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arXiv 2023年

作者： Johnson, Ruth Li, Michelle M. Noori, Ayush Queen, Owen Zitnik, Marinka Department of Biomedical Informatics Harvard Medical School BostonMA02115 United States Berkowitz Family Living Laboratory Harvard Medical School BostonMA02115 United States Bioinformatics and Integrative Genomics Program Harvard Medical School BostonMA02115 United States Harvard College CambridgeMA02138 United States Broad Institute of MIT and Harvard CambridgeMA02142 United States Harvard Data Science Initiative CambridgeMA02138 United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University AllstonMA United States

In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data-from patient records to imaging-graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters or minimal to no re-training. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions. © 2023, CC BY.

关键词： Knowledge graph

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Correction to: The International Mouse Phenotyping Consortium (IMPC): a functional catalogue of the mammalian genome that informs conservation

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Conservation Genetics 2019年第1期20卷 135-136页

作者： Violeta Muñoz-Fuentes Pilar Cacheiro Terrence F. Meehan Juan Antonio Aguilar-Pimentel Arthur L. Beaudet Steve D. M. Brown Mary E. Dickinson Ann M. Flenniken Paul Flicek Antonella Galli Hamed Haseli Mashhadi Jason D. Heaney Martin Hrabě de Angelis Jong Kyoung Kim K. C. Kent Lloyd Colin McKerlie Hugh Morgan Stephen A. Murray Lauryl M. J. Nutter Patrick T. Reilly John R. Seavitt Je Kyung Seong Michelle Simon Hannah Wardle-Jones Ann-Marie Mallon Damian Smedley Helen E. Parkinson European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Clinical Pharmacology William Harvey Research Institute School of Medicine and Dentistry Queen Mary University of London London UK German Mouse Clinic Institute of Experimental Genetics Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg Germany Department of Molecular and Human Genetics Baylor College of Medicine Houston USA Medical Research Council Harwell Institute (Mammalian Genetics Unit and Mary Lyon Centre) Harwell UK Department of Molecular Physiology and Biophysics Baylor College of Medicine Houston USA The Centre for Phenogenomics Toronto Canada Mount Sinai Hospital Toronto Canada Wellcome Trust Sanger Institute Cambridge UK German Center for Diabetes Research (DZD) Neuherberg Germany School of Life Science Weihenstephan Technische Universität München Freising Germany Department of New Biology DGIST Daegu Republic of Korea Mouse Biology Program University of California Davis USA The Hospital for Sick Children Toronto Canada The Jackson Laboratory Bar Harbor USA PHENOMIN-iCS Illkirch Cedex France Laboratory of Developmental Biology and Genomics College of Veterinary Medicine Interdisciplinary Program for Bioinformatics and Program for Cancer Biology Seoul National University Seoul Republic of Korea

The original publication of this article unfortunately contained the following mistakes.

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Graph Artificial Intelligence in Medicine

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Biomedical Data Science 1000年第1期7卷 345-368页

作者： Ruth Johnson Michelle M. Li Ayush Noori Owen Queen Marinka Zitnik 1Department of Biomedical Informatics Harvard Medical School Boston Massachusetts USA email: marinka@hms.harvard.edu 2Berkowitz Family Living Laboratory Harvard Medical School Boston Massachusetts USA 3Bioinformatics and Integrative Genomics Program Harvard Medical School Boston Massachusetts USA 4Department of Computer Science Harvard John A. Paulson School of Engineering and Applied Sciences Allston Massachusetts USA 5Broad Institute of MIT and Harvard Cambridge Massachusetts USA 7Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Allston Massachusetts USA 6Harvard Data Science Initiative Cambridge Massachusetts USA

In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data—from patient records to imaging—graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human–AI collaboration, paving the way toward clinically meaningful predictions.

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Quantum processor-inspired machine learning in the biomedical sciences

arXiv

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arXiv 2019年

作者： Li, Richard Y. Gujja, Sharvari Bajaj, Sweta R. Gamel, Omar E. Cilfone, Nicholas Gulcher, Jeffrey R. Lidar, Daniel A. Chittenden, Thomas W. Department of Chemistry University of Southern California Los AngelesCA United States Computational Biology and Bioinformatics Program Department of Biological Sciences University of Southern California Los AngelesCA United States Center for Quantum Information Science & Technology University of Southern California Los AngelesCA United States Computational Statistics and Bioinformatics Group Advanced Artificial Intelligence Research Laboratory Genuity Science BostonMA United States Complex Biological Systems Alliance MedfordMA United States Cancer Genetics Group Genuity Science BostonMA United States Department of Electrical Engineering University of Southern California Los AngelesCA United States Department of Physics and Astronomy University of Southern California Los AngelesCA United States Division of Genetics and Genomics Boston Children's Hospital Harvard Medical School BostonMA United States

Recent advances in high-throughput genomic technologies coupled with exponential increases in computer processing and memory have allowed us to interrogate the complex aberrant molecular underpinnings of human disease from a genome-wide perspective. While the deluge of genomic information is expected to increase, a bottleneck in conventional high-performance computing is rapidly approaching. Inspired in part by recent advances in physical quantum processors, we evaluated several unconventional machine learning (ML) strategies on actual human tumor data;namely "Ising-type" methods whose objective function is formulated identically to simulated annealing and quantum annealing, as well as a restricted Boltzmann machine. Here we show for the first time the efficacy of multiple Ising-type ML algorithms for classification of high-dimensional, multi-omics human cancer data from the Cancer Genome Atlas. To assess algorithm performance, we compared these classifiers to a variety of standard ML methods. Our results indicate the feasibility of using Ising-type ML to provide competitive classification of human cancer types and associated molecular subtypes and superior performance with smaller training datasets, thus providing compelling empirical evidence for the potential future application of unconventional computing approaches in the biomedical sciences. © 2019, CC BY.

关键词： Machine learning

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Shotgun Sequencing of Southern Thailand Traditional Sweet Pickled Mango During the Production Process

SSRN

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SSRN 2023年

作者： Indrati, Niken Sirisarn, Wanchat Nuanpirom, Jiratchaya Yodsawat, Prasert Wanna, Warapond Prombutara, Pinidphon Phonsatta, Natthaporn Panya, Atikorn Maneerat, Suppasil Poungsombat, Patcha Khoomrung, Sakda Sumpavapol, Punnanee Food Microbiology and Safety Laboratory Food Science and Technology Program Faculty of Agro-Industry Prince of Songkla University Songkhla90110 Thailand Center of Genomics and Bioinformatics Research Faculty of Science Prince of Songkla University Songkhla90110 Thailand Omics Sciences and Bioinformatics Center Faculty of Science Chulalongkorn University Bangkok10330 Thailand Thailand Science Park Pathumthani12120 Thailand Center of Excellence in Innovative Biotechnology for Sustainable Utilization of Bioresources Faculty of Agro-Industry Prince of Songkla University Songkhla90110 Thailand Metabolomics and Systems Biology Department of Biochemistry Faculty of Medicine Siriraj Hospital Mahidol University Bangkok10700 Thailand Siriraj Metabolomics and Phenomics Center Faculty of Medicine Siriraj Hospital Mahidol University Bangkok10700 Thailand Faculty of Science Mahidol University Bangkok10400 Thailand Faculty of Science and Technology Songkhla Rajabhat University Songkhla90000 Thailand

Southern Thailand's traditional sweet pickled mango, known as Ma-Muang Bao Chae-Im (MBC), holds an important place in the local community. Several immersion processes, including brining and submersion in a hypertonic sugar solution, have been employed in the MBC production process. The occurrence of microbial succession during the production process affects its flavor due to the produced metabolites. However, microbial succession during the production process has received inadequate research. Therefore, this study aimed to (i) understand the microbiome alteration and (ii) investigate the functional profiling (metabolic pathways) during MBC production process. Through shotgun metagenomics, the microbial succession was shifting from bacteria in raw mango to fungi in MBC, with Ascomycota as the dominant phylum and Hanseniaspora as the dominant genus in MBC. The abundance of biosynthesis pathways was in line with the occurrence of microbial growth throughout the production process and prolonged storage. Polymyxin resistance was found as a significant pathway during brining 1 (B1-RM) and brining 2 (B2-B1). Mixed acid fermentation pathway and glyoxylate cycle were suggested to have a role in the aroma development of MBC by synthesizing metabolites related to the acidic and alcoholic aroma. © 2023, The Authors. All rights reserved.

关键词： Brining

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Building a Model for Predicting Target Gene Expression in Rice T-DNA Insertional Mutants by Machine Learning Approaches

Research Square

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Research Square 2020年

作者： Liao, Chi-Chou Chen, Liang-Jwu Lo, Shuen-Fang Chen, Chi-Wei Chen, Jia-Jyun Chu, Yen-Wei Institute of Molecular Biology National Chung Hsing University Taichung Taiwan Agricultural Biotechnology Center National Chung Hsing University Taichung Taiwan Institute of Molecular Biology Academia Sinica Taipei Taiwan Department of Computer Science and Engineering National Chung Hsing University Taichung Taiwan Institute of Genomics and Bioinformatics National Chung Hsing University Taichung Taiwan Biotechnology Center National Chung Hsing University Taichung Taiwan Ph.D. Program in Translational Medicine National Chung Hsing University Taichung Taiwan Rong Hsing Research Center for Translational Medicine National Chung Hsing University Taichung402 Taiwan

Background: T-DNA activation-tagging technology is widely used to enhance flanking gene expression near the site of insertion for functional genomics research in rice. However, whether the expression of a gene of interest is enhanced must be validated experimentally. Results: In this study, we built a model to predict gene expression in T-DNA mutants by machine learning approaches, thereby improving the efficiency of screening for activated genes. We gathered experimental consisting of gene expression data in T-DNA mutants and captured the PROMOTER and MIDDLE sequences for encoding. In first-layer models, SVM models were constructed with nine features consisting of information about biological function and local and global sequences. Feature-encoding based on the PROMOTER sequence was weighted by logistic regression. The second-layer models integrated 16 first-layer models with feature selection and the algorithm, which were selected from nine feature selection methods and 65 classified methods, respectively. The accuracy of the final two-layer machine learning model, referred to as was 99.3% based on fivefold cross-validation, and 85.6% based on independent-testing. Conclusion: We discovered that the information within the local sequence had a greater contribution than the global sequence with respect to classification had a good predictive ability for target genes within 20 from the 35S enhancer. Based on the analysis of significant sequences, the G-box regulatory sequence may also play an important role in the mechanism of activation of the 35S enhancer. © 2020, CC BY.

关键词： Gene expression

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Meeting report: "Metagenomics, metadata and meta-analysis" (M3) workshop at the pacific symposium on biocomputing 2010

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Standards in Genomic Sciences 2010年第3期2卷 357-360页

作者： Hirschman, Lynette Sterk, Peter Field, Dawn Wooley, John Cochrane, Guy Gilbert, Jack Kolker, Eugene Kyrpides, Nikos Meyer, Folker Mizrachi, Ilene Nakamura, Yasukazu Sansone, Susanna-Assunta Schriml, Lynn Tatusova, Tatiana White, Owen Yilmaz, Pelin Information Technology Center The MITRE Corporation Bedford MA United States NERC Center for Ecology and Hydrology Oxford OX1 3SR United Kingdom Wellcome Trust Sanger Institute Hinxton Cambridge United Kingdom University of California San Diego La Jolla CA United States European Molecular Biology Laboratory (EMBL) European Bioinformatics Institute (EBI) Hinxton Cambridge United Kingdom Plymouth Marine Laboratory (PML) Plymouth United Kingdom Seattle Children's Hospital Seattle WA United States Genome Biology Program DOE Joint Genome Institute Walnut Creek CA United States Argonne National Laboratory Argonne IL 60439 9700 South Cass Avenue United States National Center for Biotechnology Information National Library of Medicine National Institutes of Health Bethesda MD United States DNA Data Bank of Japan National Institute of Genetics Yata Mishima Japan University of Maryland School of Medicine Baltimore MD United States Microbial Genomics Group Max Planck Institute for Marine Microbiology and Jacobs University Bremen Bremen Germany

This report summarizes the M3 Workshop held at the January 2010 Pacific Symposium on Biocomputing. The workshop, organized by Genomic Standards Consortium members, in-cluded five contributed talks, a series of short presentations from stakeholders in the genom-ics standards community, a poster session, and, in the evening, an open discussion session to review current projects and examine future directions for the GSC and its stakeholders.

关键词： Special Interest Group Human Microbiome Project Metadata Standard Genomic Standard Consortium International Nucleotide Sequence Database Collaboration

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Novel strategy for disease risk prediction incorporating predicted gene expression and DNA methylation data:a multi-phased study of prostate cancer

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Cancer Communications 2021年第12期41卷 1387-1397页

作者： Chong Wu Jingjing Zhu Austin King Xiaoran Tong Qing Lu Jong Y.Park Liang Wang Guimin Gao Hong-Wen Deng Yaohua Yang Karen E.Knudsen Timothy R.Rebbeck Jirong Long Wei Zheng Wei Pan David V.Conti Christopher A Haiman Lang Wu Department of Statistics Florida State UniversityTallahasseeFL 32304USA Cancer Epidemiology Division Population Sciences in the Pacific ProgramUniversity of Hawaii Cancer CenterUniversity of Hawaii at ManoaHonoluluHI 96813USA Department of Epidemiology and Biostatistics Michigan State UniversityEast LansingMI 48824USA Department of Biostatistics University of FloridaGainesvilleFL 32603USA Department of Cancer Epidemiology H.Lee Moffitt Cancer Center and Research InstituteTampaFL 33612USA Department of Tumor Biology H.Lee Moffitt Cancer Center and Research InstituteTampaFL 33612USA Department of Public Health Sciences University of ChicagoChicagoIL 60637USA Center of Bioinformatics and Genomics Department of Global Biostatistics and Data ScienceTulane UniversityNew OrleansLA 70112USA Division of Epidemiology Department of MedicineVanderbilt Epidemiology CenterVanderbilt-Ingram Cancer CenterVanderbilt University Medical CenterNashvilleTN 37203USA Department of Cancer Biology Sidney Kimmel Cancer CenterThomas Jefferson UniversityPhiladelphiaPA 19107USA Department of Medical Oncology Dana-Farber Cancer InstituteBostonMA 02215USA Department of Epidemiology Harvard TH Chan School of Public HealthBostonMA 02115USA Division of Biostatistics University of MinnesotaMinneapolisMN 55455USA Department of Preventive Medicine Keck School of MedicineUniversity of Southern California/Norris Comprehensive Cancer CenterLos AngelesCA 90033USA

Background:DNA methylation and gene expression are known to play important roles in the etiology of human diseases such as prostate cancer(PCa).However,it has not yet been possible to incorporate information of DNA methylation and gene expression into polygenic risk scores(PRSs).Here,we aimed to develop and validate an improved PRS for PCa risk by incorporating genetically predicted gene expression and DNA methylation,and other genomic information using an integrative ***:Using data from the PRACTICAL consortium,we derived multiple sets of genetic scores,including those based on available single-nucleotide polymorphisms through widely used methods of pruning and thresholding,LDpred,LDpred-funt,AnnoPred,and EBPRS,as well as PRS constructed using the genetically predicted gene expression and DNA methylation through a revised pruning and thresholding *** the tuning step,using the UK Biobank data(1458 prevalent cases and 1467 controls),we selected PRSs with the best *** an independent set of data from the UK Biobank,we developed an integrative PRS combining information from individual ***,in the testing step,we tested the performance of the integrative PRS in another independent set of UK Biobank data of incident cases and ***:Our constructed PRS had improved performance(C statistics:76.1%)over PRSs constructed by individual benchmark methods(from 69.6%to 74.7%).Furthermore,our new PRS had much higher risk assessment power than family *** overall net reclassification improvement was 69.0%by adding PRS to the baseline model compared with 12.5%by adding family ***:We developed and validated a new PRS which may improve the utility in predicting the risk of developing *** innovative method can also be applied to other human diseases to improve risk prediction across multiple outcomes.

关键词： risk prediction polygenic risk scores predicted gene expression predicted DNA methylation integrative models prostate cancer

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Genetics in Medicine 2025年第6期27卷 101375页

作者： Loberti, Lorenzo Adamo, Loredaria Antolini, Enrica Casamassima, Giulia Destrèe, Anne Brunetti-Pierri, Nicola Genevieve, David Christophe, Philippe Coubes, Christine Van Esch, Hilde Herget, Theresia Kortüm, Fanny Lisfeld, Jasmin Möllring, Anna Charlotte Zenker, Martin Levy, Jonathan Perrin, Laurence Tabet, Anne-Claude Maruani, Anna Sorlin, Arthur Stieber, Daniel Herissant, Lucas Dahan, Karin Sinibaldi, Lorenzo Capolino, Rossella Dentici, Maria Lisa Dallapiccola, Bruno Novelli, Antonio Garavelli, Livia Caraffi, Stefano Giuseppe Piatelli, Gianluca Valenzuela, Irene Digilio, Maria Cristina Caumes, Roseline Knopp, Cordula Chwiałkowska, Karolina Jezela-Stanek, Aleksandra Kwasniewski, Miroslaw Korotko, Urszula Gorzałczyńska, Ewelina Canitano, Roberto Grosso, Salvatore Rahikkala, Elisa Mattern, Larissa Elbracht, Miriam Zuffardi, Orsetta Caputo, Valentina Toschi, Benedetta Beunders, Gea Leeuwen, Lisette Elting, Mariet W. van der Laan, Liselot Broekema, Marjoleine F. Groffen, Alexander J. van de Kamp, Jiddeke M. van Haelst, Mieke M. Alders, Marielle Mauro, Salvatore Pietro De Razza, Francesca Varvara, Dora Kick, Johanna Gaspar, Harald Braun, Dominique Lausberg, Eva Maier, Andrea Ruault, Valentin Genesio, Rita Tartaglia, Marco Tita, Rossella Bruttini, Mirella Longo, Ilaria Baldassarri, Margherita Mencarelli, Maria Antonietta Renieri, Alessandra Pinto, Anna Maria Medical Genetics University of Siena Siena Italy Med Biotech Hub and Competence Centre Department of Medical Biotechnologies University of Siena Siena Italy Genetica Medica Azienda Ospedaliera Universitaria Senese Siena Italy Institut de Pathologie et de Génétique Gosselies Belgium Department of Translational Medicine University of Naples “Federico II” Naples Italy Telethon Institute of Genetics and Medicine Pozzuoli Italy Scuola Superiore Meridionale (SSM School of Advanced Studies) Genomics and Experimental Medicine Program University of Naples Federico II Naples Italy Montpellier University and INSERM U1183 Montpellier France Reference Center for Malformative Syndrome and Developmental Anomalies Clinical Genetics Unit Montpellier University Hospital ERN ITHACA Montpellier France Unité Fonctionnelle d'Innovation diagnostique des maladies rares FHU-TRANSLAD CHU Dijon Bourgogne Dijon France UFR Des Sciences de Santé INSERM-Université de Bourgogne UMR1231 GAD “Génétique des Anomalies du Développement” FHU-TRANSLAD Dijon France Département de Génétique Médicale Hôpital Arnaud de Villeneuve CHRU Montpellier Montpellier France Center for Human Genetics University Hospitals Leuven Leuven Belgium Institute of Human Genetics University Medical Center Hamburg-Eppendorf Hamburg Germany Institute of Human Genetics University Hospital Otto-von-Guericke University Magdeburg Magdeburg Germany Genetics Department AP-HP Robert-Debré University Hospital Paris France Excellence Centre for Autism & Neuro-developmental Disorders Child and Adolescent Psychiatry Department Robert Debré Hospital Paris and CRMR Déficiences intellectuelles et TND de causes rares-Robert-Debré Paris France National Center of Genetics Laboratoire National de Santé Dudelange Luxembourg Medical Genetics Unit and Scientific Rectorate and Laboratory of Medical Genetics Translational Cytogenomics Research Unit Bambino Gesù Children's Hospital IRCCS Rome Italy Medical Genetics Unit Azienda U

Purpose: AUTS2-related syndrome is characterized by developmental delay, autism spectrum disorder, and intellectual disability. From alternative promoters, AUTS2 encodes 2 distinct long and short isoforms encoding a putative transcriptional activator. Methods: Through a European collaborative study, we collected clinical and genotype data on the largest AUTS2-related syndrome cohort of 58 patients harboring genomic rearrangements or single-nucleotide variants (SNVs). Results: Pathogenic SNVs were recurrently found in individuals from different countries, suggesting mutational hotspots. Independent of the underlying defect at the AUTS2 locus, we observed that autistic behavior, hyperactivity, learning difficulties, and speech delay are common features of AUTS2-related syndrome. Among patients with SNVs, individuals carrying pathogenic variants affecting both longer and shorter AUTS2 transcripts showed a recognizable phenotype with microcephaly, brachycephaly, microretrognathia, broad nasal base, and anteverted nares. Behavioral disorders were more common in patients with variants affecting only the longer isoform. Arthrogryposis and stiff movements were only observed in patients with SNVs. Conclusion: This study provides a comprehensive clinical characterization of AUTS2-related syndrome, reveals few genotype-phenotype correlations, and suggests that the disruption of the 2 distinct AUTS2 transcripts has a different impact on the clinical phenotype. © 2025 The Authors

关键词： AUTS2 AUTS2-syndrome Dysmorphology Genotype-phenotype Neurodevelopmental disorder

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The perceptions of Brazilian postgraduate students about the impact of COVID-19 on their well-being and academic performance

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International Journal of Educational Research Open 2022年 3卷 100185页

作者： Corrêa, Roberta Pires Castro, Helena Carla Ferreira, Roberto Rodrigues Araújo-Jorge, Tania Stephens, Paulo Roberto Soares Program in Education in Biosciences and Health Oswaldo Cruz Institute Oswaldo Cruz Foundation (Fiocruz) Rio de Janeiro Brazil Laboratory of Innovations in Therapies Education and Bioproducts Oswaldo Cruz Institute Oswaldo Cruz Foundation (Fiocruz) Rio de Janeiro Brazil Laboratory of Antibiotics Biochemistry Teaching and Molecular Modelling Federal Fluminense University (LABiEMol-UFF) UFF Niterói Brazil Postgraduate Program in Science Technologies and Inclusion Federal Fluminense University (PGCTIn/UFF) UFF Niterói Brazil Professional Masters Course in Diversity and Inclusion Federal Fluminense University (CMPDI/UFF) UFF Niterói Brazil Postgraduate Program in Pathology Federal Fluminense University (PPGPatol/UFF) UFF Niterói Brazil Laboratory of Functional Genomics and Bioinformatics Oswaldo Cruz Institute (LAGFB-IOC/Fiocruz) Oswaldo Cruz Foundation (Fiocruz) Rio de Janeiro Brazil

This study aimed at identifying the perceptions of Brazilian postgraduate students from all over the country on the impacts of the COVID-19 pandemic on their academic trajectories. Data from 5985 postgraduate students were collected in the end of 2020, through a 37-item questionnaire, including multiple-choice questions, through Google Forms. The questions were divided into blocks with different proposals: personal profile, academic profile, issues related to COVID-19 infection, and issues related to mental health. Our analysis showed that 51.43% were master's degree students;43.02% were doctorate and 5.55% were specialization students, mostly attending Biological, Health, and Human Sciences post-graduation courses (18.13%, 17.91%, and 17.38%, respectively) of different Brazilian educational institutions, including public (e.g., UFRJ) and private (e.g., PUC) federal universities as well as research institutions (e.g., Fiocruz) from all five regions of Brazil (north, south, southeast, northeast, and center Midwest). Most of them were academically impacted by the COVID-19 pandemic, which also involved psychological aspects such as high levels of anxiety and depression. The results showed readjustments of research projects, and academic activities, which in some particular research fields led to the successful completion through the remote activities. However, efforts are still needed by graduate programs in order to allow greater flexibility in academic activities to fulfill all previous planning and chronograms, in addition to implementing ongoing projects to support students' mental health. © 2022

关键词： Academic activities COVID-19 Mental health Pandemic in Brazil Postgraduate students

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