Prevention of Chronic Renal Allograft Rejection by SERP-1 Protein
Bibliographic record
Abstract
BACKGROUND: In previous studies we have demonstrated that Serp-1, a myxoma virus encoded serine protease inhibitor, dramatically inhibits neointimal hyperplasia in vascular injury and aortic transplant models. Here we examined the effect of peritransplant Serp-1 administration on chronic renal allograft rejection. METHODS: Rat renal transplants were performed with sequential recipient sacrifice on postoperative days 2, 10 and 140 to examine both the acute and chronic effects of Serp-1 in recipient rats. RESULTS: Serp-1 administration reduced early posttransplant injury (POD 2) with less acute tubular and vascular necrosis. This translated into a reduction of the characteristic late stage changes of chronic rejection (POD 140), with significantly decreased glomerulosclerosis and neointimal hyperplasia. Effects of Serp-1 treatment were already evident as early as POD 2 with markedly decreased levels of TGF-beta mRNA witnessed at both the early and late time points (POD 2, 10 and 140). CONCLUSION: We have demonstrated that peritransplant Serp-1 viral protein decreased early injury and allowed reduced chronic rejection in a rat renal model. Recipients treated with Serp-1 are associated with a decrease in TGF-beta mRNA levels in the allografts suggesting that the serine protease inhibitor may inhibit TGF-beta transcription and its profibrotic effects.
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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) | 0.001 | 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".