Cytochrome p450 2C Enzymes Contribute to Peritransplant Ischemic Injury and Cardiac Allograft Vasculopathy
Bibliographic record
Abstract
Peritransplant ischemia and reperfusion (I/R) injury contributes to posttransplant vascular dysfunction and cardiac allograft vasculopathy (CAV). We have previously shown that cytochrome p450 (CYP) 2C inhibition significantly reduces I/R-induced myocardial infarction and postischemic vascular dysfunction. In the latter study, pretreatment with sulfaphenazole (SP), a specific inhibitor of CYP 2C, restored postischemic NO-mediated, endothelium-dependent vasodilation and reduced vascular superoxide production. Given the association between I/R injury, early vascular dysfunction and CAV, we hypothesized that CYP 2C may also contribute to the onset of CAV. Lewis-to-Fisher rat heterotopic heart transplants were performed. Donors and recipients were treated with 5 mg/kg SP or vehicle control 1 h prior to surgery. SP did not affect posttransplant morbidity, mortality or weight gain. Coronary blood vessels from rats treated with SP exhibited significantly reduced luminal narrowing and demonstrated a corresponding decrease in smooth muscle cell (SMC) proliferation compared to controls. SP did not reduce diffuse, focal, epicardial, endocardial or perivascular immune infiltration nor did it significantly alter TUNEL positivity in myocardial, endothelial or SMC populations. In conclusion, CYP 2C contributes to SMC proliferation CAV without affecting general immune infiltration.
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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 | 0.000 |
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".