Cellular Senescence in Human Skin Aging: Leveraging Senotherapeutics
Bibliographic record
Abstract
BACKGROUND: As the largest organ in the human body, the skin is continuously exposed to intrinsic and extrinsic stimuli that impact its functionality and morphology with aging. Skin aging entails dysregulation of skin cells and loss, fragmentation, or fragility of extracellular matrix fibers that are manifested macroscopically by wrinkling, laxity, and pigmentary abnormalities. Age-related skin changes are the focus of many surgical and nonsurgical treatments aimed at improving overall skin appearance and health. SUMMARY: As a hallmark of aging, cellular senescence, an essentially irreversible cell cycle arrest with apoptosis resistance and a secretory phenotype, manifests across skin layers by affecting epidermal and dermal cells. Knowledge of skin-specific senescent cells, such as melanocytes (epidermal aging) and fibroblasts (dermal aging), will promote our understanding of age-related skin changes and how to optimize patient outcomes in esthetic procedures. KEY MESSAGES: This review provides an overview of skin aging in the context of cellular senescence and discusses senolytic intervention strategies to selectively target skin senescent cells that contribute to premature skin aging.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".