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Erratum: Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores

biomedical Data Science 1000年第1期

作者： Ying Wang Kristin Tsuo Masahiro Kanai Benjamin M. Neale Alicia R. Martin 2Stanley Center for Psychiatric Research and Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge Massachusetts USA 1Analytic and Translational Genetics Unit Massachusetts General Hospital Boston Massachusetts USA email: armartin@*** 3Biological and Biomedical Sciences Harvard Medical School Boston Massachusetts USA 4Department of Biomedical Informatics Harvard Medical School Boston Massachusetts USA 5Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan

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Exploring disparities of teleconsultation readiness: A comparative analysis of healthcare facilities in Indonesia

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DIGITAL HEALTH 2024年 10卷

作者： Daniel Chriswinanto Adityo Nugroho Katherina Adisaputro Mitra Andini Sigilipoe Ida Ayu Triastuti Suryani Hutomo Ardhinta Bellano Septarda Aryososepti Yuwono Caraka Randi Yusuf Dedik Sulistiawan Balqis Gusbela Jason C Hsu Emily Chia-Yu Su Faculty of Medicine Universitas Kristen Duta Wacana Yogyakarta IndonesiaGraduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Faculty of Medicine Universitas Kristen Duta Wacana Yogyakarta Indonesia Faculty of Public Health Universitas Ahmad Dahlan Yogyakarta IndonesiaSchool of Public Health Taipei Medical University Taipei International PhD Program in Biotech and Healthcare Management College of Management Taipei Medical University Taipei International PhD Program in Biotech and Healthcare Management College of Management Taipei Medical University TaipeiClinical Data Center Office of Data Science Taipei Medical University TaipeiClinical Big Data Research Center Taipei Medical University Hospital TaipeiResearch Center of Health Care Industry Data Science College of Management Taipei Medical University Taipei Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University TaipeiClinical Big Data Research Center Taipei Medical University Hospital TaipeiInstitute of Biomedical Informatics College of Medicine National Yang Ming Chiao Tung University Taipei

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Random-effects substitution models for phylogenetics via scalable gradient approximations

arXiv

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arXiv 2023年

作者： Magee, Andrew F. Holbrook, Andrew J. Pekar, Jonathan E. Caviedes-Solis, Itzue W. Matsen, Fredrick A. Baele, Guy Wertheim, Joel O. Ji, Xiang Lemey, Philippe Suchard, Marc A. Department of Biostatistics Jonathan and Karin Fielding School of Public Health University of California Los Angeles Los AngelesCA United States Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA United States Department of Biomedical Informatics University of California San Diega La Jolla CA United States Department of Biology Swarthmore College SwarthmorePA United States Howard Hughes Medical Institute SeattleWA United States Computational Biology Program Fred Hutchinson Cancer Research Center SeattleWA United States Department of Genome Sciences University of Washington SeattleWA United States Department of Statistics University of Washington SeattleWA United States Department of Microbiology Immunology and Transplantation Rega Institute KU Leuven Leuven Belgium Department of Medicine University of California San Diego La Jolla CA United States Department of Mathematics Tulane University New OrleansLA United States Department of Biomathematics David Geffen School of Medicine at UCLA University of California Los Angeles Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine at UCLA Universtiy of California Los Angeles Los AngelesCA United States

Phylogenetic and discrete-trait evolutionary inference depend heavily on an appropriate characterization of the underlying character substitution process. In this paper, we present random-effects substitution models that extend common continuous-time Markov chain models into a richer class of processes capable of capturing a wider variety of substitution dynamics. As these random-effects substitution models often require many more parameters than their usual counterparts, inference can be both statistically and computationally challenging. Thus, we also propose an efficient approach to compute an approximation to the gradient of the data likelihood with respect to all unknown substitution model parameters. We demonstrate that this approximate gradient enables scaling of sampling-based inference, namely Bayesian inference via Hamiltonian Monte Carlo, under random-effects substitution models across large trees and state-spaces. Applied to a dataset of 583 SARS-CoV-2 sequences, an HKY model with random-effects shows strong signals of nonreversibility in the substitution process, and posterior predictive model checks clearly show that it is a more adequate model than a reversible model. When analyzing the pattern of phylogeographic spread of 1441 influenza A virus (H3N2) sequences between 14 regions, a random-effects phylogeographic substitution model infers that air travel volume adequately predicts almost all dispersal rates. A random-effects state-dependent substitution model reveals no evidence for an effect of arboreality on the swimming mode in the tree frog subfamily Hylinae. Simulations reveal that random-effects substitution models can accommodate both negligible and radical departures from the underlying base substitution model. We show that our gradient-based inference approach is over an order of magnitude more time efficient than conventional approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Phylogenetic

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Age at menarche by birth cohort: A pooled analysis of half a million women in Asia

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Public Health 2024年 237卷 130-134页

作者： Abe, Sarah K. Nishio, Marisa Huang, Hsi-Lan Leung, Chi Yan Islam, Md Rashedul Rahman, Md Shafiur Saito, Eiko Shin, Aesun Merritt, Melissa A. Choi, Ji-Yeob Katagiri, Ryoko Mohammadi, Zahra Shu, Xiao-Ou Wakai, Kenji Sawada, Norie Ideno, Yuki Tamakoshi, Akiko Seow, Wei Jie Koh, Woon-Puay Sakata, Ritsu Hozawa, Atsushi Kim, Jeongseon Nagata, Chisato Sugawara, Yumi Park, Sue K. Kweon, Sun-Seog Azizi, Fereidoun Malekzadeh, Reza Moy, Foong-Ming Pourfarzi, Farhad Gao, Yu-Tang Kubo, Yoko Hirabayashi, Mayo Nagai, Kazue Kimura, Takashi Yuan, Jian-Min Kanemura, Seiki Wada, Keiko Kang, Daehee Shin, Min-Ho Khalili, Davood Poustchi, Hossein Rezaianzadeh, Abbas Mansour-Ghanaei, Fariborz Najafi, Farid Mohebbi, Iraj Boffetta, Paolo Lee, Jung Eun Matsuo, Keitaro Rothman, Nathaniel Qiao, You-Lin Zheng, Wei Inoue, Manami Division of Prevention National Cancer Center Institute for Cancer Control Japan Hitotsubashi Institute for Advanced Study Hitotsubashi University Japan Research Center for Child Mental Development Hamamatsu University School of Medicine Japan Sustainable Society Design Center Graduate School of Frontier Sciences the University of Tokyo Japan Department of Preventive Medicine Seoul National University College of Medicine Seoul South Korea Cancer Research Institute Seoul National University Seoul South Korea The Daffodil Centre The University of Sydney a joint venture with Cancer Council NSW Sydney NSW Australia Department of Biomedical Sciences Seoul National University Graduate School Seoul South Korea Graduate School of Informatics Faculty of Informatics Chiba University Japan Liver and Pancreatobiliary Diseases Research Center Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Division of Epidemiology Department of Medicine Vanderbilt Epidemiology Center Vanderbilt-Ingram Cancer Center Vanderbilt University Medical Center Nashville TN United States Department of Preventive Medicine Nagoya University School of Medicine Nagoya Japan Division of Cohort Research National Cancer Center Institute for Cancer Control Japan Gunma University Center for Food Science and Wellness Maebashi Japan Department of Public Health Hokkaido University Faculty of Medicine Sapporo Japan Saw Swee Hock School of Public Health and Department of Medicine National University of Singapore and National University Health System Singapore Healthy Longevity Translational Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore Institute for Clinical Sciences Agency for Science Technology and Research (A∗STAR) Singapore Department of Epidemiology Radiation Effects Research Foundation Hiroshima Japan Division of Epidemiology Department of Public Health and Forensic Medicine Tohoku University Graduate School

Objectives: To evaluate changes in the age at menarche in Asian populations. Study design: Retrospective cohort study. Methods: We included 548,830 women from six countries in Asia. The data were sourced from 20 cohorts participating in the Asia Cohort Consortium (ACC) and two additional cohort studies: Japan Multi-institutional Collaborative Cohorts (J-MICC), and Japan Nurse Health Study (JNHS) with data on age at menarche. Joinpoint regression was used to evaluate changes in age at menarche by birth year and by country. Results: The study includes data from cohorts in six Asian countries namely, China, Iran, Japan, Korea, Malaysia and Singapore. Birth cohorts ranged from 1873 to 1995. The mean age of menarche was 14.0 years with a standard deviation (SD) of 1.4 years, ranged from 12.6 to 15.5 years. Over 100 years age at menarche showed an overall decrease in all six countries. China showed a mixed pattern of decrease, increase, and subsequent decrease from 1926 to 1960. Iran and Malaysia experienced a sharp decline between about 1985 and 1990, with APC values of −4.48 and −1.24, respectively, while Japan, South Korea, and Singapore exhibited a nearly linear decline since the 1980s, notably with an APC of −3.41 in Singapore from 1993 to 1995. Conclusions: Overall, we observed a declining age at menarche, while the pace of the change differed by country. Additional long-term observation is needed to examine the contributing factors of differences in trend across Asian countries. The study could serve as a tool to strengthen global health campaigns. © 2024 The Royal Society for Public Health

关键词： Age Asia Birth cohort Menarche

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Development and evaluation of a hospital-wide healthcare-associated surgical site infection detection algorithm

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Journal of Microbiology, Immunology and Infection 2015年第2期48卷 S43-S43页

作者： Yi-Ju Tseng Bo-Chiang Huang Hui-Chi Lin Ming-Yuan Chen Rung-Ji Shang Wang-Huei Sheng Yee-Chun Chen Feipei Lai Shan-Chwen Chang Children's Hospital Informatics Program Boston Children's Hospital Boston MA USA Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taiwan Center for Infection Control National Taiwan University Hospital (NTUH) Taiwan Information Systems Office NTUH Taipei Taiwan Department of Internal Medicine NTUH and College of Medicine Taiwan National Institute of Infectious Diseases and Vaccinology National Health Research Institutes Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan

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Correction to: T-cell aging in end-stage renal disease: an evolving story with CMV

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Medical microbiology and immunology 2019年第6期208卷 885页

作者： TienYu Owen Yang Yi-Fang Chuang Yen-Ling Chiu Nuffield Department of Population Health University of Oxford Oxford UK. Wycombe Hospital Buckinghamshire Healthcare NHS Trust High Wycombe UK. International Health Program National Yang Ming University School of Public Health Taipei Taiwan. Department of Epidemiology National Yang Ming University School of Public Health Taipei Taiwan. Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan. yenlingchiu@ntu.edu.tw. Division of Nephrology Department of Medicine Far Eastern Memorial Hospital Taipei Taiwan. yenlingchiu@ntu.edu.tw. Graduate Institute of Clinical Medicine National Taiwan University College of Medicine Taipei 100 Taiwan. yenlingchiu@ntu.edu.tw.

Unfortunately in the original article the first author name incorrectly published as TienYu Yang. The correct name is TienYu Owen Yang.

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Microbiota analysis in the hemodialysis population - Focusing on Enterobacteriaceae

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Journal of microbiology, immunology, and infection = Wei mian yu gan ran za zhi 2023年第2期56卷 311-323页

作者： Hon-Yen Wu Yi-Tsung Lin Wan-Chuan Tsai Yen-Ling Chiu Mei-Ju Ko Ju-Yeh Yang Mei-Fen Pai Hsin-Sui Hsu Shih-Ping Hsu Yu-Sen Peng Chun-Hsing Liao Department of Internal Medicine Far Eastern Memorial Hospital New Taipei City Taiwan School of Medicine College of Medicine National Yang Ming Chiao Tung University Taipei City Taiwan Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei City Taiwan Department of Internal Medicine National Taiwan University Hospital and College of Medicine Taipei City Taiwan. Division of Infectious Diseases Department of Medicine Taipei Veterans General Hospital Taipei City Taiwan Institute of Emergency and Critical Care Medicine National Yang Ming Chiao Tung University Taipei City Taiwan. Center for General Education Lee-Ming Institute of Technology New Taipei City Taiwan. Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Graduate Institute of Medicine Yuan Ze University Taoyuan City Taiwan. Department of Dermatology Taipei City Hospital Taipei City Taiwan Department of Dermatology National Taiwan University Hospital and College of Medicine Taipei City Taiwan. Department of Internal Medicine Far Eastern Memorial Hospital New Taipei City Taiwan. School of Life Science National Taiwan Normal University Taipei City Taiwan. Department of Applied Cosmetology Lee-Ming Institute of Technology New Taipei City Taiwan Department of Healthcare Administration Asia Eastern University of Science and Technology New Taipei City Taiwan. School of Medicine College of Medicine National Yang Ming Chiao Tung University Taipei City Taiwan. Electronic address: liaoch@femh.org.tw.

BACKGROUND:Infection is a recognized risk factor for mortality among hemodialysis (HD) population, including infection caused by Enterobacteriaceae. We aimed to investigate Enterobacteriaceae in gut microbiota among HD patients and to analyze associations between microbiota and clinical parameters. METHODS:This prospective study of microbiota analysis in HD patients was conducted in April-May 2019. A control group without recent antibiotic use or hospitalization was used for comparison. Stool samples underwent 16S rRNA sequencing, using Greengenes 16S rRNA database for microbiota analysis. RESULTS:Among 96 hemodialysis (HD) patients, mean age was 61.9 ± 0.8 years and mean duration of HD was 6.5 ± 0.7 years. No significant differences were found in alpha diversity between HD and control groups (HD group 949.5, controls 898; p = 0.16) although significant between-group differences were found in beta diversity (p < 0.001). At phylum level, HD group had a higher abundance of Firmicutes and Proteobacteria, but lower abundance of Bacteriodetes. At genus level, Escherichia-Shigella complex increased among HD patients who had hospitalization with 1 year (median 0.024 vs 0.004, p = 0.054) and Klebsiella was associated with emergency room visit within 1 year among HD patients (p = 0.002). CONCLUSIONS:Alpha diversity in HD patients is not lower than that in healthy controls but significant between-group differences are found in microbiota composition according to beta diversity, due to decreased Bacteriodetes and increased Firmicutes and Proteobacteria. Deeper microbiota analyses for Enterobacteriaceae are necessary. Whether change in dietary components can help to decrease mortality among dialysis population warrants further research.

关键词： Chronic Enterobacteriaceae Gastrointestinal microbiome Hemodialysis Kidney failure Microbiota Prognosis

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Author Correction: Rare variant analyses in 51,256 type 2 diabetes cases and 370,487 controls reveal the pathogenicity spectrum of monogenic diabetes genes

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Nature genetics 2025年第4期57卷 1060页

作者： Alicia Huerta-Chagoya Philip Schroeder Ravi Mandla Jiang Li Lowri Morris Maheak Vora Ahmed Alkanaq Dorka Nagy Lukasz Szczerbinski Jesper G S Madsen Silvia Bonàs-Guarch Fanny Mollandin Joanne B Cole Bianca Porneala Kenneth Westerman Josephine H Li Toni I Pollin Jose C Florez Anna L Gloyn David J Carey Inês Cebola Uyenlinh L Mirshahi Alisa K Manning Aaron Leong Miriam Udler Josep M Mercader Programs in Metabolism and Medical & Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA. Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. Diabetes Unit Massachusetts General Hospital Boston MA USA. Graduate Program in Genomics and Computational Biology University of Pennsylvania Philadelphia PA USA. Department of Genomic Health Geisinger Danville PA USA. Section of Genetics and Genomics Department of Metabolism Digestion and Reproduction Imperial College London London UK. National Heart and Lung Institute Faculty of Medicine London UK. Department of Endocrinology Diabetology and Internal Medicine Medical University of Bialystok Bialystok Poland. Clinical Research Centre Medical University of Bialystok Bialystok Poland. Institute of Mathematics and Computer Science University of Southern Denmark Odense Denmark. The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease Broad Institute of MIT and Harvard Cambridge MA USA. Department of Biochemistry and Molecular Biology University of Southern Denmark Odense Denmark. Centre for Genomic Regulation The Barcelona Institute of Science and Technology Barcelona Spain. Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas Madrid Spain. Department of Metabolism Digestion and Reproduction Imperial College London London UK. Department of Medicine Harvard Medical School Boston MA USA. Division of Endocrinology Boston Children's Hospital Boston MA USA. Department of Biomedical Informatics University of Colorado School of Medicine Aurora CO USA. Division of General Internal Medicine Massachusetts General Hospital Boston MA USA. Department of Medicine Massachusetts General Hospital Boston MA USA. University of Maryland School of Medicine Baltimore MD USA. Endocrine Division Massachusetts General Hospital Boston MA USA. Department of Pediatrics Division of Endocrinology Stanford School of Medicine Stanford

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