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Augment Reality-Based Teaching Practice

Biomedical engineering education 2021年第1期1卷 237-241页

作者： You Wu Mingzhen Zhang Xiaosi Li Yu Gan Chao Zhao Department of Chemical and Biological Engineering The University of Alabama P. O. Box870203 Tuscaloosa AL 35401 USA. Computational Structural Biology Section Basic Science Program Frederick National Laboratory for Cancer Research Frederick MD 21702 USA. Department of Electrical and Computer Engineering The University of Alabama P. O. Box870286 Tuscaloosa AL 35401 USA.

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The Minimum Information about CLinical Artificial Intelligence Checklist for Generative Modeling Research (MI-CLAIM-GEN)

arXiv

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arXiv 2024年

作者： Miao, Brenda Y. Chen, Irene Y. Williams, Christopher Y.K. Davidson, Jaysón Garcia-Agundez, Augusto Sun, Shenghuan Zack, Travis Saria, Suchi Arnaout, Rima Quer, Giorgio Sadaei, Hossein J. Torkamani, Ali Beaulieu-Jones, Brett Yu, Bin Gianfrancesco, Milena Butte, Atul J. Norgeot, Beau Sushil, Madhumita Bakar Computational Health Sciences Institute University of California San Francisco San FranciscoCA United States UCSF-UC Berkeley Joint Program in Computational Precision Health University of California Berkeley University of California San Francisco BerkeleyCA United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA United States Berkeley AI Research University of California Berkeley BerkeleyCA United States Department of Medicine Division of Rheumatology University of California San Francisco San FranciscoCA United States Helen Diller Family Comprehensive Cancer Center University of California San Francisco San FranciscoCA United States Center for Data-driven Insights and Innovation University of California Office of the President OaklandCA United States Bayesian Health New YorkNY10282 United States Department of Computer Science Johns Hopkins University Whiting School of Engineering BaltimoreMD United States Department of Health Policy & Management Johns Hopkins University Bloomberg School of Public Health BaltimoreMD United States Department of Medicine Johns Hopkins Medicine BaltimoreMD21205 United States Departments of Medicine Radiology and Pediatrics University of California San Francisco San FranciscoCA United States Scripps Research Translational Institute La Jolla CA United States Department of Integrative Structural and Computational Biology Scripps Research La Jolla CA92037 United States Department of Medicine University of Chicago ChicagoIL United States Department of Statistics University of California Berkeley BerkeleyCA United States Center for Computational Biology University of California Berkeley BerkeleyCA United States Qualified Health PBC Palo AltoCA United States

Recent advances in generative models, including large language models (LLMs), vision language models (VLMs), and diffusion models, have accelerated the field of natural language and image processing in medicine and marked a significant paradigm shift in how biomedical models can be developed and deployed. While these models are highly adaptable to new tasks, scaling and evaluating their usage presents new challenges not addressed in previous frameworks. In particular, the ability of these models to produce useful outputs with little to no specialized training data ("zero-" or "few-shot" approaches), as well as the open-ended nature of their outputs, necessitate the development of new guidelines for robust reporting of clinical generative model research. In response to gaps in standards and best practices for the development of clinical AI tools identified by US Executive Order 141103 and several emerging national networks for clinical AI evaluation, we begin to formalize some of these guidelines by building on the original MI-CLAIM checklist. The new checklist, MI-CLAIM-GEN (Table 1), aims to address differences in training, evaluation, interpretability, and reproducibility of new generative models compared to non-generative ("predictive") AI models. This MI-CLAIM-GEN checklist also seeks to clarify cohort selection reporting with unstructured clinical data and adds additional items on alignment with ethical standards for clinical AI research. © 2024, CC BY.

关键词： Generative adversarial networks

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An open-source, automated, and cost-effective platform for COVID-19 diagnosis and rapid portable genomic surveillance using nanopore sequencing

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Scientific Reports 2023年第1期13卷 1-12页

作者： Ramos-Mandujano, Gerardo Grünberg, Raik Zhang, Yingzi Bi, Chongwei Guzmán-Vega, Francisco J. Shuaib, Muhammad Gorchakov, Rodion V. Xu, Jinna Tehseen, Muhammad Takahashi, Masateru Takahashi, Etsuko Dada, Ashraf Ahmad, Adeel Nazir Hamdan, Samir M. Pain, Arnab Arold, Stefan T. Li, Mo Stem Cell and Regeneration Laboratory Bioscience Program Biological and Environmental Science and Engineering Division (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Structural Biology and Engineering Computational Biology Research Center. Biological and Environmental Science and Engineering Division (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Pathogen Genomics Laboratory Bioscience Program Biological and Environmental Science and Engineering (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Health Safety and Environment Department King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Laboratory of DNA Replication and Recombination Bioscience Program Biological and Environmental Science and Engineering Division (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Department of Pathology and Laboratory Medicine King Faisal Specialist Hospital and Research Center Jeddah Saudi Arabia College of Medicine Al Faisal University Riyadh Saudi Arabia KAUST Health King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Bioengineering Program Biological and Environmental Science and Engineering Division (BESE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia

The COVID-19 pandemic, caused by SARS-CoV-2, has emphasized the necessity for scalable diagnostic workflows using locally produced reagents and basic laboratory equipment with minimal dependence on global supply chains. We introduce an open-source automated platform for high-throughput RNA extraction and pathogen diagnosis, which uses reagents almost entirely produced in-house. This platform integrates our methods for self-manufacturing magnetic nanoparticles and qRT-PCR reagents-both of which have received regulatory approval for clinical use–with an in-house, open-source robotic extraction protocol. It also incorporates our "Nanopore Sequencing of Isothermal Rapid Viral Amplification for Near Real-time Analysis" (NIRVANA) technology, designed for tracking SARS-CoV-2 mutations and variants. The platform exhibits high reproducibility and consistency without cross-contamination, and its limit of detection, sensitivity, and specificity are comparable to commercial assays. Automated NIRVANA effectively identifies circulating SARS-CoV-2 variants. Our in-house, cost-effective reagents, automated diagnostic workflows, and portable genomic surveillance strategies provide a scalable and rapid solution for COVID-19 diagnosis and variant tracking, essential for current and future pandemic responses. © 2023, The Author(s).

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Front Cover: Design, Synthesis, and Characterization of Novel Styryl Dyes as Fluorescent Probes for Tau Aggregate Detectionin Vitroand in Cells (Chem. Asian J. 6/2024)

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Chemistry – An Asian Journal 2024年第6期19卷

作者： Withsakorn Sangsuwan Kriangsak Faikhruea Kotchakorn Supabowornsathit Don Sangsopon Praewphan Ingrungruanglert Hathaichanok Chuntakaruk Napatsaporn Nuntavanotayan Kittiporn Nakprasit Nipan Israsena Thanyada Rungrotmongkol Pitak Chuawong Tirayut Vilaivan Chanat Aonbangkhen Center of Excellence in Natural Products Chemistry (CENP) Department of Chemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand Department of Chemistry and Center of Excellence for Innovation in Chemistry Faculty of Science Special Research Unit for Advanced Magnetic Resonance (AMR) Kasetsart University Bangkok 10900 Thailand These authors are contributed equally. Organic Synthesis Research Unit Department of Chemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand Stem Cell and Cell Therapy Research Unit and Department of Pharmacology Faculty of Medicine Chulalongkorn University Bangkok 10330 Thailand Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok 10330 Thailand Center of Excellence in Structural and Computational Biology Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand

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Towards Autonomous Synchrotron Fourier Transform Infrared Microscopy

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Microscopy and Microanalysis 2024年第SUPPLEMENT_1期30卷

作者： Zwart, Petrus Tas, Neslihan Noack, Marcus Holman, HoiYing Berkeley Synchrotron Infrared Structural Biology Program Lawrence Berkeley National Laboratory Berkeley California USA Molecular Biophysics and Integrated Bioimaging Division Lawrence Berkeley National Laboratory Berkeley California USA Center for Advanced Mathematics in Energy Research Applications Lawrence Berkeley National Laboratory Berkeley California USA Climate and Ecosystem Sciences Division Lawrence Berkeley National Laboratory Berkeley California USA Applied Mathematics & Computational Research Lawrence Berkeley National Laboratory Berkeley California USA

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Review: Precision medicine and driver mutations: computational methods, functional assays and conformational principles for interpreting cancer drivers (vol 15, e1006658, 2019)

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PLOS computational biology 2019年第6期15卷 e1006658-e1006658页

作者： Nussinov, Ruth Jang, Hyunbum Tsai, Chung-Jung Cheng, Feixiong Computational Structural Biology Section Basic Science Program Frederick National Laboratory for Cancer Research National Cancer Institute Frederick Maryland United States of America Department of Human Molecular Genetics and Biochemistry Sackler School of Medicine Tel Aviv University Tel Aviv Israel Genomic Medicine Institute Lerner Research Institute Cleveland Clinic Cleveland Ohio United States of America Department of Molecular Medicine Cleveland Clinic Lerner College of Medicine Case Western Reserve University Cleveland Ohio United States of America

At the root of the so-called precision medicine or precision oncology, which is our focus here, is the hypothesis that cancer treatment would be considerably better if therapies were guided by a tumor’s genomic alterations. This hypothesis has sparked major initiatives focusing on whole-genome and/or exome sequencing, creation of large databases, and developing tools for their statistical analyses—all aspiring to identify actionable alterations, and thus molecular targets, in a patient. At the center of the massive amount of collected sequence data is their interpretations that largely rest on statistical analysis and phenotypic observations. Statistics is vital, because it guides identification of cancer-driving alterations. However, statistics of mutations do not identify a change in protein conformation; therefore, it may not define sufficiently accurate actionable mutations, neglecting those that are rare. Among the many thematic overviews of precision oncology, this review innovates by further comprehensively including precision pharmacology, and within this framework, articulating its protein structural landscape and consequences to cellular signaling pathways. It provides the underlying physicochemical basis, thereby also opening the door to a broader community.

关键词： INDIVIDUALIZED medicine CANCER genetics MUTATION (biology) ONCOLOGY CANCER treatment EXOMES PHARMACOLOGY TUMOR genetics

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A Quinolin-8-Ol Sub-Millimolar Inhibitor of UGGT, the ER Glycoprotein Folding Quality Control Checkpoint

SSRN

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SSRN 2022年

作者： Guay, Kevin P. Ibba, Roberta Kiappes, J.L. De Benedictis, Maria Zeni, Ilaria Le Cornu, James David Hensen, Mario Chandran, Anu V. Kantsadi, Anastassia L. Caputo, Alessandro T. Capurro, Juan Ignacio Blanco Bayo, Yusupha Hill, Johan Christopher Hudson, Kieran Lia, Andrea Vasiljević, Snežana Modenutti, Carlos Pablo Withers, Stephen G. Martí, Marcelo Biasini, Emiliano Santino, Angelo Hebert, Daniel N. Zitzmann, Nicole Roversi, Pietro Department of Biochemistry and Molecular Biology and Program in Molecular and Cellular Biology University of Massachusetts AmherstMA United States Oxford Glycobiology Institute Biochemistry Department University of Oxford South Parks Road OxfordOX1 3QU United Kingdom Department of Medicine Surgery and Pharmacy University of Sassari Via Muroni 23A Sassari07100 Italy Institute of Sciences of Food Production C.N.R. Unit of Lecce via Monteroni LecceI-73100 Italy Department of Cellular Computational and Integrative Biology University of Trento Povo Trento38123 Italy Wellcome Centre for Cell Biology School of Biological Sciences University of Edinburgh Edinburgh United Kingdom Biomedical Manufacturing Commonwealth Scientific and Industrial Research Organisation 343 Royal Parade ParkvilleVIC3052 Australia Buenos Aires Argentina Buenos Aires Argentina Department of Biosciences University of Milano via Celoria 26 MilanoI-20133 Italy Department of Chemistry University of British Columbia 2036 Main Mall VancouverBCV6T 1Z1 Canada Dulbecco Telethon Institute University of Trento Povo Trento38123 Italy Institute of Agricultural Biology and Biotechnology IBBA-CNR Unit of Milano via Bassini 15 MilanoI-20133 Italy Leicester Institute of Chemical and Structural Biology Department of Molecular and Cell Biology University of Leicester Henry Wellcome Building Lancaster Road LeicesterLE1 7HR United Kingdom

Misfolded glycoprotein recognition and endoplasmic reticulum (ER) retention are mediated by the ER glycoprotein folding Quality Control (ERQC) checkpoint enzyme, UDP-Glucose glycoprotein glucosyltransferase (UGGT). UGGT modulation is a promising strategy for broad-spectrum antivirals, rescue-of-secretion therapy in rare disease caused by responsive mutations in glycoprotein genes, and many cancers, but to date no selective UGGT inhibitors are known. We carried out a fragment-based lead discovery screen via X-ray crystallography and discovered that the small molecule 5-[(morpholin4-yl)methyl]quinolin-8-ol (5M-8OH-Q) binds a CtUGGTGT24 ‘WY’ conserved surface motif conserved across UGGTs but not present in other GT24 family glycosyltransferases. 5M-8OH-Q has a 613 µM binding affinity for human UGGT1 in vitro as measured by saturation transfer difference NMR spectroscopy. In cellula, 5M-8OH-Q inhibits both human UGGT isoforms at concentrations higher than 750 µM. 5M-8OH-Q likely works as a competitive inhibitor, binding to the site of recognition of the first GlcNAc residue of the substrate N-glycan. A medicinal program based on 5M-8OH-Q will yield the next generation of UGGT inhibitors. © 2022, The Authors. All rights reserved.

关键词： X ray crystallography

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Inhibition of topoisomerase IIα and induction of DNA damage in cholangiocarcinoma cells by altholactone and its halogenated benzoate derivatives

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Biomedicine and Pharmacotherapy 2020年 127卷 110149-110149页

作者： Kitdumrongthum, Sarunya Reabroi, Somrudee Suksen, Kanoknetr Tuchinda, Patoomratana Munyoo, Bamroong Mahalapbutr, Panupong Rungrotmongkol, Thanyada Ounjai, Puey Chairoungdua, Arthit Toxicology Graduate Program Faculty of Science Mahidol University Bangkok Thailand Excellent Center for Drug Discovery (ECDD) Mahidol University Bangkok Thailand Department of Pharmacology Faculty of Science Mahidol University Bangkok Thailand Department of Physiology Faculty of Science Mahidol University Bangkok Thailand Department of Chemistry Faculty of Science Mahidol University Bangkok Thailand Structural and Computational Biology Research Unit Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok Thailand Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok Thailand Department of Biology Faculty of Science Mahidol University Bangkok Thailand

Topoisomerase IIα enzyme (Topo IIα) plays a critical function in DNA replication process and is considered to be a promising target of anti-cancer drugs. In the present study, we reported that the altholactone derivatives modified by adding a halogenated benzoate group showed greater inhibitory activity on Topo IIα enzyme in cell-free system concomitant with cytotoxicity against the CCA cell lines (KKU-M055 and KKU-M213) than those of the parent altholactone. However, the cytotoxic activities of four halogenated benzoate altholactone derivatives including iodo-, fluoro-, chloro-, and bromobenzoate derivatives (compound 1, 2, 3, and 4, respectively) were of equal potency. The fluorobenzoate derivative (compound 2) was chosen for investigating the underlying mechanism in CCA cells. Compound 2 arrested CCA cell cycle at sub G1 phase and induced apoptotic cell death. It markedly inhibited Topo IIα protein expression in both KKU-M055 and KKU-M213 cells, which was accompanied by DNA double-strand breaks demonstrated by an increase in phosphorylated H2A.X protein. Interestingly, KKU-M055 cells, which express higher Topo IIα mRNA compared to KKU-M213 cells, showed greater sensitivity to the compound, indicating the selectivity of the compound to Topo IIα enzyme. By computational docking analysis, the binding affinity of altholactone (-52.5 kcal/mol) and compound 2 (-56.7 kcal/mol) were similar to that of the Topo II poison salvicine (-53.7 kcal/mol). The aromatic moiety of both altholactones embedded in a hydrophobic pocket of Topo II ATPase domain. In addition, compound 2 induced the formation of linear DNA in Topo II-mediated cleavage assay. Collectively, our results demonstrate that the addition of fluorobenzoyl group to altholactone enhances potency and selectivity to inhibit type IIα topoisomerases. Atholactone and fluorobenzoate derivative act as Topo II cleavage complexes stabilizing compounds or Topo II poisons preferentially through binding at ATPase domain of Topo

关键词： Altholactone Cholangiocarcinoma Halogenated benzoate Topoisomerase IIα

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Role of Stem-like Cells in Chemotherapy Resistance and Relapse in Pediatric T Cell Acute Lymphoblastic Leukemia (T-ALL)

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Blood 2024年 144卷 314-314页

作者： Julia Costea Kerstin K. Rauwolf Pietro Zafferani Tobias Rausch Anna Mathioudaki Judith Zaugg Martin Schrappe Cornelia Eckert Gabriele Escherich Jean-Pierre Bourquin Beat Bornhauser Andreas E. Kulozik Jan O. Korbel Molecular Medicine Partnership Unit European Molecular Biology Laboratory (EMBL) and University Hospital Heidelberg Heidelberg Germany European Molecular Biology Laboratory (EMBL) Genome Biology Unit Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Department of Pediatric Oncology University Children's Hospital Zurich Zurich Switzerland Molecular Biosciences/Cancer Biology Program Heidelberg University and German Cancer Research Center (DKFZ) Heidelberg Germany European Molecular Biology Laboratory (EMBL) Genomics Core Facility Heidelberg Germany European Molecular Biology Laboratory (EMBL) Structural and Computational Biology Unit Heidelberg Germany University Hospital Schleswig-Holstein Department of Pediatrics Campus Kiel Kiel Germany Charité Universitätsmedizin Berlin Department of Hematology and Oncology Berlin Germany German Cancer Consortium (DKTK) Heidelberg Germany German Cancer Research Center (DKFZ) Heidelberg Germany University Medical Center Hamburg-Eppendorf Hamburg Germany Hopp-Kindertumorzentrum Heidelberg (KiTZ) Heidelberg Germany Clinical Cooperation Unit Pediatric Leukemia German Cancer Research Center (DKFZ) Heidelberg Germany Department of Pediatric Oncology Hematology and Immunology Heidelberg University Heidelberg Germany Genome Biology Unit European Molecular Biology Laboratory Heidelberg Germany Bridging Research Division on Mechanisms of Genomic Variation and Data Science German Cancer Research Center (DKFZ) Heidelberg Germany

T-ALL is an aggressive leukemia predominantly affecting adolescents and young adults. T-ALL relapses are characterized by chemotherapy resistance, cellular heterogeneity and dismal outcome (PMID: 33925883). While mutations, changes in gene expression and epigenetic dysregulations have been documented (PMID: 26294725; 27655895; 35585141; 23023710; 27623214), a unifying mechanism explaining the development of relapse remains unknown. To comprehensively investigate the heterogeneity of T-ALL and gain a better understanding of mechanisms driving T-ALL relapse, we conducted single-cell full-length RNA sequencing utilizing VASA-seq (PMID: 35760914). This analysis included 13 matched pediatric T-ALL patient-derived xenografts (PDX) samples obtained at initial diagnosis and relapse, generating the to date most comprehensive longitudinal single cell study in paired T-ALL samples, along with 5 non-relapsing PDX samples collected at initial diagnosis. Our dataset encompasses 11 TAL1- 3 TLX1/2-, 2 NKX2- and 2 HOXA-driven T-ALLs patients. While the predominant cell populations in these patients exhibit substantial interpatient heterogeneity, our data unveil a previously elusive subpopulation of T-ALL cells that converges at a gene-regulatory network shared between the majority of patients. This subpopulation is characterized by a molecular stem-like cell phenotype including immaturity in differentiation, persistence in the G1 cell cycling phase, metabolic quiescence and increased cell adhesion. Among the upregulated genes, we identified several surface markers previously recognized for their role in T-ALL relapse (PMID: 21487112; 20231613) or in stem-like cell maintenance in a murine T-ALL model (PMID: 29781813; 38553571), such as CD44, CD226, CD52, CD97, CD27 and ITGB7. We further identified significant enrichment of the anti-apoptotic proteins BCL-6, BCL-2 and MCL1. By contrast, NOTCH1 is among the downregulated genes in the stem-like cell population when compared to the other

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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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