Losartan-induced apoptosis as a novel mechanism for the prevention of vascular lesion formation after injury
Bibliographic record
Abstract
Smooth muscle cell (SMC) apoptosis is transiently increased at the onset of the regression of aortic hypertrophy in spontaneously hypertensive rats (SHR) treated with the angiotensin II AT(1) antagonist losartan. We postulated that losartan induction of SMC apoptosis contributes to suppression of neointimal hyperplasia after vascular injury. Losartan or placebo treatment was initiated two days before balloon injury in the SHR aorta. Compared with time-matched placebo, losartan decreased neointimal cross-sectional area at Days 5 and 10 after injury by 50% and 64% respectively, without affecting medial mass. At Day 10, losartan significantly decreased SMC number (by 56%) in the neointima, but not in the media. DNA synthesis was significantly inhibited at Day 5 but not at Day 10. Losartan significantly increased aortic DNA fragmentation by 2.6- and 4.1-fold, at Days 5 and 10, respectively. In situ labeling of SMC with terminal deoxynucleotidyltransferase revealed significant 61% and 68% increases in apoptotic SMC at Days 5 and 10 with losartan treatment, predominantly in the neointima. Thus, losartan suppressed neointima formation in part by the induction of SMC apoptosis, which may be dissociated from the inhibition of DNA synthesis. Therefore, losartan-induced SMC apoptosis may be a potential therapeutic approach to control occlusive vascular disorders.
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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".