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Quantitative electroencephalogram and machine learning to predict expired sevoflurane concentration in infants

Journal of Clinical Monitoring and Computing 2025年 1-16页

作者： Kumar, Rachit Skowno, Justin von Ungern-Sternberg, Britta S. Davidson, Andrew Xu, Ting Zhang, Jianmin Song, XingRong Zhang, Mazhong Zhao, Ping Liu, Huacheng Jiang, Yifei Zuo, Yunxia de Graaff, Jurgen C. Vutskits, Laszlo Olbrecht, Vanessa A. Szmuk, Peter Simpao, Allan F. Tsui, Fuchiang Fuchiang Rich Pratap, Jayant Jayant Nick Padiyath, Asif Nelson, Olivia Kurth, Charles D. Yuan, Ian Alex, Gijo Efune, Proshad Saynhalath, Rita Hu, Paula Lang, Shih-Shan Huh, Jimmy W. Roden, Emilie van Hoorn, Camille Bhuju, Abhusani Du, Bin Chen, Panpan Ding, Mengmeng Zheng, Jijian Lei, DongXu Gao, ZhengZheng Sommerfield, David Sheppard, Suzette Menezes, Minal Genomics and Computational Biology Graduate Group at the Perelman School of Medicine at the University of Pennsylvania Philadelphia PA United States Department of Anaesthesia The Children’s Hospital at Westmead Sydney Australia School of Child and Adolescent Health The University of Sydney Sydney Australia Department of Anaesthesia and Pain Medicine Perth Children’s Hospital Perth Australia Perioperative Care Program Perioperative Medicine Team Telethon Kids Institute Perth Australia Institute for Paediatric Perioperative Excellence The University of Western Australia Perth Australia Division of Emergency Medicine Anaesthesia and Pain Medicine Medical School The University of Western Australia Perth Australia Department of Anaesthesia Royal Children’s Hospital Melbourne Parkville Australia Departments of Paediatrics and Critical Care University of Melbourne Parkville Australia Anaesthesia Research and Melbourne Children’s Trials Centre Murdoch Children’s Research Institute Melbourne Australia President of Sichuan Provincial Jiaotong Hospital The Affiliated Hospital of Sichuan Academy of Medical Science & Sichuan Provincial People’s Hospital Sichuan China Department of Anesthesiology Beijing Children’s Hospital Capital Medical University National for Children’s Health Beijing China Department of Anesthesiology. Guangzhou Women and Children’s Medical Center Guangzhou China Department of Anesthesiology Shanghai Children’s Medical Center Shanghai China Shanghai Jiao Tong University School of Medicine Shanghai China Shanghai Children’s Medical Center Guizhou Hospital Shanghai China Department of Anesthesiology at the Shengjing Hospital of China Medical University Shenyang China Department of Anesthesiology and Perioperative Medicine The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University Wenzhou China Key Laboratory of Pediatric Anesthesiology Ministry of Education Wenzhou Medical University Wenzhou China Key Laboratory of

Processed electroencephalography (EEG) indices used to guide anesthetic dosing in adults are not validated in young infants. Raw EEG can be processed mathematically, yielding quantitative EEG parameters (qEEG). We hypothesized that machine learning combined with qEEG can accurately classify expired sevoflurane concentrations in young infants. Knowledge from this may contribute to development of future infant-specific EEG algorithms. Frontal EEG collected from infants ≤ 3 months were time-matched as one-minute epochs to expired sevoflurane (eSevo). Fifteen qEEG parameters were extracted from each epoch and eight machine learning models combined the qEEG to classify each epoch into one of four eSevo levels (%): 0.1–1.0, 1.0–2.1, 2.1–2.9, and > 2.9. 64 epochs formed the post hoc SHAP dataset to determine the qEEG that contributed most to the model. The remaining epochs were randomly split 50 times into 80/20 training/testing sets. Accuracy and F1-score determined model performance. 42 infants provided 4574 epochs. The top classifiers K-nearest neighbors, default multi-layer perceptron, and support vector machine achieved 67.5–68.7% accuracy. Burst suppression ratio and entropy β were the top contributors to the models. Post hoc analysis performed without burst suppression ratio yielded similar prediction performance. In young infants, machine learning applied to qEEG predicted eSevo levels with moderate success. Burst suppression ratio, the most important contributor, represented an efficient EEG feature that encapsulated underlying EEG changes seen on other qEEG features. These results provided insight into EEG parameter selection and optimal machine learning models used for future development of infant-specific EEG algorithms. © The Author(s) 2025.

关键词： Infant electroencephalogram EEG Machine learning Pediatric EEG anesthesia Quantitative EEG anesthesia SHAP EEG analysis

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Deconvoluting clonal and cellular architecture in IDH -mutant acute myeloid leukemia

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Cell Stem Cell 2025年

作者： Maria Sirenko Soobeom Lee Zhengxi Sun Ronan Chaligne Sanam Loghavi Georgios Asimomitis Charlotte K. Brierley Elsa Bernard Sheng F. Cai Robert M. Myers Bettina Nadorp Junya Sango Morgan Lallo Max F. Levine Dylan Domenico Juan E. Arango Ossa Juan S. Medina-Martinez Kamal Menghrajani Audrey Lasry Alice S. Mims Elli Papaemmanuil Department of Pathology New York University Grossman School of Medicine New York NY USA Laura and Isaac Perlmutter Cancer Center New York University Grossman School of Medicine New York NY USA Louis V. Gerstner Jr. Graduate School of Biomedical Sciences Memorial Sloan Kettering Cancer Center New York NY USA Computational Oncology Service Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Hematopathology The University of Texas MD Anderson Cancer Center Houston TX USA Department of Biology New York University New York NY USA Program for Computational and Systems Biology Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New York NY 10065 USA Biomedical Systems Laboratory Department of Mechanical Engineering National Technical University of Athens Athens Greece Medical Research Council Weatherall Institute of Molecular Medicine University of Oxford Oxford UK NIHR Biomedical Research Centre University of Oxford Oxford UK Leukemia Service Department of Medicine Center for Hematologic Malignancies Memorial Sloan Kettering Cancer Center New York NY USA Human Oncology and Pathogenesis Program Memorial Sloan Kettering Cancer Center New York NY USA Tri-Institutional MD-PhD Program Weill Cornell Medicine Rockefeller University Memorial Sloan Kettering Cancer Center New York NY USA Division of Precision Medicine Department of Medicine New York University School of Medicine New York NY USA Department of Oncological Sciences Tisch Cancer Institute Center for Advancement of Blood Cancer Therapies Icahn School of Medicine at Mount Sinai New York NY USA Vilcek Institute of Graduate Biomedical Sciences NYU Grossman School of Medicine New York NY USA Clara D. Bloomfield Center for Leukemia Outcomes Research The Ohio State University Comprehensive Cancer Center Columbus OH USA Division of Hematology Department of Internal Medicine The Ohio State University Comprehensive Cancer Cen

Isocitrate dehydrogenase 1/2 ( IDH ) mutations are early initiating events in acute myeloid leukemia (AML). The complex clonal architecture and cellular heterogeneity in IDH- mutant AML underlies the heterogeneous clinical presentation and outcomes. Integrating single-cell genotyping and transcriptomics, we demonstrate a stem-like and inflammatory phenotype of IDH -mutant AML and identify clone-specific programs associated with NPM1 , NRAS , and SRSF2 co-mutations. Furthermore, these clones had distinct responses to treatment with combination IDH inhibitors and chemotherapy, including elimination, reconstitution of myeloid differentiation, or retention within progenitor populations. At relapse after IDH inhibitor monotherapy, we identify upregulated stemness, inflammation, mitochondrial metabolism, and anti-apoptotic factors, as well as downregulated major histocompatibility complex (MHC) class II antigen presentation. At the pre-leukemic stage, we observe upregulation of IDH2 -associated pathways, including inflammation. We deliver a detailed phenotyping of IDH -mutant AML and a framework for dissecting contributions of recurrently mutated genes in AML at diagnosis and following therapy, with implications for precision medicine.

关键词： IDH inhibitors IDH1/2 mutations acute myeloid leukemia cellular hierarchy clonal architecture clonal evolution relapse single-cell transcriptomics targeted therapy treatment resistance

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Derivatives (halogen, nitro and amino) of 8-hydroxyquinoline with highly potent antimicrobial and antioxidant activities

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Biochemistry and Biophysics Reports 2016年 6卷 135-141页

作者： Cherdtrakulkiat, Rungrot Boonpangrak, Somchai Sinthupoom, Nujarin Prachayasittikul, Supaluk Ruchirawat, Somsak Prachayasittikul, Virapong Department of Clinical Microbiology and Applied Technology Faculty of Medical Technology Mahidol University Bangkok 10700 Thailand Center for Innovation Development and Technology Transfer Faculty of Medical Technology Mahidol University Bangkok 10700 Thailand Center of Data Mining and Biomedical Informatics Faculty of Medical Technology Mahidol University Bangkok 10700 Thailand Laboratory of Medicinal Chemistry Chulabhorn Research Institute and Program in Chemical Biology Chulabhorn Graduate Institute Bangkok 10210 Thailand Center of Excellence on Environmental Health and Toxicology Commission on Higher Education (CHE) Ministry of Education Thailand

8-Hydroxyquinoline (8HQ) compounds have been reported to possess diverse bioactivities. In recent years, drug repositioning has gained considerable attention in drug discovery and development. Herein, 8HQ (1) and its derivatives (2-9) bearing various substituents (amino, nitro, cyano and halogen) were investigated for their antimicrobial against 27 microorganisms (agar dilution method) and antioxidant (DPPH method) activities. The parent 8HQ (1) exerted a highly potent antimicrobial activity against Gram-positive bacteria including diploid fungi and yeast with MIC values in the range of 3.44-13.78 μM. Moreover, the halogenated 8HQ, especially 7-bromo-8HQ (4) and clioquinol (6), displayed a high antigrowth activity against Gram-negative bacteria compared with the parent compound (1). Apparently, the derivatives with a relatively high safely index, e.g., nitroxoline (2), exhibited strong antibacterial activity against Aeromonas hydrophila (MIC=5.26 μM) and selectively inhibited the growth of P. aeruginosa with the MIC value of 84.14 μM;cloxyquin (3) showed a strong activity against L. monocytogenes and P. shigelloides with MIC values of 5.57 and 11.14 μM, respectively. Most compounds displayed an antioxidant activity. Specifically, 5-amino-8HQ (8) was shown to be the most potent antioxidant (IC₅₀=8.70 μM) compared with the positive control (α-tocopherol) with IC₅₀ of 13.47 μM. The findings reveal that 8HQ derivatives are potential candidates to be further developed as antimicrobial and antioxidant agents. © 2016 The Authors.

关键词： 8-Hydroxyquinoline Antimicrobial Antioxidant Clioquinol Cloxyquin Nitroxoline

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An integrative ChIP-chip and gene expression profiling to model SMAD regulatory modules

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BMC Systems biology 2009年第1期3卷 1-20页

作者： Qin, Huaxia Chan, Michael W.Y. Liyanarachchi, Sandya Balch, Curtis Potter, Dustin Souriraj, Irene J. Cheng, Alfred S.L. Agosto-Perez, Francisco J. Nikonova, Elena V. Yan, Pearlly S. Lin, Huey-Jen Nephew, Kenneth P. Saltz, Joel H. Showe, Louise C. Huang, Tim H.M. Davuluri, Ramana V. Human Cancer Genetics Program Department of Molecular Virology Immunology and Medical Genetics The Ohio State University Columbus OH 43210 United States Division of Medical Technology School of Allied Medical Professions The Ohio State University Columbus OH 43210 United States Department of Biomedical Informatics The Ohio State University Columbus OH 43210 United States Department of Medical Sciences Indiana University School of Medicine Bloomington IN 47405 United States Department of Life Science Institute of Molecular Biology National Chung Cheng University Min-Hsiung Chia-Yi 621 Taiwan Institute of Digestive Disease The Chinese University of Hong Kong Hong Kong Hong Kong Center for Systems and Computational Biology Molecular and Cellular Oncogenesis Program The Wistar Institute Philadelphia PA United States

Background: The TGF-β/SMAD pathway is part of a broader signaling network in which crosstalk between pathways occurs. While the molecular mechanisms of TGF-β/SMAD signaling pathway have been studied in detail, the global networks downstream of SMAD remain largely unknown. The regulatory effect of SMAD complex likely depends on transcriptional modules, in which the SMAD binding elements and partner transcription factor binding sites (SMAD modules) are present in specific context. Results: To address this question and develop a computational model for SMAD modules, we simultaneously performed chromatin immunoprecipitation followed by microarray analysis (ChIP-chip) and mRNA expression profiling to identify TGF-β/SMAD regulated and synchronously coexpressed gene sets in ovarian surface epithelium. Intersecting the ChIP-chip and gene expression data yielded 150 direct targets, of which 141 were grouped into 3 co-expressed gene sets (sustained up-regulated, transient up-regulated and down-regulated), based on their temporal changes in expression after TGF-β activation. We developed a data-mining method driven by the Random Forest algorithm to model SMAD transcriptional modules in the target sequences. The predicted SMAD modules contain SMAD binding element and up to 2 of 7 other transcription factor binding sites (E2F, P53, LEF1, ELK1, COUPTF, PAX4 and DR1). Conclusion: Together, the computational results further the understanding of the interactions between SMAD and other transcription factors at specific target promoters, and provide the basis for more targeted experimental verification of the co-regulatory modules. © 2009 Qin et al;licensee BioMed Central Ltd.

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Microarray analysis of genes associated with cell surface NIS protein levels in breast cancer

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BMC Research Notes 2011年第1期4卷 1-14页

作者： Beyer, Sasha J. Zhang, Xiaoli Jimenez, Rafael E. Lee, Mei-Ling T. Richardson, Andrea L. Huang, Kun Jhiang, Sissy M. Integrated Biomedical Sciences Graduate Program Ohio State University Columbus OH 43210 United States Department of Physiology and Cell Biology Ohio State University Columbus OH 43210 United States Center for Biostatistics Ohio State University Columbus OH 43210 United States Department of Anatomic Pathology Mayo Clinic Rochester MN 55901 United States Department of Epidemiology and Biostatistics University of Maryland College Park MD 20742 United States Department of Pathology Brigham and Women's Hospital Harvard Medical School Boston MA 02115 United States Department of Biomedical Informatics Ohio State University Columbus OH 43210 United States

Background: Na+/I- symporter (NIS)-mediated iodide uptake allows radioiodine therapy for thyroid cancer. NIS is also expressed in breast tumors, raising potential for radionuclide therapy of breast cancer. However, NI... 详细信息

Background: Na⁺/I^- symporter (NIS)-mediated iodide uptake allows radioiodine therapy for thyroid cancer. NIS is also expressed in breast tumors, raising potential for radionuclide therapy of breast cancer. However, NIS expression in most breast cancers is low and may not be sufficient for radionuclide therapy. We aimed to identify biomarkers associated with NIS expression such that mechanisms underlying NIS modulation in human breast tumors may be elucidated. Methods. Published oligonucleotide microarray data within the National Center for Biotechnology Information Gene Expression Omnibus database were analyzed to identify gene expression tightly correlated with NIS mRNA level among human breast tumors. NIS immunostaining was performed in a tissue microarray composed of 28 human breast tumors which had corresponding oligonucleotide microarray data available for each tumor such that gene expression associated with cell surface NIS protein level could be identified. Results and Discussion. NIS mRNA levels do not vary among breast tumors or when compared to normal breast tissues when detected by Affymetrix oligonucleotide microarray platforms. Cell surface NIS protein levels are much more variable than their corresponding NIS mRNA levels. Despite a limited number of breast tumors examined, our analysis identified cysteinyl-tRNA synthetase as a biomarker that is highly associated with cell surface NIS protein levels in the ER-positive breast cancer subtype. Conclusions: Further investigation on genes associated with cell surface NIS protein levels within each breast cancer molecular subtype may lead to novel targets for selectively increasing NIS expression/function in a subset of breast cancers patients. © 2011 Jhiang et al;licensee BioMed Central Ltd.

关键词： Breast Cancer Breast Tumor Oligonucleotide Microarray Breast Cancer Molecular Subtype Cars Protein

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Simultaneous Identification for Synergistic Transcription Factors and their Transcription Factor Binding Sites

Simultaneous Identification for Synergistic Transcription Fa...

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International Conference on Complex, Intelligent and Software Intensive Systems, CISIS

作者： Zing Tsung-Yeh Tsai Grace Tzu-Wei Huang Huai-Kuang Tsai Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang-Ming University Taiwan Joint CMU-Pitt PhD Program in Computational Biology University of Pittsburgh Carnegie Mellon University Pittsburgh USA Computer Science and Engineering National Taiwan Ocean University Keelung Taiwan

Identifying transcription factor binding sites (TFBSs) is crucial for understanding the mechanism of transcriptional regulation. It is known that transcription factors (TFs) often cooperate to regulate genes. While traditional approaches can be used to discover binding motifs of a group of co-regulated genes, they often fail to accurately assign motifs to the corresponding TFs. Here, we consider two TFs together to infer their TFBSs and their synergistic relationship simultaneously. The basic idea is that if two TFs interact, their TFBSs, if distinct, would be conserved across species and coincided in the promoter regions of the genes they co-regulated. Applying our method to Saccharomyces cerevisiae chromatin immunoprecipitation data, we predicted 110 TF pairs with statistically significant motif assignments. A majority of these TF pairs have literature support to be synergistic, and the designated motifs to TFs match well with their known consensus. We further examined the synergism of predicted TF pairs in seven experimental conditions using ANOVA, and identified significant interactions.

关键词： Bioinformatics Analysis of variance DNA Databases Gene expression Proteins Stress

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Modeling of solitary pulmonary nodules in PET/CT images using Monte Carlo methods

Modeling of solitary pulmonary nodules in PET/CT images usin...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： George Tzanoukos Anastasios Gaitanis Alexandros Georgakopoulos Achilleas Chatziioannou Sofia Chatziioannou George Spyrou post-graduate Program: “Information Technologies in Medicine and Biology” (ITMB) National Center for Scientific Research (NCSR) “Demokritos” Athens CO Greece Department of Biomedical Technology Biomedical Research Foundation of the Academy of Athens (BRFAA) Athens Greece Nuclear Medicine Department Biomedical Research Foundation of the Academy of Athens (BRFAA) Athens Greece First Radiology Department University of Athens Athens Greece Biomedical Informatics Unit Biomedical Research Foundation of the Academy of Athens (BRFAA) Athens Greece

The assessment of solitary pulmonary nodules (SPN) is a very difficult task in PET imaging due to adjacent normal structures. Physicians and computational systems as well, would gain benefit if trained to a large number of SPN cases with controlled topological and morphological characteristics. Our objective was to develop a method for the modeling of the solitary pulmonary nodules in CT and PET images. The modeling of SPN was implemented by Monte Carlo methods taking into consideration morphological characteristics, internal features and Standardized Uptake Value (SUV) activity distribution. For the validation of the model, an observer study from three independent medical experts was performed. The reviewers characterized the lesions as simulated or real and finally they classified them as benign or malignant. According to the results of the human observer study a significant percentage of simulated images could not be differentiated from real ones and the simulated class (benign or malignant) was consistent with the observers' classification.

关键词： Positron emission tomography Lesions Computed tomography Cancer Observers computational modeling Monte Carlo methods

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Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time

arXiv

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arXiv 2021年

作者： Zhu, Feng Sedler, Andrew R. Grier, Harrison A. Ahad, Nauman Davenport, Mark A. Kaufman, Matthew T. Giovannucci, Andrea Pandarinath, Chethan Neuroscience Graduate Program Emory University Center for Machine Learning Georgia Tech Computational Neuroscience Graduate Program The University of Chicago School of Electrical and Computer Engineering Georgia Tech Dept. of Organismal Biology and Anatomy The University of Chicago Neuroscience Institute The University of Chicago Joint Dept. of Biomedical Engineering UNC Chapel Hill NC State University UNC Chapel Hill Neuroscience Center Coulter Dept. of Biomedical Engineering Emory University Georgia Tech Dept. of Neurosurgery Emory University

Modern neural interfaces allow access to the activity of up to a million neurons within brain circuits. However, bandwidth limits often create a trade-off between greater spatial sampling (more channels or pixels) and the temporal frequency of sampling. Here we demonstrate that it is possible to obtain spatio-temporal super-resolution in neuronal time series by exploiting relationships among neurons, embedded in latent low-dimensional population dynamics. Our novel neural network training strategy, selective backpropagation through time (SBTT), enables learning of deep generative models of latent dynamics from data in which the set of observed variables changes at each time step. The resulting models are able to infer activity for missing samples by combining observations with learned latent dynamics. We test SBTT applied to sequential autoencoders and demonstrate more efficient and higher-fidelity characterization of neural population dynamics in electrophysiological and calcium imaging data. In electrophysiology, SBTT enables accurate inference of neuronal population dynamics with lower interface bandwidths, providing an avenue to significant power savings for implanted neuroelectronic interfaces. In applications to two-photon calcium imaging, SBTT accurately uncovers high-frequency temporal structure underlying neural population activity, substantially outperforming the current state-of-the-art. Finally, we demonstrate that performance could be further improved by using limited, high-bandwidth sampling to pretrain dynamics models, and then using SBTT to adapt these models for sparsely-sampled data. © 2021, CC BY-NC-SA.

关键词： Neurons

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A Multi-phase Deep Learning Approach for Predicting HCC Response to TACE using Complete Computed Tomography

A Multi-phase Deep Learning Approach for Predicting HCC Resp...

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Data Science and Engineering in Healthcare, Medicine and biology, IEEE EMBS Special Topic Conference on

作者： Shun Cheng Chang Chien Fu Liu Marta Misztal Yi Hsien Hsieh Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taiwan School of Electronic Engineering and Computer Science Queen Mary University of London UK Department of Electrical Engineering NTU Taiwan Center for Computational and Systems Biology and Center for Biotechnology NTU Taiwan Smart Medicine and Health Informatics Program NTU Taiwan

In this research study, we compare the predictive performance of two advanced deep learning-based models in order to provide a solution to TACE (Transarterial Chemoembolization) response prediction in HCC (Hepatocellular Carcinoma) patients. Using entire abdominal CT scans enabled a broader perspective available for the model, eliminating the need for segmentation during the preprocessing. Making use of both single-phase and multi-phase CT imaging, we have used DenseNet121 and have obtained an accuracy of 80% for the multi-phase *** Relevance: The ability to predict the effectiveness of TACE treatment prior to its administration makes it possible to provide a better decision-making aid for physicians and patients.

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Experimental and real-world evidence supporting the computational repurposing of bumetanide for APOE4-related Alzheimer's disease ( Oct, 10.1038/s43587-021-00122-7, 2021)

NATURE AGING

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NATURE AGING 2021年第12期1卷 1202-1202页

作者： Taubes, Alice Nova, Phil Zalocusky, Kelly A. Kosti, Idit Bicak, Mesude Zilberter, Misha Y. Hao, Yanxia Yoon, Seo Yeon Oskotsky, Tomiko Pineda, Silvia Chen, Bin Jones, Emily Aery A. Choudhary, Krishna Grone, Brian Balestra, Maureen E. Chaudhry, Fayzan Paranjpe, Ishan De Freitas, Jessica Koutsodendris, Nicole Chen, Nuo Wang, Celine Chang, William An, Alice Glicksberg, Benjamin S. Sirota, Marina Huang, Yadong Gladstone Institute of Neurological Disease Gladstone Institutes San Francisco CA USA Biomedical Sciences Graduate Program University of California San Francisco CA USA Gladstone Center for Translational Advancement Gladstone Institutes San Francisco CA USA Department of Neurology University of California San Francisco CA USA Bakar Computational Health Sciences Institute University of California San Francisco CA USA Department of Pediatrics University of California San Francisco CA USA Hasso Plattner Institute for Digital Health at Mount Sinai Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York NY USA Department of Surgery University of California San Francisco CA USA Gladstone Institute of Data Science and Biotechnology Gladstone Institutes San Francisco CA USA Development and Stem Cell Biology Graduate Program University of California San Francisco CA USA Department of Pathology University of California San Francisco CA USA

The evident genetic, pathological and clinical heterogeneity of Alzheimer’s disease (AD) poses challenges for traditional drug development. We conducted a computational drug-repurposing screen for drugs to treat apolipoprotein E4 (APOE4)-related AD. We first established APOE genotype-dependent transcriptomic signatures of AD by analyzing publicly available human brain databases. We then queried these signatures against the Connectivity Map database, which contains transcriptomic perturbations of more than 1,300 drugs, to identify those that best reverse APOE genotype-specific AD signatures. Bumetanide was identified as a top drug for APOE4-related AD. Treatment of APOE4-knock-in mice without or with amyloid β (Aβ) accumulation using bumetanide rescued electrophysiological, pathological or cognitive deficits. Single-nucleus RNA sequencing revealed transcriptomic reversal of AD signatures in specific cell types in these mice, a finding confirmed in APOE4 induced pluripotent stem cell (iPSC)-derived neurons. In humans, bumetanide exposure was associated with a significantly lower AD prevalence in individuals over the age of 65 years in two electronic health record databases, suggesting the effectiveness of bumetanide in preventing AD.

关键词： Ageing Alzheimer's disease computational neuroscience Life Sciences general

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