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Publisher Correction: Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

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Eye (London, England) 2024年第11期38卷 2229-2231页

作者： Konrad Pesudovs Hugh R. Taylor Tabassom Sedighi Rupert R. A. Bourne Shahina Pardhan Van Charles Lansingh John H. Kempen Ian Tapply Arthur G. Fernandes Maria Vittoria Cicinelli Alessandro Arrigo Nicolas Leveziel Serge Resnikoff Seth Flaxman Mukkharram M. Bikbov Tasanee Braithwaite Alain Bron Ching-Yu Cheng Monte A. Del Monte Joshua R. Ehrlich Leon B. Ellwein David Friedman João M. Furtado Gus Gazzard Ronnie George M. Elizabeth Hartnett Jost B. Jonas Rim Kahloun Moncef Khairallah Rohit C. Khanna Janet Leasher Julie-Anne Little Vinay Nangia Michal Nowak Tunde Peto Pradeep Ramulu Fotis Topouzis Mitiadis Tsilimbaris Ya Xing Wang Ningli Wang Paul Svitil Briant Theo Vos Katrin Burkart Kaleb Coberly Xiaochen Dai Stephen S. Lim Tomislav Mestrovic Ali H. Mokdad Christopher J. L. Murray Jaimie D. Steinmetz Awoke Misganaw Yohannes Habtegiorgis Abate Mohammad Abdollahi Mozhan Abdollahi Ayele Mamo Abebe Olumide Abiodun Richard Gyan Aboagye Woldu Aberhe Abrha Hasan Abualruz Hiwa Abubaker Ali Eman Abu-Gharbieh Salahdein Aburuz Tadele Girum Girum Adal Mesafint Molla Adane Isaac Yeboah Addo Qorinah Estiningtyas Sakilah Adnani Muhammad Sohail Afzal Muhammad Umair Shahin Aghamiri Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Ali Ahmadi Siamak Sabour Ayman Ahmed Haroon Ahmed Ahmad Samir Alfaar Abid Ali Syed Shujait Shujait Ali Awais Altaf Hubert Amu Emmanuel Manu Sofia Androudi Rodrigo Anguita Abhishek Anil Rimple Jeet Kaur Muhammad Aaqib Shamim Saeid Anvari Anayochukwu Edward Anyasodor Francis Appiah Jalal Arabloo Haleh Ayatollahi Mosab Arafat Damelash Areda Reza Arefnezhad Brhane Berhe Aregawi Akeza Awealom Asgedom Tahira Ashraf Seyyed Shamsadin Athari Bantalem Tilaye Tilaye Atinafu Birhan Tsegaw Taye Maha Moh’d Wahbi Atout Alok Atreya Ahmed Y. Azzam Hassan Babamohamadi Sara Bagherieh Yogesh Bahurupi Atif Amin Baig Shumaila Nargus Biswajit Banik Mainak Bardhan Saurav Basu Kavita Batra Nebiyou Simegnew Bayileyegn Fatemeh Bazvand Addisu Shunu Beyene Awoke Masrie Asrat Derese Devidas S. Bhagat Akshaya Srikanth Bhagavathula Pankaj Bh Medicine & Health University of New South Wales Sydney NSW Australia School of Optometry and Vision Science University of New South Wales Sydney NSW Australia School of Population and Global Health University of Melbourne Carlton VIC Australia Vision and Eye Research Institute Anglia Ruskin University Cambridge UK HelpMeSee Instituto Mexicano de Oftalmologia Santiago de Querétaro Mexico University of Miami Gables FL USA University of Utah Salt Lake City UT USA Chief Medical Office HelpMeSee New York NY USA Mexican Institute of Ophthalmology Queretaro Mexico Department of Ophthalmology Massachusetts Eye and Ear/Shepens Eye Research Institute/Harvard Medical School Boston MA USA Eye Unit MyungSung Christian Medical Center (MCM) Comprehensive Specialized Hospital and MyungSun Medical College Addis Ababa Ethiopia Department of Ophthalmology Addis Ababa University Addis Ababa Ethiopia Sight for Souls Bellevue WA USA Department of Ophthalmology Harvard University Boston MA USA Eye Unit MyungSung Medical College Addis Ababa Ethiopia Addenbrooke’s Hospital Cambridge UK Department of Ophthalmology Cambridge University Hospitals Cambridge UK Federal University of Sao Paolo Sao Paolo SP Brazil University of Calgary Calgary AB Canada Department of Ophthalmology and Visual Sciences Federal University of São Paulo Sao Paulo Brazil School of Medicine Vita-Salute San Raffaele University Milan Italy Department of Ophthalmology IRCCS San Raffaele Scientific Institute Milan Italy Department of Ophthalmology San Raffaele Scientific Institute Milano Italy IRCCS San Raffaele Scientific Institute Vita-Salute University Milan Italy Scientific Institute San Raffaele Hospital Vita-Salute University Milan Italy University of Poitiers Poitiers France CHU de Poitiers Poitiers France Ophthalmology Department CHU de Poitiers (Poitiers University Hospital) Poitiers France Unité 1084 National Institute of Health and Medical Research (INSERM) Poitiers France

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Nitrogen assimilation in cassava: Implications for carbon metabolism and biomass synthesis 1

Nitrogen assimilation in cassava: Implications for carbon me...

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1st International Conference on Education, Science and Technology, ICON-EST 2018

作者： Siriwat, W. Muhardina, V. Thammarongtham, C. Kalapanulak, S. Saithong, T. Systems Biology and Bioinformatics Research Laboratory Pilot Plant Development and Training Institute King Mongkut's University of Technology Thonburi Bang Khun Thian Bangkok10150 Thailand Department of Agricultural Industrial Technology Faculty of Agricultural Technology University of SerambiMekkah Jl. TeungkuImumLueng Bata Batoh Banda Aceh Aceh23245 Indonesia Biochemical Engineering and Pilot Plant Research and Development Unit National Center for Genetic Engineering and Biotechnology King Mongkut's University of Technology Thonburi Bang KhunThian Bangkok10150 Thailand Bioinformatics and Systems Biology Program School of Bioresources and Technology King Mongkut's University of Technology Thonburi Bang KhunThian Bangkok10150 Thailand

The nitrogen assimilation pathway in cassava was reconstructed by comparative genomics approach to understand the underlying metabolism as well as the interaction between carbon and nitrogen assimilation towards the synthesis of metabolic phenotype. First, theproteins of cassava were annotated via sequence similarity search against genes of 11 template plants obtained from KEGG and PMN databases, employing reciprocal BLASTp(E-value ≤ 1x10-10, identity percentage ≥ 60, and coverage percentage ≥ 80). The template plants comprised well-known plant, starchy crops, nitrogen-fixing crops and crops that are evolutionarily related to cassava and includedArabidopsis thaliana, Oryzasativa, Zea mays, Ricinuscommunis, Solanumtuberosum, Brassica rapa, Cicerarietinum, Jatrophacurcas, Medicagotruncatula, Phaseolus vulgaris and Glycine *** pathway was then curatedwith reactions obtained from the CassavaCyc database to ensure full pathway *** was subsequently validated with cloned sequence of cassava from the GenBank and cassava transcriptome data from literature. The resulting N-assimilation pathway, covering the conversion of nitrate to amino acids (glutamine and glutamate),consists of 14 biochemical reactions corresponding to 59 genes, 73 proteins and 2 transport reactions. At least 92 percent of the identified proteins in the pathway were supported by the transcriptome data. In addition, the proposed N-assimilation pathway contains four additional enzymes, including glutamate synthase, nitrilase, formamidase and carbamoyl phosphate synthasecompared to the existing N-assimilation pathway in CassavaCyc database. Taken together, the N-assimilation pathway herein proposed identified reactions involved in N-assimilation and represents a forward step towards understanding metabolic basis for cassava yield as well as its phenotypic plasticity and adaptation to stress. © Published under licence by IOP Publishing Ltd.

关键词： Genes

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Efficient Simulation of Metasurfaces Comprising of Truncated Periodic or Quasi-periodic structures

Efficient Simulation of Metasurfaces Comprising of Truncated...

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European Conference on Antennas and Propagation

作者： Raj Mittra Nathawut Homsup Abdelkhalek Nasri Yang Su Electrical and Computer Engineering Department University of Central Florida Orlando FL USA Research Unit of mechatronic systems and signals National Engineering School of Carthage Tunis Tunisia Electrical and Computer Engineering Department University of Waterloo Waterloo Canada

This paper describes several techniques for enhancing the computational efficiency of Metasurface (MS) modeling when the geometry of the MS is either a truncated periodic structure or it is quasi-periodic, with its elements varying slowly along the surface. The quasi-periodic MS is used to introduce a phase (and amplitude) taper in the transmitted, reflected, or propagating wave interacting with the MS. The three key approaches proposed in this paper are based on using: (a) macro-basis functions for matrix-size reduction;(b) effective medium to replace the MS element;GPU to speed up the generation of multilayer Green's function and associated MoM matrix computation.

关键词： Graphics processing units Frequency selective surfaces Method of moments Geometry Computational modeling Green's function methods

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Correction: Machine learning predicts pulmonary long Covid sequelae using clinical data

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BMC medical informatics and decision making 2025年第1期25卷 68页

作者： Ermanno Cordelli Paolo Soda Sara Citter Elia Schiavon Christian Salvatore Deborah Fazzini Greta Clementi Michaela Cellina Andrea Cozzi Chandra Bortolotto Lorenzo Preda Luisa Francini Matteo Tortora Isabella Castiglioni Sergio Papa Diego Sona Marco Alì Unit of Computer Systems and Bioinformatics Department of Engineering University Campus Bio-Medico of Rome Via Alvaro del Portillo 21 Rome 00128 Italy. Unit of Computer Systems and Bioinformatics Department of Engineering University Campus Bio-Medico of Rome Via Alvaro del Portillo 21 Rome 00128 Italy. paolo.soda@umu.se. Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Universitetstorget 4 901 87 Umeå Sweden. paolo.soda@umu.se. Fondazione Bruno Kessler Via Sommarive 18 Trento 38123 Italy. Department of Physics University of Trento Via Sommarive 14 Trento 38123 Italy. DeepTrace Technologies S.R.L. Via Conservatorio 17 Milan 20122 MI Italy. Department of Science Technology and Society University School for Advanced Studies IUSS Pavia 27100 Pavia Italy. Department of Diagnostic Imaging and Stereotactic Radiosurgey Centro Diagnostico Italiano S.p.A. Via S. Saint Bon 20 Milan 20147 Italy. Radiology Department ASST Fatebenefratelli Sacco Piazza Principessa Clotilde 3 Milan 20121 Italy. Imaging Institute of Southern Switzerland (IIMSI) Ente Ospedaliero Cantonale (EOC) Lugano Switzerland. Radiology Unit Department of Clinical Surgical Diagnostic and Pediatric Sciences University of Pavia Corso Str. Nuova 65 Pavia 27100 Italy. Radiology Institute Fondazione IRCCS Policlinico San Matteo Viale Golgi 19 Pavia 27100 Italy. Department of Naval Electrical Electronics and Telecommunications Engineering University of Genova Via all'Opera Pia 11a Genoa 16145 Italy. Department of Physics G. Occhialini University of Milan-Bicocca 20133 Milan Italy. Bracco Imaging S.p.A. Via Caduti di Marcinelle 13 Milan 20134 Italy.

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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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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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Patient comprehension of oncologic surgical procedures using 3D printed surgical planning prototypes

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Annals of 3D Printed Medicine 2022年 7卷 100068-100068页

作者： Tejo-Otero, A. Valls-Esteve, A. Fenollosa-Artés, F. Siles-Hinojosa, A. Nafria, B. Ayats, M. Buj-Corral, I. Otero, MC. Rubio-Palau, J. Munuera, J. Krauel, L. Centre CIM Universitat Politècnica de Catalunya (CIM UPC) Carrer de Llorens i Artigas 12 Barcelona 08028 Spain Innovation Department. Hospital Sant Joan de Déu Universitat de Barcelona Santa Rosa 39-57 Esplugues de Llobregat 08950 Spain 3D Unit (3D4H) Hospital Sant Joan de Déu Universitat de Barcelona Spain Department of Pediatric Surgery Hospital Sant Joan de Déu Universitat de Barcelona Spain Maxillofacial Unit Department of Pediatric Surgery Hospital Sant Joan de Déu Universitat de Barcelona Spain Department of Diagnostic Imaging Hospital Sant Joan de Déu Universitat de Barcelona Spain KIDS Barcelona Sant Joan de Déu Research Foundation Barcelona Spain Universitat Politècnica de Catalunya Department of Mechanical Engineering. School of Engineering of Barcelona (ETSEIB) Av. Diagonal 647 Barcelona 08028 Spain University of the Basque Country (UPV/EHU) Department of Computer Languages and Systems. School of Engineering of Vitoria-Gasteiz Nieves Cano 12 Vitoria-Gasteiz 01006 Spain

Purpose: Patient understanding of complex surgical procedures and post-intervention consequences is often poor. Little is known about the effectiveness of 3D printed models to improve the comprehension of the medical information provided to patients. The purpose of this study was to determine if 3D printed patient-specific anatomical models could help improve patients’ satisfaction and understanding of complex oncological surgical procedures, their risks, benefits, and alternatives. Basic procedure: A randomized, controlled crossover experiment was performed, where subjects were randomly assigned to different treatments of the study. This experiment involved teenage patients experts from Kids Barcelona, a Young Person's Advisory Group. The team (n = 14, age range 14–20, 9 females and 5 males) was divided into two groups involved in two simulated pre-surgical outpatient visits for complex oncologic surgical procedures: a high-risk stage 4 abdominal neuroblastoma, and a biliary tract rhabdomyosarcoma. Two senior oncologic surgeons participated in the study by performing the structured outpatient pre-surgical visit. Each participant received information before the study explaining the study methodology and was given a questionnaire. Main findings: Data analysis of the group using the 3D printed model for the neuroblastoma case showed better results than without the 3D model. On the other hand, conversely, on the data analysis of the rhabdomyosarcoma case with the 3D printed model no better results were observed as compared to the case of not using a 3D model. However, the results of the participants’ knowledge were still better than before the intervention. Satisfaction was significantly better with a 3D model in both cases. Conclusion: The use of 3D physical models improves the patient's knowledge and shows the effectiveness of 3D printed models to enhance the comprehension of the medical information provided to patients and improve satisfaction. © 2022 The Authors

关键词： 3d printing Additive manufacturing health Patient experience Phantoms Preoperative planning Surgical education Surgical planning prototypes

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The status of the human gene catalogue

arXiv

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arXiv 2023年

作者： Amaral, Paulo Carbonell-Sala, Silvia De La Vega, Francisco M. Faial, Tiago Frankish, Adam Gingeras, Thomas Guigo, Roderic Harrow, Jennifer L. Hatzigeorgiou, Artemis G. Johnson, Rory Murphy, Terence D. Pertea, Mihaela Pruitt, Kim D. Pujar, Shashikant Takahashi, Hazuki Ulitsky, Igor Varabyou, Ales Wells, Christine A. Yandell, Mark Carninci, Piero Salzberg, Steven L. INSPER Institute of Education and Research SP São Paulo Brazil Dr. Aiguader 88 Catalonia Barcelona08003 Spain Department of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Tempus Labs Inc. ChicagoIL United States Nature Genetics San FranciscoCA United States European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Department of Functional Genomics Cold Spring Harbor Laboratory Cold Spring HarborNY United States Catalonia Barcelona Spain Centre for Genomics Research Discovery Sciences AstraZeneca Da Vinci Building. Melbourn Science Park RoystonSG8 6HB United Kingdom Universithy of Thessaly Department of Computer Science and Biomedical Informatics Lamia Greece Hellenic Pasteur Institute Athens Greece School of Biology and Environmental Science University College Dublin DublinD04 V1W8 Ireland Conway Institute of Biomedical and Biomolecular Research University College Dublin DublinD04 V1W8 Ireland Department of Medical Oncology Inselspital Bern University Hospital University of Bern Bern3010 Switzerland Department for BioMedical Research University of Bern Bern3008 Switzerland National Center for Biotechnology Information National Library of Medicine National Institutes of Health BethesdaMD20894 United States Center for Computational Biology Johns Hopkins University BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins University BaltimoreMD United States Laboratory for Transcriptome Technology RIKEN Center for Integrative Medical Sciences Kanagawa Yokohama230-0045 Japan Department of Immunology and Regenerative Biology Department of Molecular Neuroscience Weizmann Institute of Science Rehovot76100 Israel Department of Computer Science Johns Hopkins University BaltimoreMD United States Stem Cell Systems Department of Anatomy and Physiology Faculty of Medicine Dentistry and Health Scien

Scientists have been trying to identify all of the genes in the human genome since the initial draft of the genome was published in 2001. Over the intervening years, much progress has been made in identifying protein-coding genes, and the estimated number has shrunk to fewer than 20,000, although the number of distinct protein-coding isoforms has expanded dramatically. The invention of high-throughput RNA sequencing and other technological breakthroughs have led to an explosion in the number of reported non-coding RNA genes, although most of them do not yet have any known function. A combination of recent advances offers a path forward to identifying these functions and towards eventually completing the human gene catalogue. However, much work remains to be done before we have a universal annotation standard that includes all medically significant genes, maintains their relationships with different reference genomes, and describes clinically relevant genetic variants. © 2023, CC BY.

关键词： Genes

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Understanding metric-related pitfalls in image analysis validation

arXiv

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： computer vision

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Assessing the Potential of GPT-4 to Annotate Medical Notes Across Different Languages and Countries: A Model Evaluation Study

SSRN

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SSRN 2024年

作者： Saad Menezes, Maria Clara Hoffman, Alexander F. Tan, Amelia L.M. Nalbandyan, Mariné Omenn, Gilbert S. Mazzotti, Diego R. Hernández-Arango, Alejandro Visweswaran, Shyam Venkatesh, Shruthi Mandl, Kenneth D. Bourgeois, Florence T. Lee, James W.K. Makmur, Andrew Hanauer, David A. Semanik, Michael G. Kerivan, Lauren T. Hill, Terra Forero, Julian Restrepo Castro, Carlos Andrés C.R. Vigna, Matteo Ceriana, Piero Abu-El-rub, Noor Avillach, Paul Bellazzi, Riccardo Callaci, Thomas Gutiérrez-Sacristán, Alba Malovini, Alberto Mathew, Jomol P. Morris, Michele Murthy, Venkatesh Parimbelli, Enea Patel, Lav P. Sáez, Carlos Samayamuthu, Malarkodi Jebathilagam Thompson, Jeffrey A. Tibollo, Valentina Xia, Zongqi Kohane, Isaac S. Harvard Medical School Department of Biomedical Informatics 10 Shattuck Street BostonMA02115 United States School of Medicine and Public Health University of Wisconsin-Madison Office of Informatics and Information Technology 750 Highland Ave MadisonWI53705 United States University of Michigan Department of Computational Medicine & Bioinformatics Internal Medicine Human Genetics Environmental Health 100 Washtenaw Avenue Ann ArborMI48109 United States University of Kansas Medical Center Division of Medical Informatics Division of Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine 3901 Rainbow Blvd Kansas CityKS66160 United States University of Antioquia Hospital Alma Máter de Antioquia Department of Internal Medicine Calle 64 N°51 D - 154 Medellín Colombia University of Pittsburgh Department of Biomedical Informatics 5607 Baum Blvd PittsburghPA15206 United States University of Pittsburgh Department of Neurology 3501 5th Avenue PittsburghPA15206 United States Boston Children's Hospital Computational Health Informatics Program 300 Longwood Avenue BostonMA02115 United States Boston Children's Hospital Department of Pediatrics 300 Longwood Avenue BostonMA02115 United States National University Health System Department of Surgery 1E Kent Ridge Rd Singapore119228 Singapore National University Health System Department of Diagnostic Imaging 1E Kent Ridge Rd Singapore119228 Singapore University of Michigan Department of Learning Health Sciences NCRC 100 2800 Plymouth Road Ann ArborMI48109 United States University of Kansas Medical Center Department of Surgery 3901 Rainbow Blvd Kansas CityKS66160 United States Istituti Clinici Scientifici Maugeri IRCCS Respiratory rehabilitation unit Via Maugeri 10 Pavia27100 Italy University of Kansas Medical Center 3901 Rainbow Blvd Kansas CityKS66160 United States University of Pavia Department of Electrical Computer and Biomedical Engineering via Ferrata 5 Pavia2710

Background: Physicians invest hours creating patient notes, which are rich in information but difficult for computers to analyze due to their unstructured format. GPT-4 reshaped our ability to process text, yet it is unknown how well this model can handle medical notes. This project aims to compare GPT-4’s ability to annotate medical notes against experienced physicians across three different languages at multiple institutions and countries. Methods: This study included eight sites from four countries - the united States, Colombia, Singapore, and Italy. Each site contributed seven de-identified notes (admission, progress, or consult) from hospitalized patients. GPT-4 assessed each note by answering 14 questions, including demographic information, clinical judgments, data quality, and patients’ eligibility for a hypothetical study enrollment. For validation, two physicians from each site independently evaluated GPT-4's responses. Findings: Overall, 56 medical notes, written in English, Italian, and Spanish, were analyzed. A total of 784 responses from GPT-4 were generated. Both physicians agreed with GPT-4’s response 79% of the time (622/784, 95%CI 76-82%). Only one of the two physicians agreed with GPT-4’s response 10% of the time (82/784, 95%CI 8-13%). Neither physician agreed with GPT-4’s response 10% of the time (80/784, 95%CI 8-13%). Both physicians agreed with GPT-4 more often in notes written in Spanish and Italian than in English, with agreement rates of 88% (86/98, 95%CI 79-93%), 84% (82/98, 95%CI 75-90%), and 77% (454/588, 95%CI 74-80%), respectively. Hallucinations were rare (10/784, 95%CI 0-2%). GPT-4 correctly selected patients for a hypothetical study enrollment based on three criteria 90% of the time (95%CI 81-98%). Interpretation: The findings indicate that GPT-4 annotations demonstrated a high agreement rate with physicians across all languages. We also demonstrate GPT-4's potential to assist in patient selection for studies. Funding: None. Declarati

关键词： Medical computing

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