An Unanticipated Role for Survivin in Organ Transplant Damage
Bibliographic record
Abstract
Ischemia/reperfusion (I/R) injury is a major determinant of graft survival in kidney transplantation. Survivin, an inhibitor of apoptosis that participates in the control of mitosis and cell cycle progression, has been implicated in renal protection and repair after I/R injury; however, no study has been performed in the transplant setting. We investigated the role of survivin in modulating posttransplant I/R injury in syngeneic and allogeneic kidney grafts, and studied whether protection from I/R injury impacted on the recipient immune system, on chronic allograft nephropathy and rejection. We used genetically engineered mice with survivin haploinsufficiency and WT mice in which survivin over-expression was induced by gene-delivery. Survivin haploinsufficiency in syngeneic grafts was associated with exuberant I/R tissue injury, which triggered inflammation eventually resulting in graft loss. Conversely, survivin over-expression in the grafts minimized I/R injury and dysfunction in syngeneic grafts and in a clinically relevant fully MHC-mismatched allogeneic combination. In the latter, survivin over-expression translated into limited anti-donor adaptive immune response and less long-term allograft injury with protection from renal parenchymal damage. Our data support survivin over-expression in the graft as a novel target for protocols aimed at limiting tissue damage at the time of transplant ultimately modulating the recipient immune system.
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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 | 0.002 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".