Effects of Donor Age and Cell Senescence on Kidney Allograft Survival
Bibliographic record
Abstract
The biological processes responsible for somatic cell senescence contribute to organ aging and progression of chronic diseases, and this may contribute to kidney transplant outcomes. We examined the effect of pre-existing donor aging on the performance of kidney transplants, comparing mouse kidney isografts and allografts from old versus young donors. Before transplantation, old kidneys were histologically normal, but displayed an increased expression of senescence marker p16(INK4a). Old allografts at day 7 showed a more rapid emergence of epithelial changes and a further increase in the expression of p16(INK4a). Similar but much milder changes occurred in old isografts. These changes were absent in young allografts at day 7, but emerged by day 21. The expression of p16(INK4a) remained low in young kidney allografts at day 7, but increased with severe rejection at day 21. Isografts from young donors showed no epithelial changes and no increase in p16(INK4a). The measurements of the alloimmune response-infiltrate, cytology, expression of perforin, granzyme B, IFN-gamma and MHC-were not increased in old allografts. Thus, old donor kidneys display abnormal parenchymal susceptibility to transplant stresses and enhanced induction of senescence marker p16(INK4a), but were not more immunogenic. These data are compatible with a key role of somatic cell senescence mechanisms in kidney transplant outcomes by contributing to donor aging, being accelerated by transplant stresses, and imposing limits on the capacity of the tissue to proliferate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".