Anti-senescent drug screening by deep learning-based morphology senescence scoring

Cellular senescence is a hallmark of ageing and is important for the pathogenesis of ageing-related diseases. Here, the authors develop a morphology-based deep learning system to identify senescent cells and a quantitative scoring system to evaluate the state of endothelial cells to evaluate the eff...

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Autores principales:	Dai Kusumoto, Tomohisa Seki, Hiromune Sawada, Akira Kunitomi, Toshiomi Katsuki, Mai Kimura, Shogo Ito, Jin Komuro, Hisayuki Hashimoto, Keiichi Fukuda, Shinsuke Yuasa
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/93a65072350a4334a3d4dc48ff43d47b
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spelling	oai:doaj.org-article:93a65072350a4334a3d4dc48ff43d47b2021-12-02T15:22:48ZAnti-senescent drug screening by deep learning-based morphology senescence scoring10.1038/s41467-020-20213-02041-1723https://doaj.org/article/93a65072350a4334a3d4dc48ff43d47b2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-20213-0https://doaj.org/toc/2041-1723Cellular senescence is a hallmark of ageing and is important for the pathogenesis of ageing-related diseases. Here, the authors develop a morphology-based deep learning system to identify senescent cells and a quantitative scoring system to evaluate the state of endothelial cells to evaluate the effects of anti-senescent reagents.Dai KusumotoTomohisa SekiHiromune SawadaAkira KunitomiToshiomi KatsukiMai KimuraShogo ItoJin KomuroHisayuki HashimotoKeiichi FukudaShinsuke YuasaNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Dai Kusumoto Tomohisa Seki Hiromune Sawada Akira Kunitomi Toshiomi Katsuki Mai Kimura Shogo Ito Jin Komuro Hisayuki Hashimoto Keiichi Fukuda Shinsuke Yuasa Anti-senescent drug screening by deep learning-based morphology senescence scoring
description	Cellular senescence is a hallmark of ageing and is important for the pathogenesis of ageing-related diseases. Here, the authors develop a morphology-based deep learning system to identify senescent cells and a quantitative scoring system to evaluate the state of endothelial cells to evaluate the effects of anti-senescent reagents.
format	article
author	Dai Kusumoto Tomohisa Seki Hiromune Sawada Akira Kunitomi Toshiomi Katsuki Mai Kimura Shogo Ito Jin Komuro Hisayuki Hashimoto Keiichi Fukuda Shinsuke Yuasa
author_facet	Dai Kusumoto Tomohisa Seki Hiromune Sawada Akira Kunitomi Toshiomi Katsuki Mai Kimura Shogo Ito Jin Komuro Hisayuki Hashimoto Keiichi Fukuda Shinsuke Yuasa
author_sort	Dai Kusumoto
title	Anti-senescent drug screening by deep learning-based morphology senescence scoring
title_short	Anti-senescent drug screening by deep learning-based morphology senescence scoring
title_full	Anti-senescent drug screening by deep learning-based morphology senescence scoring
title_fullStr	Anti-senescent drug screening by deep learning-based morphology senescence scoring
title_full_unstemmed	Anti-senescent drug screening by deep learning-based morphology senescence scoring
title_sort	anti-senescent drug screening by deep learning-based morphology senescence scoring
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/93a65072350a4334a3d4dc48ff43d47b
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Anti-senescent drug screening by deep learning-based morphology senescence scoring

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