ArticleNature communications2025
Nuclear morphometrics coupled with machine learning identifies dynamic states of senescence across age.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
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Who cites it
5 citing papers in PubMed.
- Article
- Review
- Deployment of Artificial Intelligence in Clinical Oral Pathology: Evidence Summary and Implementation Gaps.Analytical cellular pathology (Amsterdam) · 2026Review
- Cancer and Aging Biomarkers: Classification, Early Detection Technologies and Emerging Research Trends.Biosensors · 2025Review
- The Integrated Stress Response Pathway Improves Aged Murine Muscle Stem Cell Activation andbioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Cellular senescence is an irreversible state of cell cycle arrest with a complex role in tissue repair, aging, and disease. However, inconsistencies in identifying cellular senescence have led to varying conclusions about their functional significance. We developed a machine learning-based approach that uses nuclear morphometrics to identify senescent cells at single-cell resolution. By applying unsupervised clustering and dimensional reduction techniques, we built a robust pipeline that distinguishes senescent cells in cultured systems, freshly isolated cell populations, and tissue sections. Here we show that this method reveals dynamic, age-associated patterns of senescence in regenerating skeletal muscle and osteoarthritic articular cartilage. Our approach offers a broadly applicable strategy to map and quantify senescent cell states in diverse biological contexts, providing a means to readily assess how this cell fate contributes to tissue remodeling and degeneration across lifespan.
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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.