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Pruritus and Its Association With Cancer and Mortality in Dermatomyositis and Polymyositis: A Nationwide Cohort Study in Taiwan From 2005 to 2022

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Dermatologic Therapy 2025年第1期2025卷

作者： Der-Jr Huang Yu-Hsuan Joni Shao Yi-Hsien Shih Woan-Ruoh Lee Ling-Ya Huang Yu-Min Kuo Quoc Thao Trang Pham Hao-Jui Weng Department of Dermatology Taipei Medical University-Shuang Ho Hospital New Taipei City Taiwan shh.org.tw Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan tmu.edu.tw Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan tmuh.org.tw Health Data Analytics and Statistics Center Office of Data Science Taipei Medical University Taipei Taiwan tmu.edu.tw Department of Dermatology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan tmu.edu.tw Graduate Institute of Medical Sciences College of Medicine Taipei Medical University Taipei Taiwan tmu.edu.tw Division of Allergy Immunology and Rheumatology Department of Internal Medicine National Taiwan University Hospital and National Taiwan University Taipei Taiwan ntu.edu.tw Graduate Institute of Clinical Medicine College of Medicine National Taiwan University Taipei Taiwan ntu.edu.tw International Ph.D. Program in Cell Therapy and Regenerative Medicine College of Medicine Taipei Medical University Taipei Taiwan tmu.edu.tw Department of Dermatology Faculty of Medicine University of Medicine and Pharmacy at Ho Chi Minh City Ho Chi Minh City Vietnam hcmut.edu.vn Graduate Institute of Clinical Medicine College of Medicine Taipei Medical University Taipei Taiwan medcol.mw

Background: Pruritus is the most common initial symptom reported by patients with dermatomyositis (DM) and polymyositis (PM). However, there is limited data regarding the impact of pruritus on cancer and mortality in patients with DM and PM. In this study, we aimed to investigate how pruritus is associated with cancer and mortality in patients with DM and PM. Methods: This nationwide, population-based retrospective cohort study included adult DM and PM patients from Taiwan’s National Health Insurance Research database between 2005 and 2022. Sex- and age-matched pruritic patients, identified by over 6 weeks of antipruritic medication use, and nonpruritic patients were analyzed. The primary outcome was cancer occurrence or all-cause mortality. The association between pruritus and these outcomes was estimated using Cox proportional hazards models. Results: Among 919 matched pairs of pruritic and nonpruritic patients, cancer was observed in 19.96% in the long-term pruritic group (LPG), 14.63% in the short-term pruritic group (SPG), and 10.34% in the non-pruritic group (NPG) ( p < 0.0001). All-cause mortality was documented as 30.37% in the LPG, 29.69% in the SPG, and 37.76% in the NPG ( p < 0.0001). After adjusting for sex, age, and other comorbidities, pruritus was associated with an increased risk of cancer (hazard ratio (HR) 1.708, 95% confidence interval (CI) 1.229–2.374) and a lower risk of all-cause mortality (HR 0.483, 95% CI 0.409–0.569). Conclusion: This population-based study revealed that pruritus appeared to be associated with increased risks of cancer and decreased all-cause mortality. Thus, pruritus may serve as a pragmatic factor for risk stratification and tailored treatment strategies in DM and PM. Cancer screening, particularly for nasopharyngeal and breast cancers in East Asian populations, is recommended for patients with DM or PM, especially those presenting with pruritus. Meanwhile, patients without pruritus may require vigilant management for pote

关键词： cancer dermatomyositis idiopathic inflammatory myopathies mortality polymyositis pruritus

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Multi-view hybrid graph convolutional network for volume-to-mesh reconstruction in cardiovascular MRI

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Medical image analysis 2025年 104卷 103630页

作者： Nicolás Gaggion Benjamin A Matheson Yan Xia Rodrigo Bonazzola Nishant Ravikumar Zeike A Taylor Diego H Milone Alejandro F Frangi Enzo Ferrante Institute for Signals Systems and Computational Intelligence sinc(i) CONICET-UNL Santa Fe Argentina Institute of Computer Sciences (ICC) CONICET-Universidad de Buenos Aires Ciudad Autónoma de Buenos Aires Argentina. Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB) School of Computing University of Leeds Leeds UK. Institute for Signals Systems and Computational Intelligence sinc(i) CONICET-UNL Santa Fe Argentina. Christabel Pankhurst Institute The University of Manchester Manchester UK Centre for Computational Imaging and Modelling in Medicine (CIMIM) Department of Computer Science School of Engineering and Division of Informatics Imaging and Data Science School of Health Sciences The University of Manchester Manchester UK NIHR Manchester Biomedical Research Centre Manchester Academic Health Sciences Centre University of Manchester Manchester UK Medical Imaging Research Centre (MIRC) Department of Cardiovascular Sciences KU Leuven Leuven Belgium. Institute of Computer Sciences (ICC) CONICET-Universidad de Buenos Aires Ciudad Autónoma de Buenos Aires Argentina. Electronic address: eferrante@sinc.unl.edu.ar.

Cardiovascular magnetic resonance imaging is emerging as a crucial tool to examine cardiac morphology and function. Essential to this endeavour are anatomical 3D surface and volumetric meshes derived from CMR images, which facilitate computational anatomy studies, biomarker discovery, and in-silico simulations. Traditional approaches typically follow complex multi-step pipelines, first segmenting images and then reconstructing meshes, making them time-consuming and prone to error propagation. In response, we introduce HybridVNet, a novel architecture for direct image-to-mesh extraction seamlessly integrating standard convolutional neural networks with graph convolutions, which we prove can efficiently handle surface and volumetric meshes by encoding them as graph structures. To further enhance accuracy, we propose a multi-view HybridVNet architecture which processes both long axis and short axis CMR, showing that it can increase the performance of cardiac MR mesh generation. Our model combines traditional convolutional networks with variational graph generative models, deep supervision and mesh-specific regularisation. Experiments on a comprehensive dataset from the UK Biobank confirm the potential of HybridVNet to significantly advance cardiac imaging and computational cardiology by efficiently generating high-fidelity meshes from CMR images. Multi-view HybridVNet outperforms the state-of-the-art, achieving improvements of up to ∼27% reduction in Mean Contour Distance (from 1.86 mm to 1.35 mm for the LV Myocardium), up to ∼18% improvement in Hausdorff distance (from 4.74 mm to 3.89 mm, for the LV Endocardium), and up to ∼8% in Dice Coefficient (from 0.78 to 0.84, for the LV Myocardium), highlighting its superior accuracy.

关键词： Cardiac imaging Geometric deep learning Hybrid graph convolutional neural network Volume-to-mesh

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Workshop proceedings: GWAS summary statistics standards and sharing

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Cell Genomics 2021年第1期1卷 100004-100004页

作者： MacArthur, Jacqueline A.L. Buniello, Annalisa Harris, Laura W. Hayhurst, James McMahon, Aoife Sollis, Elliot Cerezo, Maria Hall, Peggy Lewis, Elizabeth Whetzel, Patricia L. Bahcall, Orli G. Barroso, Inês Carroll, Robert J. Inouye, Michael Manolio, Teri A. Rich, Stephen S. Hindorff, Lucia A. Wiley, Ken Parkinson, Helen European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Genome Campus Hinxton United Kingdom BHF Data Science Centre Health Data Research UK London United Kingdom Division of Genomic Medicine National Human Genome Research Institute National Institutes of Health Bethesda 20892 MD United States Cell Genomics Cell Press 50 Hampshire St. 5th Floor Cambridge 02139 MA United States Exeter Centre of Excellence in Diabetes (EXCEED) University of Exeter Medical School Exeter United Kingdom Department of Biomedical Informatics Vanderbilt University Medical Center Nashville TN United States Cambridge Baker Systems Genomics Initiative Department of Public Health and Primary Care University of Cambridge Cambridge CB1 8RN United Kingdom Cambridge Baker Systems Genomics Initiative Baker Heart and Diabetes Institute 75 Commercial Rd. Melbourne 3004 VIC Australia The Alan Turing Institute London United Kingdom Center for Public Health Genomics University of Virginia Charlottesville 22908 VA United States

Genome-wide association studies (GWASs) have enabled robust mapping of complex traits in humans. The open sharing of GWAS summary statistics (SumStats) is essential in facilitating the larger meta-analyses needed for increased power in resolving the genetic basis of disease. However, most GWAS SumStats are not readily accessible because of limited sharing and a lack of defined standards. With the aim of increasing the availability, quality, and utility of GWAS SumStats, the National Human Genome Research Institute-European Bioinformatics Institute (NHGRI-EBI) GWAS Catalog organized a community workshop to address the standards, infrastructure, and incentives required to promote and enable sharing. We evaluated the barriers to SumStats sharing, both technological and sociological, and developed an action plan to address those challenges and ensure that SumStats and study metadata are findable, accessible, interoperable, and reusable (FAIR). We encourage early deposition of datasets in the GWAS Catalog as the recognized central repository. We recommend standard requirements for reporting elements and formats for SumStats and accompanying metadata as guidelines for community standards and a basis for submission to the GWAS Catalog. Finally, we provide recommendations to enable, promote, and incentivize broader data sharing, standards and FAIRness in order to advance genomic medicine. © 2022 The Authors

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Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

arXiv

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arXiv 2022年

作者： Ali, Sharib Ghatwary, Noha Jha, Debesh Isik-Polat, Ece Polat, Gorkem Yang, Chen Li, Wuyang Galdran, Adrian Ballester, Miguel-Ángel González Thambawita, Vajira Hicks, Steven Poudel, Sahadev Lee, Sang-Woong Jin, Ziyi Gan, Tianyuan Yu, ChengHui Yan, JiangPeng Yeo, Doyeob Lee, Hyunseok Tomar, Nikhil Kumar Haithmi, Mahmood Ahmed, Amr Riegler, Michael A. Daul, Christian Halvorsen, Pål Rittscher, Jens Salem, Osama E. Lamarque, Dominique Cannizzaro, Renato Realdon, Stefano de Lange, Thomas East, James E. Institute of Biomedical Engineering Department of Engineering Science University of Oxford OxfordOX3 7DQ United Kingdom Oxford National Institute for Health Research Biomedical Research Centre OxfordOX4 2PG United Kingdom Big Data Institute University of Oxford Li Ka Shing Centre for Health Information and Discovery OxfordOX3 7LF United Kingdom Translational Gastroenterology Unit Nuffield Department of Medicine Experimental Medicine Div. John Radcliffe Hospital University of Oxford OxfordOX3 9DU United Kingdom Computer Engineering Department Arab Academy for Science and Technology Alexandria1029 Egypt SimulaMet Oslo0167 Norway Department of Computer Science UiT The Arctic University of Norway Hansine Hansens veg 18 Tromsø9019 Norway Medical Department Sahlgrenska University Hospital-Mölndal Blå stråket 5 Göteborg413 45 Sweden Veneto Institute of Oncology IOV-IRCCS Via Gattamelata 64 Padua35128 Italy CRO Centro Riferimento Oncologico IRCCS Aviano Italy Via Franco Gallini 2 PN Aviano33081 Italy Université de Versailles St-Quentin en Yvelines Hôpital Ambroise Paré 9 Av. Charles de Gaulle Boulogne-Billancourt92100 France Faculty of Medicine University of Alexandria Alexandria21131 Egypt Graduate School of Informatics Middle East Technical University Ankara06800 Turkey City University of Hong Kong Kowloon Hong Kong BCN MedTech Dept. of Information and Communication Technologies Universitat Pompeu Fabra Barcelona08018 Spain Department of IT Convergence Engineering Gachon University Seongnam13120 Korea Republic of College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou310027 China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Department of Automation Tsinghua University Beijing100084 China Smart Sensing & Diagnosis Research Division Korea Atomic Energy Research Institute 34057 Korea Republic of Daegu-Gyeongbuk Medical Innovation Foundation Medical Device Deve

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, location, and surface largely affect identification, localisation, and characterisation. Moreover, colonoscopic surveillance and removal of polyps (referred to as polypectomy) are highly operator-dependent procedures. There exist a high missed detection rate and incomplete removal of colonic polyps due to their variable nature, the difficulties to delineate the abnormality, the high recurrence rates, and the anatomical topography of the colon. There have been several developments in realising automated methods for both detection and segmentation of these polyps using machine learning. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets that come from different centres, modalities and acquisition systems. To test this hypothesis rigorously we curated a multi-centre and multi-population dataset acquired from multiple colonoscopy systems and challenged teams comprising machine learning experts to develop robust automated detection and segmentation methods as part of our crowd-sourcing Endoscopic computer vision challenge (EndoCV) 2021. In this paper, we analyse the detection results of the four top (among seven) teams and the segmentation results of the five top teams (among 16). Our analyses demonstrate that the top-ranking teams concentrated on accuracy (i.e., accuracy > 80% on overall Dice score on different validation sets) over real-time performance required for clinical applicability. We further dissect the methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets. © 2022, CC BY-NC-SA.

关键词： Endoscopy

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Corrigendum to "Investigating nurses' acceptance of patients' bring your own device implementation in a clinical setting: A pilot study" [Asia-Pacific J Oncol Nurs 10 (2023) 100195]

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Asia-Pacific journal of oncology nursing 2024年第5期11卷 100437页

作者： Shuo-Chen Chien Chun-You Chen Chia-Hui Chien Usman Iqbal Hsuan-Chia Yang Huei-Chia Hsueh Shuen-Fu Weng Wen-Shan Jian Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Artificial Intelligence Research and Development Center Wan Fang Hospital Taipei Medical University Taipei Taiwan. International Center for Health Information and Technology College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Department of Radiation Oncology Wan Fang Hospital Taipei Medical University Taipei Taiwan. Office of Public Affairs Taipei Medical University Taipei Taiwan. Health ICT Department of Health Tasmania Australia. Global Health and Health Security Department College of Public Health Taipei Medical University Taipei Taiwan. Department of Artificial Intelligence in Medicine Professional Master Program Taipei Medical University Taipei Taiwan. Department of Pharmacy Taipei Veterans General Hospital Taipei Taiwan. Division of Endocrinology and Metabolism Department of Internal Medicine Taipei Medical University Hospital Taipei Taiwan. Division of Endocrinology and Metabolism Department of Internal Medicine School of Medicine College of Medicine Taipei Medical University Taipei Taiwan. School of Health Care Administration Taipei Medical University Taipei Taiwan. School of Gerontology Health Management College of Nursing Taipei Medical University Taipei Taiwan. Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan. Graduate Institute of Data Science Taipei Medical University Taipei Taiwan.

[This corrects the article DOI: 10.1016/***.2023.100195.].

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Lessons learned from the IMMREP23 TCR-epitope prediction challenge

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Immunoinformatics 2024年 16卷

作者： Morten Nielsen Anne Eugster Mathias Fynbo Jensen Manisha Goel Andreas Tiffeau-Mayer Aurelien Pelissier Sebastiaan Valkiers María Rodríguez Martínez Barthélémy Meynard-Piganeeau Victor Greiff Thierry Mora Aleksandra M. Walczak Giancarlo Croce Dana L Moreno David Gfeller Pieter Meysman Justin Barton Department of Health Technology Technical University of Denmark Lyngby DK-2800 Denmark Center for Regenerative Therapies Dresden Faculty of Medicine TU Dresden Dresden Germany Division of Infection and Immunity University College London United Kingdom Institute of Computational Life Sciences ZHAW 8400 Winterthur Switzerland Department of Oncology UNIL CHUV Ludwig Institute for Cancer Research University of Lausanne Lausanne Switzerland Biomedical Informatics and Data Science Yale School of Medicine 06510 New Haven United States Computational and Quantitative Biology LCQB UMR 7238 Institut de Biologie Paris Seine CNRS Sorbonne Universite Paris 75005 France Department of Immunology University of Oslo and Oslo University Hospital Oslo Norway Laboratoire de Physique de l'Ecole Normale Supérieure ENS Université PSL CNRS Sorbonne Université Université de Paris Cité F-75005 Paris France University of Antwerp Antwerp Belgium Institute of Structural and Molecular Biology University of London United Kingdom

Here, we present the findings from IMMREP23, the second benchmark competition focused on predicting the specificity of TCR-pMHC interactions. The interaction of T cell receptors (TCR) towards their pMHC target is a cornerstone of the cellular immune system. Over the last decade, substantial progress has been made within the field of TCR specificity prediction, providing proof of concept for predicting TCR-pMHC interactions in a narrow space of “seen” pMHC targets where substantial training data is available. However, a significant challenge persists in extending the predictive capability to novel “unseen” pMHC targets. Furthermore, the performance of proposed methods is often challenged when evaluated outside the initial publication and data sets. To address these issues, IMMREP23 challenge invited participants to predict, for a given test set of TCR-pMHC pairs, the likelihood that a pair would bind. A total of 53 teams participated, providing a total of 398 submissions. The benchmark confirms that current methods achieve reasonable performance in the "seen" pMHC setting. However, most participating methods had close to random performance on the subset of “unseen” peptides, underlining that this prediction challenge remains essentially unsolved. Finally, another key lesson from the benchmark is the critical issue of data leakage. Specifically, the data set construction procedure employed in IMMREP23 led to biases in the negative test data set. These biases were identified by several participating teams, and complicated the interpretation of the benchmark results. Based on these results, we put forward suggestions on how future competitions could avoid such data leakages and biases.

关键词： TCR specificity Benchmark Prediction Machine learning

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Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

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Nature 2024年第8003期627卷 347-357页

作者： Ken Suzuki Konstantinos Hatzikotoulas Lorraine Southam Henry J Taylor Xianyong Yin Kim M Lorenz Ravi Mandla Alicia Huerta-Chagoya Giorgio E M Melloni Stavroula Kanoni Nigel W Rayner Ozvan Bocher Ana Luiza Arruda Kyuto Sonehara Shinichi Namba Simon S K Lee Michael H Preuss Lauren E Petty Philip Schroeder Brett Vanderwerff Mart Kals Fiona Bragg Kuang Lin Xiuqing Guo Weihua Zhang Jie Yao Young Jin Kim Mariaelisa Graff Fumihiko Takeuchi Jana Nano Amel Lamri Masahiro Nakatochi Sanghoon Moon Robert A Scott James P Cook Jung-Jin Lee Ian Pan Daniel Taliun Esteban J Parra Jin-Fang Chai Lawrence F Bielak Yasuharu Tabara Yang Hai Gudmar Thorleifsson Niels Grarup Tamar Sofer Matthias Wuttke Chloé Sarnowski Christian Gieger Darryl Nousome Stella Trompet Soo-Heon Kwak Jirong Long Meng Sun Lin Tong Wei-Min Chen Suraj S Nongmaithem Raymond Noordam Victor J Y Lim Claudia H T Tam Yoonjung Yoonie Joo Chien-Hsiun Chen Laura M Raffield Bram Peter Prins Aude Nicolas Lisa R Yanek Guanjie Chen Jennifer A Brody Edmond Kabagambe Ping An Anny H Xiang Hyeok Sun Choi Brian E Cade Jingyi Tan K Alaine Broadaway Alice Williamson Zoha Kamali Jinrui Cui Manonanthini Thangam Linda S Adair Adebowale Adeyemo Carlos A Aguilar-Salinas Tarunveer S Ahluwalia Sonia S Anand Alain Bertoni Jette Bork-Jensen Ivan Brandslund Thomas A Buchanan Charles F Burant Adam S Butterworth Mickaël Canouil Juliana C N Chan Li-Ching Chang Miao-Li Chee Ji Chen Shyh-Huei Chen Yuan-Tsong Chen Zhengming Chen Lee-Ming Chuang Mary Cushman John Danesh Swapan K Das H Janaka de Silva George Dedoussis Latchezar Dimitrov Ayo P Doumatey Shufa Du Qing Duan Kai-Uwe Eckardt Leslie S Emery Daniel S Evans Michele K Evans Krista Fischer James S Floyd Ian Ford Oscar H Franco Timothy M Frayling Barry I Freedman Pauline Genter Hertzel C Gerstein Vilmantas Giedraitis Clicerio González-Villalpando Maria Elena González-Villalpando Penny Gordon-Larsen Myron Gross Lindsay A Guare Sophie Hackinger Liisa Hakaste Sohee Han Andrew T Hattersley Christian Herder Momoko Horikoshi Annie-Green Howard Willa Centre for Genetics and Genomics Versus Arthritis Centre for Musculoskeletal Research Division of Musculoskeletal and Dermatological Sciences University of Manchester Manchester UK. Department of Diabetes and Metabolic Diseases Graduate School of Medicine University of Tokyo Tokyo Japan. Department of Statistical Genetics Osaka University Graduate School of Medicine Suita Japan. Institute of Translational Genomics Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany. konstantinos.hatzikotoulas@helmholtz-munich.de. Institute of Translational Genomics Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany. Center for Precision Health Research National Human Genome Research Institute National Institutes of Health Bethesda MD USA. British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK. Heart and Lung Research Institute University of Cambridge Cambridge UK. Department of Epidemiology School of Public Health Nanjing Medical University Nanjing China. Department of Biostatistics and Center for Statistical Genetics University of Michigan Ann Arbor MI USA. Corporal Michael J. Crescenz VA Medical Center Philadelphia PA USA. Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Department of Genetics University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Programs in Metabolism and Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA USA. Diabetes Unit and Center for Genomic Medicine Massachusetts General Hospital Boston MA USA. TIMI Study Group Division of Cardiovascular Medicine Brigham and Women's Hospital Harvard Medical School Boston MA USA. William Harvey Research Institute Barts and the London School of Medicine and Dentistry Queen Mary University of London L

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes and molecular mechanisms that are often specific to cell type. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

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biomedical image analysis competitions: The state of current participation practice

arXiv

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arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

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A damaged-informed lung model for ventilator waveforms

arXiv

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arXiv 2020年

作者： Agrawal, Deepak K. Smith, Bradford J. Sottile, Peter D. Albers, David J. Department of Bioengineering University of Colorado Denver Anschutz Medical Campus AuroraCO80045 United States Department of Pediatrics University of Colorado Denver Anschutz Medical Campus AuroraCO80045 United States Division of Pulmonary Sciences and Critical Care Medicine Department of Medicine University of Colorado School of Medicine AuroraCO80045 United States Section of Informatics and Data Science Department of Pediatrics School of Medicine University of Colorado Denver Anschutz Medical Campus AuroraCO80045 United States Department of Bioengineering University of Colorado Denver/Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Engineering Columbia University New YorkNY10032 United States

The acute respiratory distress syndrome (ARDS) is characterized by the acute development of diffuse alveolar damage (DAD) resulting in increased vascular permeability and decreased alveolar gas exchange. Mechanical ventilation is a potentially lifesaving intervention to improve oxygen exchange but has the potential to cause ventilator-induced lung injury (VILI). A general strategy to reduce VILI is to use low tidal volume and low-pressure ventilation, but optimal ventilator settings for an individual patient are difficult for the bedside physician to determine and mortality from ARDS remains unacceptably high. Motivated by the need to minimize VILI, scientists have developed models of varying complexity to understand diseased pulmonary physiology. However, simple models often fail to capture real-world injury while complex models tend to not be estimable with clinical data, limiting the clinical utility of existing models. To address this gap, we present a physiologically anchored data-driven model to better model lung injury. Our approach relies on using clinically relevant features in the ventilator waveform data that contain information about pulmonary physiology, patients-ventilator interaction and ventilator settings. Our lung model can reproduce essential physiology and pathophysiology dynamics of differently damaged lungs for both controlled mouse model data and uncontrolled human ICU data. The estimated parameters values that are correlated with a known measure of lung physiology agree with the observed lung damage. In future endeavors, this model could be used to phenotype ventilator waveforms and serve as a basis for predicting the course of ARDS and improving patient care. © 2020, CC-BY-NC-SA.

关键词： Biological organs

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Author Correction: Whole-Genome Sequencing Analysis of Human Metabolome in Multi-Ethnic Populations

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Nature communications 2023年第1期14卷 6611页

作者： Elena V Feofanova Michael R Brown Taryn Alkis Astrid M Manuel Xihao Li Usman A Tahir Zilin Li Kevin M Mendez Rachel S Kelly Qibin Qi Han Chen Martin G Larson Rozenn N Lemaitre Alanna C Morrison Charles Grieser Kari E Wong Robert E Gerszten Zhongming Zhao Jessica Lasky-Su Bing Yu Human Genetics Center School of Public Health The University of Texas Health Science Center Houston TX USA. Center for Precision Health School of Biomedical Informatics The University of Texas Health Science Center at Houston Houston TX USA. Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA USA. Division of Cardiovascular Medicine Beth Israel Deaconess Medical Center Harvard Medical School Boston MA USA. Department of Biostatistics and Health Data Science Indiana University School of Medicine Indianapolis IN USA. Channing Division of Network Medicine Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA USA. Retina Service Massachusetts Eye and Ear Harvard Medical School 243 Charles Street Boston MA USA. Department of Epidemiology and Population Health Albert Einstein College of Medicine Bronx NY USA. Department of Biostatistics Boston University School of Public Health Boston MA USA. Cardiovascular Health Research Unit Departments of Medicine Epidemiology and Health Systems and Population Health University of Washington Seattle WA USA. Metabolon Inc. Morrisville NC USA. Division of Public Health Sciences Fred Hutchinson Cancer Research Center Seattle WA USA. Broad Institute of Harvard and MIT Cambridge MA USA. Human Genetics Center School of Public Health The University of Texas Health Science Center Houston TX USA. Bing.Yu@uth.tmc.edu.

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