ArticleNature communications2024
Detection of senescence using machine learning algorithms based on nuclear features.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
55 citing papers in PubMed, 63 citations in OpenAlex.
- Genomic, epigenomic and transcriptomic regulation of cellular senescence.Nature reviews. Genetics · 2026Review
- Orthogonal Light- and Enzyme-Triggered and-Gate Prodrugs for Senolytic Therapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Molecular mechanisms and pathogenesis of MASH.Nature · 2026Review
- Sustainable Discovery of Natural Anti-Aging Bioactives from Food Resources: Current Status and Machine Learning Perspectives.Current issues in molecular biology · 2026Review
- Fueling the fire: metabolic dysfunction and senescence as drivers of lung aging and disease.Physiological reviews · 2026Review
- Multiscale Architecture and Mechanics of the Cell Nucleus: Implications for Disease, Bioengineering and Nanomedicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Article
- Towards a context-aware framework for cellular senescence.Biogerontology · 2026Review
- Review
- Overview of molecular signatures of senescence and associated resources: pros and cons.FEBS open bio · 2026Review
- Lysosomal Profiling with LysoTracker for Quantitative Assessment of Cellular Senescence in Human Fibroblasts.bioRxiv : the preprint server for biology · 2026Article
- Chronic Kidney Disease and Cellular Senescence.International journal of molecular sciences · 2026Review
- Monocytes are biological sensors of aging and frailty in humans.bioRxiv : the preprint server for biology · 2026Article
- AI-Driven Innovations for Quality Control and Standardization: Future Strategies in Adipose-Derived Stem Cell Manufacturing.International journal of molecular sciences · 2026Review
- Plasma Biomarkers of Senescence in Cholestatic Liver Disease: A Signature of Risk Stratification and Progression.Journal of gastroenterology and hepatology · 2026Article
- Self-supervised pretraining with NuSPIRe unlocks nuclear morphology-driven insights in spatial omics.Genome biology · 2026Article
- Leveraging AI for cell biology discovery.Biochemical Society transactions · 2026Review
- Decoding the tumor-aging axis: from bench to clinical.Frontiers in immunology · 2026Review
- Integrating senotherapeutics into transplantation: liver reconditioning in an aging donor pool.Transplant international : official journal of the European Society for Organ Transplantation · 2026Review
- Article
Corrections and comments
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Authors and funding
15 authors at 3 institutions in 2 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.