Synergistic interaction between enalapril, <scp>L</scp>‐arginine and tetrahydrobiopterin in smooth muscle cell apoptosis and aortic remodeling induction in SHR
Bibliographic record
Abstract
Smooth muscle cell (SMC) apoptosis occurs at the onset of enalapril-induced regression of aortic hypertrophy in SHR. A potential mechanism is the correction of endothelial dysfunction (ED) leading to reduced production of reactive oxygen species and enhanced bioavailability of nitric oxide (NO), a potent apoptosis inducer. Stimulants of NO include the precursor L-arginine and the NO synthase cofactor tetrahydrobiopterin (BH(4)), which correct ED in several models. The objective was to examine the relationships between ED and the cell growth/death balance during vascular remodeling induced by enalapril in SHR. SHR, 10-week-old, received enalapril (ENA: 30 mg x kg(-1) x day(-1) p.o.) for 1 or 2 weeks, or a co-treatment of L-arginine (2.0 g x kg(-1) x day(-1) p.o.) and BH(4) (5.4 mg x kg(-1) x day(-1) i.p. twice daily) administered alone (group: LB) or in combination with enalapril (ENA+LB) for 1 week. Controls received vehicle. After 1 week, ED was completely corrected with LB but not affected significantly by ENA, whereas both treatments failed to induce SMC apoptosis or aortic remodeling. The correction of ED and the induction of SMC apoptosis (3.3-fold increase in TUNEL labeling) required 2 weeks of ENA treatment. The combination of LB with ENA for 1 week, however, was additive for the reduction of SMC proliferation, and synergistic for the induction of apoptosis and regression of vascular hypertrophy. These interactions were independent of blood pressure regulation. Our results suggest that the correction of ED is not sufficient to induce SMC apoptosis and vascular remodeling, although it facilitates these responses during enalapril treatment.
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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".