RIPK3-Mediated Necroptosis Regulates Cardiac Allograft Rejection
Bibliographic record
Abstract
Cell death results in tissue damage and ultimately donor graft rejection and can occur as an active molecular process through apoptotic, necrotic and newly identified receptor interacting protein 1 and 3 kinase (RIPK1/3)-mediated necroptotic pathways. Necroptosis leads to the release of inflammatory molecules which can activate host immune cells. This pathway has yet to be studied in heart transplantation. We have found that necroptosis was induced in murine cardiac microvascular endothelial cell (MVEC) under anti-apoptotic condition following tumor necrosis factor alpha treatment. Necroptotic cell death and release of the danger molecule high mobility group box 1 (HMGB1) were inhibited by the RIPK1 inhibiting molecule necrostatin-1 and by genetic deletion of RIPK3. In addition, tissue necrosis, release of HMGB1 and graft cell infiltrate were attenuated in RIPK3 null heart allografts following transplantation. Finally, a brief sirolimus treatment markedly prolonged RIPK3 null cardiac allograft survival in allogeneic BALB/c recipients as compared to WT C57BL/6 donor grafts (95 ± 5.8 vs. 24 ± 2.6 days, p < 0.05). This study has demonstrated that RIPK1/3 contributes to MVEC death and cardiac allograft survival through necroptotic death and the release of danger molecules. Our results suggest that targeting RIPK-mediated necroptosis may be an important therapeutic strategy in transplantation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".