitale, Dan Iwaki, Hirotaka Saffo, David Sargent, Lana Dadu, Anant Castaño, Eduardo Salmerón Carter, John F. Maleknia, Melina Botia, Juan A. Blauwendraat, Cornelis Campbell, Roy H. Hashemi, Sayed Hadi Singleton, Andrew B. Nalls, Mike A. Faghri, Faraz Laboratory of Neurogenetics National Institute on Aging National Institutes of Health BethesdaMD United States Department of Clinical and Movement Neurosciences UCL Queen Square Institute of Neurology London United Kingdom UCL Movement Disorders Centre University College London London United Kingdom Center for Alzheimer’s and Related Dementias National Institutes of Health BethesdaMD United States Data Tecnica International LLC Glen EchoMD United States Tübingen Germany Khoury College of Computer Sciences Northeastern University BostonMA United States School of Nursing Virginia Commonwealth University RichmondVA United States Geriatric Pharmacotherapy Program School of Pharmacy Virginia Commonwealth University VA United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL United States Departamento de Ingeniería de la Información y las Comunicaciones Universidad de Murcia Spain ModelOp ChicagoIL United States Georgia Institute of Technology AtlantaGA United States Department of Molecular Neuroscience UCL Queen Square Institute of Neurology London United Kingdom

GenoML is a Python package automating machine learning workflows for genomics (genetics and multi-omics) with an open science philosophy. Genomics data require significant domain expertise to clean, pre-process, harmonize and perform quality control of the data. Furthermore, tuning, validation, and interpretation involve taking into account the biology and possibly the limitations of the underlying data collection, protocols, and technology. GenoML’s mission is to bring machine learning for genomics and clinical data to non-experts by developing an easy-to-use tool that automates the full development, evaluation, and deployment process. Emphasis is put on open science to make workflows easily accessible, replicable, and transferable within the scientific community. Source code and documentation is available at https://***. Copyright © 2021, The Authors. All rights reserved.

关键词： Genome

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Classification of Major Depressive Disorder Using Vertex-Wise Brain Sulcal Depth, Curvature, and Thickness with a Deep and a Shallow Learning Model

arXiv

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arXiv 2023年

作者： Goya-Maldonado, Roberto Erwin-Grabner, Tracy Zeng, Ling-Li Ching, Christopher R.K. Aleman, Andre Amod, Alyssa R. Basgoze, Zeynep Benedetti, Francesco Besteher, Bianca Brosch, Katharina Bülow, Robin Colle, Romain Connolly, Colm G. Corruble, Emmanuelle Couvy-Duchesne, Baptiste Cullen, Kathryn Dannlowski, Udo Davey, Christopher G. Dols, Annemiek Ernsting, Jan Evans, Jennifer W. Fisch, Lukas Fuentes-Claramonte, Paola Gonul, Ali Saffet Gotlib, Ian H. Grabe, Hans J. Groenewold, Nynke A. Grotegerd, Dominik Hahn, Tim Hamilton, J. Paul Han, Laura K.M. Harrison, Ben J. Ho, Tiffany C. Jahanshad, Neda Jamieson, Alec J. Karuk, Andriana Kircher, Tilo Klimes-Dougan, Bonnie Koopowitz, Sheri-Michelle Lancaster, Thomas Leenings, Ramona Li, Meng Linden, David E.J. MacMaster, Frank P. Mehler, David M.A. Meinert, Susanne Melloni, Elisa Mueller, Bryon A. Mwangi, Benson Nenadić, Igor Ojha, Amar Okamoto, Yasumasa Oudega, Mardien L. Penninx, Brenda W.J.H. Poletti, Sara Pomarol-Clotet, Edith Portella, Maria J. Pozzi, Elena Radua, Joaquim Rodríguez-Cano, Elena Sacchet, Matthew D. Salvador, Raymond Schrantee, Anouk Sim, Kang Soares, Jair C. Solanes, Aleix Stein, Dan J. Stein, Frederike Stolicyn, Aleks Thomopoulos, Sophia I. Toenders, Yara J. Uyar-Demir, Aslihan Vieta, Eduard Vives-Gilabert, Yolanda Völzke, Henry Walter, Martin Whalley, Heather C. Whittle, Sarah Winter, Nils Wittfeld, Katharina Wright, Margaret J. Wu, Mon-Ju Yang, Tony T. Zarate, Carlos Veltman, Dick J. Schmaal, Lianne Thompson, Paul M. Georg-August University Göttingen Germany College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China Imaging Genetics Center Mark & Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine University of Southern California Marina del ReyCA90274 United States Department of Biomedical Sciences of Cells and Systems University Medical Center Groningen University of Groningen Groningen Netherlands Department of Psychiatry & Mental Health Neuroscience Institute University of Cape Town Cape Town South Africa Department of Psychiatry and Behavioral Science University of Minnesota Medical School MinneapolisMN United States Division of Neuroscience IRCCS Scientific Institute Ospedale San Raffaele Milano Italy Department of Psychiatry and Psychotherapy Jena University Hospital Jena Germany Department of Psychiatry and Psychotherapy University of Marburg Rudolf Bultmann Str. 8 Marburg35039 Germany Institute for Radiology and Neuroradiology University Medicine Greifswald Greifswald Germany MOODS Team CESP INSERM U1018 Faculté de Médecine Univ Paris-Saclay Le Kremlin Bicêtre94275 France Service Hospitalo Universitaire de Psychiatrie de Bicêtre Hôpitaux Universitaires Paris-Saclay Assistance Publique-Hôpitaux deParis Hôpital de Bicêtre Le Kremlin BicêtreF-94275 France Department of Biomedical Sciences Florida State University TallahasseeFL United States Sorbonne University Paris Brain Institute - ICM CNRS Inria Inserm AP-HP Hôpital de la Pitié Salpêtrière ParisF-75013 France Institute for Molecular Bioscience the University of Queensland St LuciaQLD Australia Institute for Translational Psychiatry University of Münster Münster Germany Melbourne Neuropsychiatry Centre Department of Psychiatry the University of Melbourne ParkvilleVIC Australia Department of Psychiatry Amsterdam UMC Vrije Universiteit Amsterdam Amsterdam Neuroscience Amsterdam Public Health Research Institute Am

Major depressive disorder (mdD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to mdD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for mdD. However, previous attempts to demarcate mdD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-mdD working group containing 7,012 participants from 30 sites (N=2,772 mdD and N=4,240 HC), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%;SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%;SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between mdD and HC. Our results support the notion that mdD classification on this combination of features and classifiers is unfeasible. Future studies are needed to determine whether more sophisticated integration of information from ot

关键词： Support vector machines

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Iran in transition (vol 393, pg 1984, 2019)

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LANCET 2019年第10186期393卷 2124-2124页

作者： Danaei, G. Farzadfar, F. Kelishadi, R. [a]Department of Global Health and Population and Department of Epidemiology Harvard T H Chan School of Public Health Boston MA USA Department of Global Health and Population and Department of Epidemiology Harvard T H Chan School of Public Health Boston MA USA [b]Scientific Association for Public Health in Iran Boston MA USA Scientific Association for Public Health in Iran Boston MA USA [c]Non-Communicable Diseases Research Center Endocrinology and Metabolism Population Sciences Institute Tehran University of Medical Sciences Tehran Iran Non-Communicable Diseases Research Center Endocrinology and Metabolism Population Sciences Institute Tehran University of Medical Sciences Tehran Iran [d]Endocrinology and Metabolism Research Center Tehran University of Medical Sciences Tehran Iran Endocrinology and Metabolism Research Center Tehran University of Medical Sciences Tehran Iran [e]Diabetes Research Center Tehran University of Medical Sciences Tehran Iran Diabetes Research Center Tehran University of Medical Sciences Tehran Iran [f]Endocrinology and Metabolism Clinical Sciences Institute Department of Health Management and Economics Tehran University of Medical Sciences Tehran Iran Endocrinology and Metabolism Clinical Sciences Institute Department of Health Management and Economics Tehran University of Medical Sciences Tehran Iran [g]Department of Global Health and Public Policy Tehran University of Medical Sciences Tehran Iran Department of Global Health and Public Policy Tehran University of Medical Sciences Tehran Iran [h]Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran [i]School of Public Health Tehran University of Medical Sciences Tehran Iran School of Public Health Tehran University of Medical Sciences Tehran Iran [j]Faculty of Medicine Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Te

Summary Being the second-largest country in the Middle East, Iran has a long history of civilisation during which several dynasties have been overthrown and established and health-related structures have been reorganised. Iran has had the replacement of traditional practices with modern medical treatments, emergence of multiple pioneer scientists and physicians with great contributions to the advancement of science, environmental and ecological changes in addition to large-scale natural disasters, epidemics of multiple communicable diseases, and the shift towards non-communicable diseases in recent decades. Given the lessons learnt from political instabilities in the past centuries and the approaches undertaken to overcome health challenges at the time, Iran has emerged as it is today. Iran is now a country with a population exceeding 80 million, mainly inhabiting urban regions, and has an increasing burden of non-communicable diseases, including cardiovascular diseases, hypertension, diabetes, malignancies, mental disorders, substance abuse, and road injuries.

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Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study (vol 2, pg 144, 2021)

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LANCET GLOBAL health 2021年第4期9卷 E408-E408页

作者： Steinmetz, Jaimie D. Vision and Eye Research Institute Anglia Ruskin University Cambridge United Kingdom Biomedical Informatics Seoul National University Hospital Seoul South Korea Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Institute of Health Services Research Yonsei University Seoul South Korea Department of Ophthalmology University of Washington Seattle WA United States Department of Mathematics Imperial College London London United Kingdom WHO Collaborating Centre for Public Health Education and Training Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom School of Population and Global Health University of Melbourne Carlton VIC Australia School of Optometry and Vision Science UNSW Sydney NSW Australia Brien Holden Vision Institute Sydney Australia Department of Ophthalmology University of Adelaide Adelaide SA Australia Department of Parasitology and Mycology Jahrom University of Medical Sciences Jahrom Iran Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Ophthalmic Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Ophthalmology Shahid Beheshti University of Medical Sciences Tehran Iran Research Institute for Endocrine Sciences Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic Epidemiology Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Health Services Management Department Qazvin University of Medical Sciences Qazvin Iran Department of Systems Populations and Leadership University of Michigan Ann Arbor MI United States Department of Medicine Sofia Ismail Memorial Medical Centre Bogura Bangladesh The Fred Hollows Foundation Sydney NSW Australia Berhan Public Health & Eye Care Consultancy Addis A

Hospital El Salvador: a novel paradigm of intensive care in response to COVID-19 in central America. Lancet Glob health 2021;9: e241?42?In this Comment, the conflict of interest statement should have included the following: ?By virtue of their roles within a public hospital or the Ministry of health, MB, LC, WH, and XS are government employees. The findings and conclusions in the Comment are only those of the authors.? This correction has been made as of Feb 26, 2021.

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Sensory experience remodels genome architecture in neural circuit to drive motor learning (vol 569, pg 708, 2019)

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NATURE 2019年第7760期570卷 E33-E33页

作者： Yamada, Tomoko Yang, Yue Valnegri, Pamela Juric, Ivan Abnousi, Armen Markwalter, Kelly H. Guthrie, Arden N. Godec, Abigail Oldenborg, Anna Hu, Ming Holy, Timothy E. Bonni, Azad Department of Neuroscience Washington University School of Medicine St. Louis USA Faculty of Medicine University of Tsukuba Tsukuba Japan Department of Neurobiology Northwestern University Evanston USA Department of Quantitative Health Sciences Lerner Research Institute Cleveland Clinic Foundation Cleveland USA MD-PhD Program Washington University School of Medicine St. Louis USA

In this Letter, '≥' should be '≤' in the sentence: "Intra-chromosomal reads were further split into short-range reads (≥1 kb) and long-range reads (>1 kb)". This error has been correcte...

In this Letter, '≥' should be '≤' in the sentence: "Intra-chromosomal reads were further split into short-range reads (≥1 kb) and long-range reads (>1 kb)". This error has been corrected *** amendment to this paper has been published and can be accessed via a link at the top of the paper.

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