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Detection of emergency department patients at risk of dementia through artificial intelligence

Alzheimer's & dementia : the journal of the Alzheimer's Association 2025年第6期21卷 e70334页

作者： Inessa Cohen Richard Andrew Taylor Haipeng Xue Isaac V Faustino Natalia Festa Cynthia Brandt Emily Gao Ling Han Siddarth Khasnavis James M Lai Adam P Mecca Atharva Vinay Sapre Juan Young Michael Zanchelli Ula Hwang Department of Emergency Medicine Yale School of Medicine New Haven Connecticut USA. Department of Biomedical Informatics and Data Science Yale University School of Medicine New Haven Connecticut USA. Program of Computational Biology and Bioinformatics Yale University School of Medicine New Haven Connecticut USA. Department of Internal Medicine Yale School of Medicine New Haven Connecticut USA. Department of Psychiatry Yale School of Medicine New Haven Connecticut USA. Division of Geriatric Medicine and Palliative Care Department of Internal Medicine New York University Grossman School of Medicine New York New York USA. Geriatric Research Education and Clinical Center James J. Peters VAMC Bronx New York USA. Departments of Emergency Medicine and Population Health New York University Grossman School of Medicine New York New York USA.

INTRODUCTION:The study aimed to develop and validate the Emergency Department Dementia Algorithm (EDDA) to detect dementia among older adults (65+) and support clinical decision-making in the emergency department (ED). METHODS:In a multisite retrospective study of 759,665 ED visits, electronic health record data from Yale New Haven Health (2014-2022) were used to train three supervised and semi-unsupervised positive-unlabeled machine learning models (XGBoost, Random Forest, LASSO). A separate test set of 400 ED encounters underwent adjudicated chart review for validation. RESULTS:EDDA achieved an area under the receiver-operating characteristic curve (AUROC) of 0.85 in the test set and 0.93 in the validation set. Positive-unlabeled learning improved performance. Agreement between EDDA and clinician-adjudicated dementia diagnoses was moderate (kappa = 0.50), with 17% of EDDA-positive patients having undiagnosed probable dementia. DISCUSSION:EDDA enhances dementia detection in the ED, with potential for real-time implementation to improve patient outcomes and care transitions. HIGHLIGHTS:Developed a machine learning algorithm using electronic health record data to detect dementia in the emergency department (ED). Algorithm designed to balance detection accuracy with ease of ED implementation. Parsimonious model with limited but predictive variables selected for rapid ED use. Focused on real-time application, optimizing ED workflows, and clinician support. Aims to enhance ED dementia detection, patient safety, and care coordination.

关键词： care transition intervention cognitive impairment dementia early dementia detection emergency department

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66O Association of a blood T cell fitness gene signature with clinical benefit from ImmTAC bispecific T cell engagers

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Annals of Oncology 2024年 35卷 S239-S240页

作者： J.J. Sacco R.D. Carvajal O. Hamid P.B. Kirk L. Collins M.O. Butler A.N. Shoushtari J.C. Hassel A.P. Ikeguchi L. Hernandez-Aya P. Nathan J.M. Piulats Rodriguez H. Shaw D. Davar C. Han M.R. Middleton F. Thistlethwaite F. Peck K. Ranade T. Sato Medical Oncology Department The Clatterbridge Cancer Centre and University of Liverpool Wirral UK Medical Oncology Northwell Health Cancer Institute New York NY USA Melanoma Center and Phase I Immuno-Oncology Program The Angeles Clinic and Research Institute a Cedars-Sinai Affiliate Los Angeles CA USA Translational Medicine Immunocore Limited Abingdon-on-Thames UK Bioinformatics and Data Science Immunocore Limited Abingdon-on-Thames UK Department of Medical Oncology and Hematology Princess Margaret Cancer Center Toronto ON Canada Medical Oncology Memorial Sloan Kettering Cancer Center New York NY USA Department of Dermatology and National Center for Tumor Diseases University Hospital Heidelberg Heidelberg Germany Department of Medicine Stephenson Cancer Center/University of Oklahoma Oklahoma City OK USA Cutaneous Oncology University of Miami Sylvester Comprehensive Cancer Center Miami Beach FL USA Medical Oncology Department Mount Vernon Cancer Centre Northwood UK Medical Oncology Department ICO - Institut Català d'Oncologia l'Hospitalet (Hospital Duran i Reynals) Barcelona Spain Medical Oncology UCLH - University College London Hospitals NHS Foundation Trust London UK Medical Oncology University of Pittsburgh Cancer Institute Pittsburgh PA USA Faculty of Medical & Health Sciences University of Auckland Auckland New Zealand Department of Oncology University of Oxford Oxford UK Medical Oncology Department The Christie NHS Foundation Trust Manchester UK Clinical Development Immunocore Limited Abingdon-on-Thames UK Translational Medicine Department Immunocore LLC Rockville MD USA Sidney Kimmel Cancer Center Jefferson University Philadelphia PA USA

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Deep Learning for Imbalance data Classification using Class Expert Generative Adversarial Network

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Procedia Computer Science 2018年 135卷 60-67页

作者： Fanny Tjeng Wawan Cenggoro Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

Without any specific way for imbalance data classification, artificial intelligence algorithm cannot recognize data from minority classes easily. In general, modifying the existing algorithm by assuming that the training data is imbalanced, is the only way to handle imbalance data. However, for a normal data handling, this way mostly produces a deficient result. In this research, we propose a class expert generative adversarial network (CE-GAN) as the solution for imbalance data classification. CE-GAN is a modification in deep learning algorithm architecture that does not have an assumption that the training data is imbalance data. Moreover, CE-GAN is designed to identify more detail about the character of each class before classification step. CE-GAN has been proved in this research to give a good performance for imbalance data classification.

关键词： imbalance data classification deep learning generative adversarial network

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Publisher Correction: Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing

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Nature genetics 2023年第5期55卷 893页

作者： Isidro Cortés-Ciriano Jake June-Koo Lee Ruibin Xi Dhawal Jain Youngsook L Jung Lixing Yang Dmitry Gordenin Leszek J Klimczak Cheng-Zhong Zhang David S Pellman Peter J Park Department of Biomedical Informatics Harvard Medical School Boston MA USA. Ludwig Center at Harvard Boston MA USA. Centre for Molecular Science Informatics Department of Chemistry University of Cambridge Cambridge UK. European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK. School of Mathematical Sciences and Center for Statistical Science Peking University Beijing China. Ben May Department for Cancer Research University of Chicago Chicago IL USA. Department of Human Genetics The University of Chicago Chicago IL USA. Genome Integrity and Structural Biology Laboratory National Institute of Environmental Health Sciences US National Institutes of Health Durham NC USA. Integrative Bioinformatics Group National Institute of Environmental Health Sciences US National Institutes of Health Durham NC USA. Department of Data Sciences Dana-Farber Cancer Institute Boston MA USA. Department of Pediatric Oncology Dana-Farber Cancer Institute Boston MA USA. Department of Cell Biology Harvard Medical School Blavatnik Institute Boston MA USA. Howard Hughes Medical Institute Boston MA USA. Department of Biomedical Informatics Harvard Medical School Boston MA USA. peter_park@hms.harvard.edu. Ludwig Center at Harvard Boston MA USA. peter_park@hms.harvard.edu.

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The putative error prone polymerase REV1 mediates DNA damage and drug resistance in Candida albicans

npj antimicrobials and resistance

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npj antimicrobials and resistance 2024年第1期2卷 42页

作者： Michelle R Agyare-Tabbi Deeva Uthayakumar Desiree Francis Laetitia Maroc Chris Grant Peter McQueen Garret Westmacott Hajer Shaker Iwona Skulska Isabelle Gagnon-Arsenault Jonathan Boisvert Christian R Landry Rebecca S Shapiro Department of Molecular and Cellular Biology University of Guelph Guelph ON N1H 5N4 Canada. Department of Immunology University of Toronto Toronto ON M5R 0A3 Canada. National Microbiology Laboratory Public Health Agency of Canada Winnipeg MB R3E 3L5 Canada. Institut de Biologie Intégrative et des Systèmes (IBIS) Université Laval Québec QC Canada. Department of Biochemistry Microbiology and Bioinformatics Université Laval Québec QC Canada. Quebec Network for Research on Protein Function Engineering and Applications (PROTEO) Université du Québec à Montréal Montréal QC Canada. Université Laval Big Data Research Center (BDRC_UL) Québec QC Canada.

Antimicrobial-induced DNA damage, and subsequent repair via upregulation of DNA repair factors, including error-prone translesion polymerases, can lead to the increased accumulation of mutations in the microbial genome, and ultimately increased risk of acquired mutations associated with antimicrobial resistance. While this phenotype is well described in bacterial species, it is less thoroughly investigated amongst microbial fungi. Here, we monitor DNA damage induced by antifungal agents in the fungal pathogen , and find that commonly used antifungal drugs are able to induce DNA damage, leading to the upregulation of transcripts encoding predicted error-prone polymerases and related factors. We focus on , encoding a putative error-prone polymerase, and find that while deleting this gene in leads to increased sensitivity to DNA damage, it also unexpectedly renders cells more likely to incur mutations and evolve resistance to antifungal agents. We further find that deletion of leads to a significant depletion in the uncharacterized protein Shm1, which itself plays a role in fungal mutagenesis. Together, this work lends new insight into previously uncharacterized factors with important roles in the DNA damage response, mutagenesis, and the evolution of antifungal drug resistance.

关键词： Antifungal agents Fungal genetics

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Author Correction: Systematic dissection of tumor-normal single-cell ecosystems across a thousand tumors of 30 cancer types

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Nature communications 2025年第1期16卷 2806页

作者： Junho Kang Jun Hyeong Lee Hongui Cha Jinhyeon An Joonha Kwon Seongwoo Lee Seongryong Kim Mert Yakup Baykan So Yeon Kim Dohyeon An Ah-Young Kwon Hee Jung An Se-Hoon Lee Jung Kyoon Choi Jong-Eun Park Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea. Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea. Division of Hematology-Oncology Department of Medicine Samsung Medical Center Sungkyunkwan University School of Medicine Seoul Republic of Korea. Division of Cancer Data Science National Cancer Center Bioinformatics Branch Goyang Republic of Korea. Department of Pathology CHA Bundang Medical Center CHA University Seongnam-si Republic of Korea. Division of Hematology-Oncology Department of Medicine Samsung Medical Center Sungkyunkwan University School of Medicine Seoul Republic of Korea. shlee119@skku.edu. Department of Health Sciences and Technology Samsung Advanced Institute of Health Science and Technology Sungkyunkwan University School of Medicine Seoul Republic of Korea. shlee119@skku.edu. Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea. jungkyoon@kaist.ac.kr. Penta Medix Co. Ltd. Seongnam-si Gyeonggi-do Republic of Korea. jungkyoon@kaist.ac.kr. Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea. jp24@kaist.ac.kr. Biomedical Research Center Korea Advanced Institute of Science and Technology Daejeon Republic of Korea. jp24@kaist.ac.kr.

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Hierarchical and modularly-minimal vertex colorings

arXiv

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arXiv 2020年

作者： Valdivia, Dulce I. Geiß, Manuela Rosales, Maribel Hernández Stadler, Peter F. Hellmuth, Marc Km. 9.6 Libramiento Norte Carretera Irapuato-León Irapuato GTOMX-36821 Mexico Bioinformatics Group Department of Computer Science & Interdisciplinary Center for Bioinformatics Universität Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Software Competence Center Hagenberg GmbH Softwarepark 21 HagenbergA-4232 Austria CONACYT-Instituto de Matemáticas UNAM Juriquilla Blvd. Juriquilla 3001 Juriquilla Querétaro QRO76230 Mexico Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine at Leipzig University Universität Leipzig Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Institute for Theoretical Chemistry University of Vienna Währingerstrasse 17 WienA-1090 Austria Facultad de Ciencias Universidad National de Colombia Sede Bogotá Colombia Santa Fe Insitute 1399 Hyde Park Rd. Santa FeNM87501 United States School of Computing University of Leeds EC Stoner Building LeedsLS2 9JT United Kingdom

Cographs are exactly the hereditarily well-colored graphs, i.e., the graphs for which a greedy vertex coloring of every induced subgraph uses only the minimally necessary number of colors χ(G). We show that greedy colorings are a special case of the more general hierarchical vertex colorings, which recently were introduced in phylogenetic combinatorics. Replacing cotrees by modular decomposition trees generalizes the concept of hierarchical colorings to arbitrary graphs. We show that every graph has a modularly-minimal coloring σ satisfying |σ(M)| = χ(M) for every strong module M of G. This, in particular, shows that modularly-minimal colorings provide a useful device to design efficient coloring algorithms for certain hereditary graph classes. For cographs, the hierarchical colorings coincide with the modularly-minimal coloring. As a by-product, we obtain a simple linear-time algorithm to compute a modularly-minimal coloring of P4-sparse graphs. Copyright © 2020, The Authors. All rights reserved.

关键词： Coloring

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Prior COVID-19 vaccination and reduced risk of cerebrovascular diseases among COVID-19 survivors

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Journal of Medical Virology 2024年第5期96卷 e29648-e29648页

作者： Chen, Sheng-Yin Hsieh, Tina Yi Jin Hung, Yao-Min Oh, Jae Won Chen, Shen-Kai Wang, Shiow-Ing Chang, Renin Wei, James Cheng-Chung Clinical and Translational Epidemiology Unit Massachusetts General Hospital and Harvard Medical School Boston Morocco Division of Gastroenterology Massachusetts General Hospital and Harvard Medical School Boston Morocco Department of Epidemiology Harvard T. H. Chan School of Public Health Boston Morocco Department of Obstetrics & Gynecology Beth Israel Deaconess Medical Center Boston Morocco Department of Bioinformatics Harvard Medical School Boston Morocco Division of Nephrology Department of Internal Medicine Taipei Veterans General Hospital Taitung Branch Taiwan Master Program in Biomedicine College of Science and Engineering National Taitung University Taitung Taiwan College of Health and Nursing Meiho University Pingtung Taiwan Department of Neurology Brigham and Women's Hospital Boston Morocco Department of Neurology Harvard Medical School Boston MA United States Department of Education Kaohsiung Chang Gung Memorial Hospital Boston MA United States Center for Health Data Science Department of Medical Research Chung Shan Medical University Hospital Taichung Taiwan Institute of Medicine College of Medicine Chung Shan Medical University Taichung Taiwan Department of Nursing Jen-Teh Junior College of Medicine Nursing and Management Miaoli County Taiwan Department of Medical Education and Research Kaohsiung Veterans General Hospital Kaohsiung Taiwan Department of Emergency Medicine Kaohsiung Veterans General Hospital Kaohsiung Taiwan School of Medicine Chung Shan Medical University Taichung Taiwan Department of Recreation and Sports Management Tajen University Pintung Taiwan Department of Allergy Immunology & Rheumatology Chung Shan Medical University Hospital Taichung Taiwan Department of Nursing Chung Shan Medical University Taichung Taiwan Graduate Institute of Integrated Medicine China Medical University Taichung Taiwan Office of Research and Development Asia University Taichung Taiwan

The effects of COVID-19 vaccination on short-term and long-term cerebrovascular risks among COVID-19 survivors remained unknown. We conducted a national multi-center retrospective cohort study with 151 597 vaccinated and 151 597 unvaccinated COVID-19 patients using the TriNetX database, from January 1, 2020 to December 31, 2023. Patients baseline characteristics were balanced with propensity score matching (PSM). The outcomes were incident cerebrovascular diseases occurred between 1st and 30th days (short-term) after COVID-19 diagnosis. Nine subgroup analyses were conducted to explore potential effect modifications. We performed six sensitivity analyses, including evaluation of outcomes between 1st to 180th days, accounting for competing risk, and incorporating different variant timeline to test the robustness of our results. Kaplan-Meier curves and Log-Rank tests were performed to evaluate survival difference. Cox proportional hazards regressions were adopted to estimate the PSM-adjusted hazard ratios (HR). The overall short-term cerebrovascular risks were lower in the vaccinated group compared to the unvaccinated group (HR: 0.66, 95% CI: 0.56–0.77), specifically cerebral infarction (HR: 0.62, 95% CI: 0.48–0.79), occlusion and stenosis of precerebral arteries (HR: 0.74, 95% CI: 0.53–0.98), other cerebrovascular diseases (HR: 0.57, 95% CI: 0.42–0.77), and sequelae of cerebrovascular disease (HR: 0.39, 95% CI:0.23–0.68). Similarly, the overall cerebrovascular risks were lower in those vaccinated among most subgroups. The long-term outcomes, though slightly attenuated, were consistent (HR: 0.80, 95% CI: 0.73–0.87). Full 2-dose vaccination was associated with a further reduced risk of cerebrovascular diseases (HR: 0.63, 95% CI: 0.50–0.80) compared to unvaccinated patients. Unvaccinated COVID-19 survivors have significantly higher cerebrovascular risks than their vaccinated counterparts. Thus, clinicians are recommended to monitor this population closely for stroke even

关键词： cohort study COVID-19 vaccination strokes

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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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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