Bibliographic record
Abstract
PURPOSE OF REVIEW: Ageing of the kidney is a problem of clinical and basic interest. The problem of renal dysfunction and end-stage renal disease is a major burden on the health system, and old donor age is a major limitation on the use of donor organs and on survival of transplanted kidneys. Moreover, stresses linked to nephropathies, postoperative stress, inflammation and allograft rejection can lead to premature senescence of renal cells thus accelerating organ atrophy. Age-related and disease or stress-related nephron loss could reflect both the limited ability of epithelial cells to repair and replicate in the face of environmental stresses, and limitations on the number of cell replications caused by telomere shortening. Therefore, elucidating cellular senescence mechanisms is relevant to kidney diseases and kidney transplantation. RECENT FINDINGS: Recent findings suggest additive effects of replicative and environmental stress-induced senescence in cellular and organ ageing. In particular, ATM/p53/p21 and Ras/p38/p16 pathways have been shown to co-contribute to the overall cellular senescence, which is caused by extrinsic and intrinsic stimuli. Moreover, the role of epigenetic factors, including protein acylation/deacetylation, chromatin remodeling or caloric restriction, is the focus of recent studies on ageing and senescence. SUMMARY: Despite significant progress, cellular senescence is still better understood in vitro than in vivo. So far, p16 remains the best marker of chronological age in the kidney, and can be considered as an indicator of premature senescence caused by stresses or disease. The beneficial effects of caloric restriction on organ ageing and the role of histone acetylation in pathologic states in rodents are of considerable interest, and deserve future studies.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".