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@ARTICLE{Duran:287620,
      author       = {I. Duran and J. Pombo and B. Sun and S. U. Gallage$^*$ and
                      H. Kudo and D. McHugh and L. Bousset and J. E. Barragan
                      Avila$^*$ and R. Forlano and P. Manousou and M.
                      Heikenwalder$^*$ and D. J. Withers and S. Vernia and R. D.
                      Goldin and J. Gil},
      title        = {{D}etection of senescence using machine learning algorithms
                      based on nuclear features.},
      journal      = {Nature Communications},
      volume       = {15},
      number       = {1},
      issn         = {2041-1723},
      address      = {[London]},
      publisher    = {Nature Publishing Group UK},
      reportid     = {DKFZ-2024-00274},
      pages        = {1041},
      year         = {2024},
      abstract     = {Cellular senescence is a stress response with broad
                      pathophysiological implications. Senotherapies can induce
                      senescence to treat cancer or eliminate senescent cells to
                      ameliorate ageing and age-related pathologies. However, the
                      success of senotherapies is limited by the lack of reliable
                      ways to identify senescence. Here, we use nuclear morphology
                      features of senescent cells to devise machine-learning
                      classifiers that accurately predict senescence induced by
                      diverse stressors in different cell types and tissues. As a
                      proof-of-principle, we use these senescence classifiers to
                      characterise senolytics and to screen for drugs that
                      selectively induce senescence in cancer cells but not normal
                      cells. Moreover, a tissue senescence score served to assess
                      the efficacy of senolytic drugs and identified senescence in
                      mouse models of liver cancer initiation, ageing, and
                      fibrosis, and in patients with fatty liver disease. Thus,
                      senescence classifiers can help to detect pathophysiological
                      senescence and to discover and validate potential
                      senotherapies.},
      cin          = {F180 / D440},
      ddc          = {500},
      cid          = {I:(DE-He78)F180-20160331 / I:(DE-He78)D440-20160331},
      pnm          = {314 - Immunologie und Krebs (POF4-314)},
      pid          = {G:(DE-HGF)POF4-314},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:38310113},
      doi          = {10.1038/s41467-024-45421-w},
      url          = {https://inrepo02.dkfz.de/record/287620},
}