Bibliographic record
Abstract
Biological sex is a factor in many conditions, including aging, neurodegenerative disease, cancer, and more. For each of these, men and women display distinct differences in disease development and progression. To date, studies on the molecular basis of such differences have largely focused on sex hormones, typically highlighting their neuroprotective benefits. However, new research suggests that cellular senescence may underlie sex differences in both neurological and non-neurological pathologies. Cellular senescence-stable proliferative arrest with a unique pro-inflammatory phenotype-occurs in response to persistent DNA damage signaling, safeguarding against tissue-level consequences of DNA damage (e.g., tumorigenesis). Though critical for maintaining tissue health, senescence has also been implicated in disease. Indeed, senescent cell accumulation occurs in multiple disease contexts, and the elimination of such cells (via senolytic therapies) alleviates associated disease hallmarks. If cell senescence is a driver of pathophysiology, sex differences in cellular senescence may underlie sex-specific disease outcomes. This review summarizes evidence of sex differences in cellular senescence-highlighting findings from both human and animal studies-and briefly discusses the potential relevance of sex chromosome epigenetics and mosaicism. Current studies show that female sex is associated with greater susceptibility to DNA damage and greater likelihood of senescence onset, despite additional evidence that estrogen protects against genotoxic insult and inhibits senescence regulatory proteins. Further studies on sex differences in cellular senescence are needed, both to verify whether findings from animal studies hold true in human contexts and to validate whether senescence manifests differently between men and women following comparable senescence-inducing stimuli.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".