Evidence That Nitric Oxide Regulates AT1-Receptor Agonist and Antagonist Efficacy in Rat Injured Carotid Artery
Bibliographic record
Abstract
Vascular injury stimulates AT1-receptor expression and nitric oxide (NO) production in smooth muscle cells (SMCs). We examined the ability of AT1 agonists and antagonists to regulate vascular tone ex vivo in injured arteries and the possible modulation by SMC-derived NO. Rings of rat carotid arteries were isolated at day 7 after endothelial denudation and stimulated with angiotensin (Ang) II in the absence or presence of the AT1 antagonists losartan, L-158,809, or EXP-3174. Freshly denuded contralateral arteries were used as controls. AngII-induced contractions were similar in control and injured arteries. Losartan caused an insurmountable inhibition of AngII-induced contractions in injured but not control arteries. Enhanced inhibition of AngII in injured arteries also was observed in the presence of L-158,809 and EXP-3174. In the presence of the NO synthesis inhibitor nitromonomethyl-L-arginine (L-NMMA), maximal contractions to AngII were greater in injured than in control vessels, and AT1-receptor blockade with losartan was surmountable in all vessels. Mechanical removal of superficial neointimal SMCs attenuated NO production and normalized the efficacy of losartan in injured arteries. These results suggest a role for NO in reducing the biologic effects of AT1-receptor agonists and potentiating the efficacy of AT1 antagonists in vessels undergoing remodeling after injury.
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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) | 0.001 | 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".