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...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-20213-0 |
| _version_ | 1818997452567478272 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-20T21:45:52Z |
| format | Article |
| id | doaj.art-93a65072350a4334a3d4dc48ff43d47b |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-12-20T21:45:52Z |
| publishDate | 2021-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-93a65072350a4334a3d4dc48ff43d47b2022-12-21T19:25:40ZengNature PortfolioNature Communications2041-17232021-01-0112111010.1038/s41467-020-20213-0Anti-senescent drug screening by deep learning-based morphology senescence scoringDai Kusumoto0Tomohisa Seki1Hiromune Sawada2Akira Kunitomi3Toshiomi Katsuki4Mai Kimura5Shogo Ito6Jin Komuro7Hisayuki Hashimoto8Keiichi Fukuda9Shinsuke Yuasa10Department of Cardiology, Keio University School of MedicineDepartment of Healthcare Information Management, The University of Tokyo HospitalDepartment of Cardiology, Keio University School of MedicineCenter for iPS Cell Research and Application, Kyoto UniversityDepartment of Cardiology, Keio University School of MedicineDepartment of Cardiology, Keio University School of MedicineDepartment of Cardiology, Keio University School of MedicineDepartment of Cardiology, Keio University School of MedicineDepartment of Cardiology, Keio University School of MedicineDepartment of Cardiology, Keio University School of MedicineDepartment of Cardiology, Keio University School of MedicineCellular 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.https://doi.org/10.1038/s41467-020-20213-0 |
| spellingShingle | 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 Nature Communications |
| title | 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_short | Anti-senescent drug screening by deep learning-based morphology senescence scoring |
| title_sort | anti senescent drug screening by deep learning based morphology senescence scoring |
| url | https://doi.org/10.1038/s41467-020-20213-0 |
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