Anish Karpurapu BS , Helen A. Williams BS , Paige DeBenedittis PhD , Caroline E. Baker BS , Simiao Ren PhD , Michael C. Thomas BS , Anneka J. Beard MS , Garth W. Devlin BS , Josephine Harrington MD , Lauren E. Parker BS , Abigail K. Smith , Boyla Mainsah PhD , Michelle Mendiola Pla MD , Aravind Asokan PhD , Dawn E. Bowles PhD , Edwin Iversen PhD , Leslie Collins PhD , Ravi Karra MD, MHS
{"title":"Deep Learning Resolves Myovascular Dynamics in the Failing Human Heart","authors":"Anish Karpurapu BS , Helen A. Williams BS , Paige DeBenedittis PhD , Caroline E. Baker BS , Simiao Ren PhD , Michael C. Thomas BS , Anneka J. Beard MS , Garth W. Devlin BS , Josephine Harrington MD , Lauren E. Parker BS , Abigail K. Smith , Boyla Mainsah PhD , Michelle Mendiola Pla MD , Aravind Asokan PhD , Dawn E. Bowles PhD , Edwin Iversen PhD , Leslie Collins PhD , Ravi Karra MD, MHS","doi":"10.1016/j.jacbts.2024.02.007","DOIUrl":null,"url":null,"abstract":"<div><p>The adult mammalian heart harbors minute levels of cycling cardiomyocytes (CMs). Large numbers of images are needed to accurately quantify cycling events using microscopy-based methods. CardioCount is a new deep learning–based pipeline to rigorously score nuclei in microscopic images. When applied to a repository of 368,434 human microscopic images, we found evidence of coupled growth between CMs and cardiac endothelial cells in the adult human heart. Additionally, we found that vascular rarefaction and CM hypertrophy are interrelated in end-stage heart failure. CardioCount is available for use via GitHub and via Google Colab for users with minimal machine learning experience.</p></div>","PeriodicalId":14831,"journal":{"name":"JACC: Basic to Translational Science","volume":"9 5","pages":"Pages 674-686"},"PeriodicalIF":8.4000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2452302X24000548/pdfft?md5=978db936761ec0f6df1d5fdff07202dc&pid=1-s2.0-S2452302X24000548-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JACC: Basic to Translational Science","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2452302X24000548","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
The adult mammalian heart harbors minute levels of cycling cardiomyocytes (CMs). Large numbers of images are needed to accurately quantify cycling events using microscopy-based methods. CardioCount is a new deep learning–based pipeline to rigorously score nuclei in microscopic images. When applied to a repository of 368,434 human microscopic images, we found evidence of coupled growth between CMs and cardiac endothelial cells in the adult human heart. Additionally, we found that vascular rarefaction and CM hypertrophy are interrelated in end-stage heart failure. CardioCount is available for use via GitHub and via Google Colab for users with minimal machine learning experience.
期刊介绍：
JACC: Basic to Translational Science is an open access journal that is part of the renowned Journal of the American College of Cardiology (JACC). It focuses on advancing the field of Translational Cardiovascular Medicine and aims to accelerate the translation of new scientific discoveries into therapies that improve outcomes for patients with or at risk for Cardiovascular Disease. The journal covers thematic areas such as pre-clinical research, clinical trials, personalized medicine, novel drugs, devices, and biologics, proteomics, genomics, and metabolomics, as well as early phase clinical trial methodology.
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