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COWID: an efficient cloud-based genomics workflow for scalable identification of SARS-COV-2

Briefings in bioinformatics 2023年第5期24卷 bbad280-bbad280页

作者： Lim, Hendrick Gao-Min Fann, Yang C Lee, Yuan-Chii Gladys Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei 11031 Taiwan Department of Medical Research Tzu Chi Hospital Indonesia Pantai Indah Kapuk Greater Jakarta 14470 Indonesia It and Bioinformatics Program Division of Intramural National Institute of Neurological Disorders and Stroke National Institutes of Health Bethesda 20892 MD United States

Implementing a specific cloud resource to analyze extensive genomic data on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a challenge when resources are limited. To overcome this, we repurposed a cloud platform initially designed for use in research on cancer genomics (https://***) to enable its use in research on SARS-CoV-2 to build Cloud Workflow for Viral and Variant Identification (COWID). COWID is a workflow based on the Common Workflow Language that realizes the full potential of sequencing technology for use in reliable SARS-CoV-2 identification and leverages cloud computing to achieve efficient parallelization. COWID outperformed other contemporary methods for identification by offering scalable identification and reliable variant findings with no false-positive results. COWID typically processed each sample of raw sequencing data within 5 min at a cost of only US$0.01. The COWID source code is publicly available (https://***/hendrick0403/COWID) and can be accessed on any computer with Internet access. COWID is designed to be user-friendly;it can be implemented without prior programming knowledge. Therefore, COWID is a time-efficient tool that can be used during a pandemic. © 2023 Published by Oxford University Press.

关键词： cloud repurposing cloud workflow COVID-19 Illumina sequencing parallel computation SARS-CoV-2

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Genome-wide analyses identify 30 loci associated with obsessive–compulsive disorder

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Nature Genetics 2025年 1-13页

作者： Strom, Nora I. Gerring, Zachary F. Galimberti, Marco Yu, Dongmei Halvorsen, Matthew W. Abdellaoui, Abdel Rodriguez-Fontenla, Cristina Sealock, Julia M. Bigdeli, Tim Coleman, Jonathan R. Mahjani, Behrang Thorp, Jackson G. Bey, Katharina Burton, Christie L. Luykx, Jurjen J. Zai, Gwyneth Alemany, Silvia Andre, Christine Askland, Kathleen D. Bäckman, Julia Banaj, Nerisa Barlassina, Cristina Nissen, Judith Becker Bienvenu, O. Joseph Black, Donald Bloch, Michael H. Børte, Sigrid Bosch, Rosa Breen, Michael Brennan, Brian P. Brentani, Helena Buxbaum, Joseph D. Bybjerg-Grauholm, Jonas Byrne, Enda M. Cabana-Dominguez, Judit Camarena, Beatriz Camarena, Adrian Cappi, Carolina Carracedo, Angel Casas, Miguel Cavallini, Maria Cristina Ciullo, Valentina Cook, Edwin H. Crosby, Jesse Cullen, Bernadette A. De Schipper, Elles J. Delorme, Richard Djurovic, Srdjan Elias, Jason A. Estivill, Xavier Falkenstein, Martha J. Fundin, Bengt T. Garner, Lauryn Gironda, Christina Goes, Fernando S. Grados, Marco A. Grove, Jakob Guo, Wei Haavik, Jan Hagen, Kristen Harrington, Kelly Havdahl, Alexandra Höffler, Kira D. Hounie, Ana G. Hucks, Donald Hultman, Christina Janecka, Magdalena Jenike, Eric Karlsson, Elinor K. Kelley, Kara Klawohn, Julia Krasnow, Janice E. Krebs, Kristi Lange, Christoph Lanzagorta, Nuria Levey, Daniel Lindblad-Toh, Kerstin Macciardi, Fabio Maher, Brion Mathes, Brittany McArthur, Evonne McGregor, Nathaniel McLaughlin, Nicole C. Meier, Sandra Miguel, Euripedes C. Mulhern, Maureen Nestadt, Paul S. Nurmi, Erika L. O’Connell, Kevin S. Osiecki, Lisa Ousdal, Olga Therese Palviainen, Teemu Pedersen, Nancy L. Piras, Federica Potluri, Sriramya Rabionet, Raquel Ramirez, Alfredo Rauch, Scott Reichenberg, Abraham Riddle, Mark A. Ripke, Stephan Rosário, Maria C. Sampaio, Aline S. Schiele, Miriam A. Skogholt, Anne Heidi Sloofman, Laura G. Smit, Jan Artigas, María Soler Thomas, Laurent F. Tifft, Eric Vallada, Homero van Kirk, Nathanial Veenstra-VanderWeele, Jeremy Vulink, Nienke N. Walker, Christopher P. Wang, Ying Wendland, Jens R. Winsvold Department of Psychology Humboldt-Universität zu Berlin Berlin Germany Department of Psychiatric Phenomics and Genomics (IPPG) Ludwig-Maximilians University Munich Munich Germany Centre for Psychiatry Research Department of Clinical Neuroscience Karolinska Institutet and Stockholm Health Services Region Stockholm Stockholm Sweden Department of Biomedicine Aarhus University Aarhus Denmark University Hospital of Psychiatry and Psychotherapy University of Bern Bern Switzerland Department of Mental Health and Neuroscience Translational Neurogenomics QIMR Berghofer Medical Research Institute Brisbane QLD Australia Department of Population Health and Immunity Healthy Development and Ageing Walter and Eliza Hall Institute of Medical Research Melbourne VIC Australia Department of Psychiatry Human Genetics Yale University New Haven CT United States VA Connecticut Healthcare System West Haven CT United States Department of Center for Genomic Medicine Psychiatric and Neurodevelopmental Genetics Unit Massachusetts General Hospital Boston MA United States Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA United States Department of Genetics University of North Carolina at Chapel Hill Chapel Hill NC United States Department of Psychiatry Amsterdam University Medical Centers University of Amsterdam Amsterdam Netherlands Center for Research in Molecular Medicine and Chronic Diseases (CiMUS) Genomics and Bioinformatics University of Santiago de Compostela Santiago de Compostela Spain Grupo de Medicina Xenómica Genetics Instituto de Investigación Sanitaria de Santiago de Compostela (FIDIS) Santiago de Compostela Spain Vanderbilt Genetics Institute Vanderbilt University Nashville TN United States Department of Psychiatry and Behavioral Sciences SUNY Downstate Health Sciences University Brooklyn NY United States VA NY Harbor Healthcare System Brooklyn NY United States Social Genetic and Developmental Psychiatry Centre Insti

Obsessive–compulsive disorder (OCD) affects ~1% of children and adults and is partly caused by genetic factors. We conducted a genome-wide association study (GWAS) meta-analysis combining 53,660 OCD cases and 2,044,417 controls and identified 30 independent genome-wide significant loci. Gene-based approaches identified 249 potential effector genes for OCD, with 25 of these classified as the most likely causal candidates, including WDR6, DALRD3 and CTNND1 and multiple genes in the major histocompatibility complex (MHC) region. We estimated that ~11,500 genetic variants explained 90% of OCD genetic heritability. OCD genetic risk was associated with excitatory neurons in the hippocampus and the cortex, along with D₁ and D₂ type dopamine receptor-containing medium spiny neurons. OCD genetic risk was shared with 65 of 112 additional phenotypes, including all the psychiatric disorders we examined. In particular, OCD shared genetic risk with anxiety, depression, anorexia nervosa and Tourette syndrome and was negatively associated with inflammatory bowel diseases, educational attainment and body mass index. © The Author(s) 2025.

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer

Predicting Microsatellite Instability from Histology Images ...

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IEEE Symposium on bioinformatics and Bioengineering (BIBE)

作者： Yun-Chi Chen Tung Chao Wimaya Nitya Phandita Tzu-Yu Sun Huai-Zhi Wang Yi-Hsien Hsieh Le-Yin Hsu Che Lin Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University (NTU) Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan Data Science Degree Program NTU Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients.

关键词： Attention mechanisms Histopathology Computational modeling Precision medicine Small satellites Immunotherapy Colorectal cancer Predictive models Transformers Genetics

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Improving quantitation accuracy in isobaric-labeling mass spectrometry experiments with spectral library searching and feature-based peptide-spectrum match filter

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Scientific Reports 2023年第1期13卷 1-10页

作者： Kuo, Tzu-Yun Wang, Jen-Hung Huang, Yung-Wen Sung, Ting-Yi Chen, Ching-Tai Department of Biochemical Science and Technology National Taiwan University Taipei 10617 Taiwan Bioinformatics Program Taiwan International Graduate Program Institute of Statistical Science Academia Sinica Taipei 11529 Taiwan Institute of Information Science Academia Sinica Taipei 11529 Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei 11221 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei 10617 Taiwan Department of Bioinformatics and Biomedical Engineering Asia University Taichung 41354 Taiwan Center for Precision Health Research Asia University Taichung 41354 Taiwan

Isobaric labeling relative quantitation is one of the dominating proteomic quantitation technologies. Traditional quantitation pipelines for isobaric-labeled mass spectrometry data are based on sequence database searching. In this study, we present a novel quantitation pipeline that integrates sequence database searching, spectral library searching, and a feature-based peptide-spectrum-match (PSM) filter using various spectral features for filtering. The combined database and spectral library searching results in larger quantitation coverage, and the filter removes PSMs with larger quantitation errors, retaining those with higher quantitation accuracy. Quantitation results show that the proposed pipeline can improve the overall quantitation accuracy at the PSM and protein levels. To our knowledge, this is the first study that utilizes spectral library searching to improve isobaric labeling-based quantitation. For users to conveniently perform the proposed pipeline, we have implemented the feature-based filter being executable on both Windows and Linux platforms;its executable files, user manual, and sample data sets are freely available at https://***/comics/Software_*** . Furthermore, with the developed filter, the proposed pipeline is fully compatible with the Trans-Proteomic Pipeline. © 2023, Springer Nature Limited.

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Kidney multiome-based genetic scorecard reveals convergent coding and regulatory variants

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Science (New York, N.Y.) 2025年第6734期387卷 eadp4753页

作者： Liu, Hongbo Abedini, Amin Ha, Eunji Ma, Ziyuan Sheng, Xin Dumoulin, Bernhard Qiu, Chengxiang Aranyi, Tamas Li, Shen Dittrich, Nicole Chen, Hua-Chang Tao, Ran Tarng, Der-Cherng Hsieh, Feng-Jen Chen, Shih-Ann Yang, Shun-Fa Lee, Mei-Yueh Kwok, Pui-Yan Wu, Jer-Yuarn Chen, Chien-Hsiun Khan, Atlas Limdi, Nita A. Wei, Wei-Qi Walunas, Theresa L. Karlson, Elizabeth W. Kenny, Eimear E. Luo, Yuan Kottyan, Leah Connolly, John J. Jarvik, Gail P. Weng, Chunhua Shang, Ning Cole, Joanne B. Mercader, Josep M. Mandla, Ravi Majarian, Timothy D. Florez, Jose C. Haas, Mary E. Lotta, Luca A. Drivas, Theodore G. Vy, Ha My T Nadkarni, Girish N. Wiley, Laura K. Wilson, Melissa P. Gignoux, Christopher R. Rasheed, Humaira Thomas, Laurent F. Åsvold, Bjørn Olav Brumpton, Ben M. Hallan, Stein I. Hveem, Kristian Zheng, Jie Hellwege, Jacklyn N. Zawistowski, Matthew Zöllner, Sebastian Franceschini, Nora Hu, Hailong Zhou, Jianfu Kiryluk, Krzysztof Ritchie, Marylyn D. Palmer, Matthew Edwards, Todd L. Voight, Benjamin F. Hung, Adriana M. Susztak, Katalin Department of Medicine Renal Electrolyte and Hypertension Division University of Pennsylvania Philadelphia PA United States Institute of Diabetes Obesity and Metabolism University of Pennsylvania Philadelphia PA United States Department of Genetics University of Pennsylvania Philadelphia PA United States Penn-CHOP Kidney Innovation Center University of Pennsylvania Philadelphia PA United States Liangzhu Laboratory Zhejiang University 1369 West Wenyi Road Hangzhou Zhejiang China Department of Nephrology Children' Hospital Zhejiang University School of Medicine National Clinical Research Center for Child Health Hangzhou Zhejiang China Institute of Molecular Life Sciences HUN-REN Research Center for Natural Sciences Budapest Hungary Department of Molecular Biology Semmelweis University Budapest Hungary Department of Medicine Federal University of São Paulo São Paulo Brazil Department of Biostatistics Vanderbilt University Medical Center Nashville TN United States Vanderbilt Genetics Institute Vanderbilt University Medical Center Nashville TN United States Institute of Clinical Medicine School of Medicine National Yang Ming Chiao Tung University ROC Taipei Taiwan Division of Nephrology Department of Medicine Taipei Veterans General Hospital ROC Taipei Taiwan Institute of Biomedical Sciences Academia Sinica ROC Taipei Taiwan Cardiovascular Center Taichung Veterans General Hospital Taichung Taiwan ROC National Chung Hsing University Taichung Taiwan ROC Heart Rhythm Center Division of Cardiology Department of Medicine Taipei Veterans General Hospital ROC Taipei Taiwan Department of Internal Medicine College of Medicine National Yang Ming Chiao Tung University ROC Taipei Taiwan Institute of Medicine Chung Shan Medical University Taichung Taiwan ROC Department of Medical Research Chung Shan Medical University Hospital Taichung Taiwan ROC Division of Endocrinology and Metabolism Department of Internal Medicine Kaohsiung Medical Universit

Kidney dysfunction is a major cause of mortality, but its genetic architecture remains elusive. In this study, we conducted a multiancestry genome-wide association study in 2.2 million individuals and identified 1026 (97 previously unknown) independent loci. Ancestry-specific analysis indicated an attenuation of newly identified signals on common variants in European ancestry populations and the power of population diversity for further discoveries. We defined genotype effects on allele-specific gene expression and regulatory circuitries in more than 700 human kidneys and 237,000 cells. We found 1363 coding variants disrupting 782 genes, with 601 genes also targeted by regulatory variants and convergence in 161 genes. Integrating 32 types of genetic information, we present the "Kidney Disease Genetic Scorecard" for prioritizing potentially causal genes, cell types, and druggable targets for kidney disease.

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Comprehensive functional annotation of susceptibility variants identifies genetic heterogeneity between lung adenocarcinoma and squamous cell carcinoma

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Frontiers of Medicine 2021年第2期15卷 275-291页

作者： Na Qin Yuancheng Li Cheng Wang Meng Zhu Juncheng Dai Tongtong Hong Demetrius Albanes Stephen Lam Adonina Tardon Chu Chen Gary Goodman Stig EBojesen Maria Teresa Landi Mattias Johansson Angela Risch H-Erich Wichmann Heike Bickeboller Gadi Rennert Susanne Arnold Paul Brennan John KField Sanjay Shete Loic Le Marchand Olle Melander Hans Brunnstrom Geoffrey Liu Rayjean JHung Angeline Andrew Lambertus AKiemeney Shan Zienolddiny Kjell Grankvist Mikael Johansson Neil Caporaso Penella Woll Philip Lazarus Matthew BSchabath Melinda CAldrich Victoria LStevens Guangfu Jin David CChristiani Zhibin Hu Christopher IAmos Hongxia Ma Hongbing Shen Department of Epidemiology Center for Global HealthSchool of Public HealthNanjing Medical UniversityNanjing 211166China State Key Laboratory of Reproductive Medicine Center for Global HealthNanjing Medical UniversityNanjing 211166China Department of Bioinformatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China Jiangsu Key Lab of Cancer Biomarkers Prevention and TreatmentCollaborative Innovation Center for Cancer MedicineNanjing Medical UniversityNanjing 211166China China International Cooperation Center for Environment and Human Health School of Public HealthNanjing Medical UniversityNanjing 211166China Division of Cancer Epidemiology and Genetics National Cancer InstituteBethesdaMD 20892-9304USA Department of Integrative Oncology British Columbia Cancer AgencyVancouverBC V5Z 1L3Canada Faculty of Medicine University of Oviedo and CIBERESPOviedo 33006Spain Program in Epidemiology Public Health Sciences DivisionFred Hutchinson Cancer Research CenterSeattleWA 98109-1024USA Public Health Sciences Division Swedish Cancer InstituteSeattleWA 98026USA Department of Clinical Biochemistry Copenhagen University HospitalCopenhagen DK-1017Denmark National Cancer Institute BethesdaMD 20892-9304USA Genetic Epidemiology Group International Agency for Research on CancerLyon 69372France Cancer Center Cluster Salzburg at PLUS Department of Molecular BiologyUniversity of SalzburgHeidelberg 5020Austria Institute of Medical Informatics Biometry and EpidemiologyChair of EpidemiologyLudwig Maximilians UniversityMunichBavaria 80539Germany Department of Genetic Epidemiology University Medical Center GoettingenGoettingen 37075Germany Technion Faculty of Medicine Carmel Medical CenterHaifa 3448516Israel Markey Cancer Center University of KentuckyLexingtonKY 40506-0054USA Department of Molecular and Clinical Cancer Medicine Roy Castle Lung Cancer Research ProgrammeThe University of Liverpool Institute of Translational MedicineLiverpool L697Z

Although genome-wide association studies have identified more than eighty genetic variants associated with non-small cell lung cancer(NSCLC)risk,biological mechanisms of these variants remain largely *** integrating a large-scale genotype data of 15581 lung adenocarcinoma(AD)cases,8350 squamous cell carcinoma(SqCC)cases,and 27355 controls,as well as multiple transcriptome and epigenomic databases,we conducted histology-specific meta-analyses and functional annotations of both reported and novel susceptibility *** identified 3064 credible risk variants for NSCLC,which were overrepresented in enhancer-like and promoter-like histone modification peaks as well as DNase I hypersensitive *** factor enrichment analysis revealed that USF1 was AD-specific while CREB1 was *** annotation and genebased analysis implicated 894 target genes,including 274 specifics for AD and 123 for SqCC,which were overrepresented in somatic driver genes(ER=1.95,P=0.005).Pathway enrichment analysis and Gene-Set Enrichment Analysis revealed that AD genes were primarily involved in immune-related pathways,while SqCC genes were homologous recombination deficiency *** results illustrate the molecular basis of both wellstudied and new susceptibility loci of NSCLC,providing not only novel insights into the genetic heterogeneity between AD and SqCC but also a set of plausible gene targets for post-GWAS functional experiments.

关键词： lung cancer genome-wide association study function annotation immune homologous recombination repair deficiency genetic heterogeneity

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Evaluation of drug repositioning by molecular docking of pharmaceutical resources available in the Brazilian healthcare system against SARS-CoV-2

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informatics in Medicine Unlocked 2021年 23卷 100539页

作者： Grahl, Matheus V.C. Alcará, Allan M. Perin, Ana Paula A. Moro, Carlo F. Pinto, Éderson S.M. Feltes, Bruno C. Ghilardi, Isadora M. Rodrigues, Felipe V.F. Dorn, Marcio da Costa, Jaderson C. Norberto de Souza, Osmar Ligabue-Braun, Rodrigo Brain Institute of Rio Grande do Sul (BraIns) Pontifical Catholic University of Rio Grande do Sul Porto Alegre Brazil Graduate Program in Medicine and Health Sciences Pontifical Catholic University of Rio Grande do Sul Porto Alegre Brazil Graduate Program in Medicine Pediatrics and Child Health Pontifical Catholic University of Rio Grande do Sul Porto Alegre Brazil Graduate Program in Cellular and Molecular Biology Center of Biotechnology Federal University of Rio Grande do Sul RS Brazil Laboratory of Structural Bioinformatics and Computational Biology Institute of Informatics Federal University of Rio Grande do Sul RS Brazil Laboratory of Immunobiology and Immunogenetics Institute of Biosciences Federal University of Rio Grande do Sul RS Brazil School of Technology Pontifical Catholic University of Rio Grande do Sul Porto Alegre Brazil Department of Pharmacosciences and Graduate Program in Biosciences (PPGBio) Federal University of Health Sciences of Porto Alegre Porto Alegre Brazil

In 2020 SARS-CoV-2 reached pandemic status, reaching Brazil in mid-February. As of now, no specific drugs for treating the disease are available. In this work, the possibility of interaction between SARS-CoV-2 viral proteins (open and closed spike protein, isolate spike protein RBD, NSP 10, NSP 16, main protease, and RdRp polymerase) and multiple molecules is addressed through the repositioning of drugs available for the treatment of other diseases that are approved by the FDA and covered by SUS, the Brazilian Public health System. Three different docking software were used, followed by a unification of the results by independent evaluation. Afterwards, the chemical interactions of the compounds with the targets were inspected via molecular dynamics and analyzed. The results point to a potential effectiveness of Penciclovir, Ribavirin, and Zanamivir, from a set of 48 potential candidates. They may also be multi-target drugs, showing high affinity with more than one viral protein. Further in vitro and in vivo validation is required to assess the suitability of repositioning the proposed drugs for COVID-19. © 2021 The Authors

关键词： COVID19 Drug repositioning Molecular docking SARS-CoV-2 SUS

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Genomic, transcriptomic, and immunogenomic landscape of over 1300 sarcomas of diverse histology subtypes

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Nature communications 2025年第1期16卷 4206-4206页

作者： Soupir, Alex Ospina, Oscar E. Hampton, Oliver Churchman, Michelle Radmacher, Michael Hedges, Dale McKean, David Agius, Phaedra Zeeshan, Saman Seligson, Nathan D. Pollock, Raphael Liebner, David Chen, James L. Tinoco, Gabriel Salhia, Bodour McCarter, Martin Wilky, Breelyn A. Miller, Benjamin J. Cavnar, Michael J. Groundland, John S. Schneider, Bryan P. Riedlinger, Gregory Edge, Stephen B. Moskaluk, Christopher A. Cardona, Kenneth Naqash, Abdul Rafeh Gonzalez, Ricardo J. Mullinax, John E. Joyce, David M. Binitie, Odion Douglas Letson, G. Naghavi, Arash O. Druta, Mihaela Reed, Damon R. Siegel, Erin M. Teer, Jamie K. Fridley, Brooke L. Brohl, Andrew S. Department of Biostatistics and Bioinformatics Moffitt Cancer Center Tampa FL United States Hudson FL United States Department of Biomedical and Health Informatics School of Medicine University of Missouri Kansas City MO United States Department of Pharmacotherapy and Translational Research College of Pharmacy University of Florida Jacksonville FL United States Division of Surgical Oncology Ohio State University Comprehensive Cancer Center College of Medicine Ohio State University Wexner Medical Center Columbus OH United States Division of Medical Oncology Ohio State University Comprehensive Cancer Center College of Medicine Ohio State University Wexner Medical Center Columbus OH United States Department of Biology Norris Comprehensive Cancer Center Keck School of Medicine University of Southern California Los Angeles CA United States University of Colorado School of Medicine Aurora CO United States Department of Orthopaedics and Rehabilitation University of Iowa IO Iowa City United States Department of Surgery University of Kentucky Lexington KY United States University of Utah Huntsman Cancer Institute Salt Lake City UT United States Indiana University Simon Comprehensive Cancer Center Indianapolis 46202 IN United States Rutgers Cancer Institute of New Jersey New Brunswick NJ USA Roswell Park Comprehensive Cancer Center Buffalo NY United States Department of Pathology University of Virginia Charlottesville VA United States Division of Surgical Oncology Emory University Atlanta GA United States Medical Oncology/Phase 1 program Stephenson Cancer Center University of Oklahoma Health Sciences Oklahoma City OK United States Sarcoma Department Moffitt Cancer Center Tampa FL United States Department of Radiation Oncology Moffitt Cancer Center Tampa FL United States Department of Individualized Cancer Management Moffitt Cancer Center Tampa FL United States Department of Cancer Epidemiology Moffitt Cancer Center Tampa FL United State

Given their rarity and diversity, a fundamental understanding of the genomic underpinnings for many sarcoma subtypes is still lacking. To better define the molecular landscape of this group of diseases, we perform matched whole exome sequencing and RNA sequencing on a cohort of 1340 sarcoma tumor specimens. We identify recurrent somatic mutations and observe an increased mutational burden in metastatic vs. primary samples (p < 0.001). We observe frequent copy number alterations including whole genome doubling, with this feature being more common in metastatic tumors (p = 0.026). Estimation of immune cell abundances followed by hierarchical clustering identifies five immune subtypes ranging from low to high and we observe inferior overall survival in immune deplete clusters compared to immune enriched (p < 0.01). Interestingly, GIST predominantly form a distinct "immune intermediate" cluster that is marked by a specific enrichment for NK cells (FDR < 0.01). © 2025. The Author(s).

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Declaración PRISMA 2020: una guía actualizada para la publicación de revisiones sistemáticas

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Revista Española de Cardiología (English Edition) 2021年第9期74卷 790-799页

作者： Matthew J. Page Joanne E. McKenzie Patrick M. Bossuyt Isabelle Boutron Tammy C. Hoffmann Cynthia D. Mulrow Larissa Shamseer Jennifer M. Tetzlaff Elie A. Akl Sue E. Brennan Roger Chou Julie Glanville Jeremy M. Grimshaw Asbjørn Hróbjartsson Manoj M. Lalu Tianjing Li Elizabeth W. Loder Evan Mayo-Wilson David Moher School of Public Health and Preventive Medicine Monash University Melbourne Australia Department of Clinical Epidemiology Biostatistics and Bioinformatics Amsterdam University Medical Centres University of Amsterdam Amsterdam Países Bajos Université de Paris Centre of Epidemiology and Statistics (CRESS) Inserm F 75004 Paris Francia Institute for Evidence-Based Healthcare Faculty of Health Sciences and Medicine Bond University Gold Coast Australia University of Texas Health Science Center at San Antonio San Antonio Texas Estados Unidos Annals of Internal Medicine Knowledge Translation Program Li Ka Shing Knowledge Institute Toronto Canadá School of Epidemiology and Public Health Faculty of Medicine University of Ottawa Ottawa Canada Evidence Partners Ottawa Canadá Clinical Research Institute American University of Beirut Beirut Líbano Department of Health Research Methods Evidence and Impact McMaster University Hamilton Ontario Canadá Department of Medical Informatics and Clinical Epidemiology Oregon Health & Science University Portland Oregón Estados Unidos York Health Economics Consortium (YHEC Ltd) University of York York Reino Unido Clinical Epidemiology Program Ottawa Hospital Research Institute Ottawa Canadá School of Epidemiology and Public Health University of Ottawa Ottawa Canadá Department of Medicine University of Ottawa Ottawa Canadá Centre for Evidence-Based Medicine Odense Odense University Hospital Odense Dinamarca Department of Clinical Research University of Southern Denmark Odense Dinamarca Open Patient data Explorative Network Odense University Hospital Odense Dinamarca Department of Anesthesiology and Pain Medicine The Ottawa Hospital Ottawa Canadá Clinical Epidemiology Program Blueprint Translational Research Group Ottawa Hospital Research Institute Ottawa Canadá Regenerative Medicine Program Ottawa Hospital Research Institute Ottawa Canadá Department of Ophthalmology School of Medicine University of Colorado Denver Den

La declaración PRISMA ( Preferred Reporting Items for Systematic reviews and Meta-Analyses ), publicada en 2009, se diseñó para ayudar a los autores de revisiones sistemáticas a documentar de manera transparente el porqué de la revisión, qué hicieron los autores y qué encontraron. Durante la última década, ha habido muchos avances en la metodología y terminología de las revisiones sistemáticas, lo que ha requerido una actualización de esta guía. La declaración prisma 2020 sustituye a la declaración de 2009 e incluye una nueva guía de presentación de las publicaciones que refleja los avances en los métodos para identificar, seleccionar, evaluar y sintetizar estudios. La estructura y la presentación de los ítems ha sido modificada para facilitar su implementación. En este artículo, presentamos la lista de verificación PRISMA 2020 con 27 ítems, y una lista de verificación ampliada que detalla las recomendaciones en la publicación de cada ítem, la lista de verificación del resumen estructurado PRISMA 2020 y el diagrama de flujo revisado para revisiones sistemáticas. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews. Full English text available from : ***

关键词： Guía de publicación Revisión sistemática Metanálisis Escritura médica Publishing guide Systematic review Meta-analysis Medical writing

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A web-based, hospital-wide health care-associated bloodstream infection surveillance and classification system: Development and evaluation

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JMIR Medical informatics 2015年第3期3卷 e31页

作者： Tseng, Yi-Ju Wu, Jung-Hsuan Lin, Hui-Chi Chen, Ming-Yuan Ping, Xiao-Ou Sun, Chun-Chuan Shang, Rung-Ji Sheng, Wang-Huei Chen, Yee-Chun Lai, Feipei Chang, Shan-Chwen Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Computational Health Informatics Program Boston Children's Hospital Boston MA United States Department of Electrical Engineering National Taiwan University Taipei Taiwan Center for Infection Control National Taiwan University Hospital Taipei Taiwan National Taiwan University Hospital Information Systems Office Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Internal Medicine National Taiwan University Hospital and College of Medicine Taipei Taiwan National Health Research Institutes National Institute of Infectious Diseases and Vaccinology Miaoli Taiwan

Background: Surveillance of health care-associated infections is an essential component of infection prevention programs, but conventional systems are labor intensive and performance dependent. Objective: To develop an automatic surveillance and classification system for health care-associated bloodstream infection (HABSI), and to evaluate its performance by comparing it with a conventional infection control personnel (ICP)-based surveillance system. Methods: We developed a Web-based system that was integrated into the medical information system of a 2200-bed teaching hospital in Taiwan. The system automatically detects and classifies HABSIs. Results: In this study, the number of computer-detected HABSIs correlated closely with the number of HABSIs detected by ICP by department (n=20;r=.999 P<.001) and by time (n=14;r=.941;P<.001). Compared with reference standards, this system performed excellently with regard to sensitivity (98.16%), specificity (99.96%), positive predictive value (95.81%), and negative predictive value (99.98%). The system enabled decreasing the delay in confirmation of HABSI cases, on average, by 29 days. Conclusions: This system provides reliable and objective HABSI data for quality indicators, improving the delay caused by a conventional surveillance system. © 2020 JMIR Publications Inc. All rights reserved.

关键词： health care-associated infection Infection control Information systems Surveillance Web-based services

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