Comparative Study of Vasodilators in an Animal Model of Chronic Volume Overload Caused by Severe Aortic Regurgitation
Bibliographic record
Abstract
BACKGROUND: Aortic regurgitation (AR) is a disease of chronic left ventricular (LV) volume overload. Over time, AR will lead to LV dilatation, hypertrophy, and loss of function. There is currently no medical treatment proven effective to slow the evolution of this cardiomyopathy. Vasodilators were once thought to have protective effects, but recent publications have cast some doubts about their effectiveness. We hypothesized that drugs targeting the renin-angiotensin system should be more effective than those having no direct effect on the renin-angiotensin system. METHODS AND RESULTS: We designed a protocol comparing the effects of 3 vasodilators in a rat AR model (n=9 to 11 animals per group). The effects of a 6-month treatment of (1) nifedipine, (2) captopril, or (3) losartan were compared in male AR rats. Sham-operated and untreated AR animals were used as controls. Nifedipine-treated animals displayed hemodynamics, LV dilatation, hypertrophy, and loss of function similar to those of the untreated group. Both captopril and losartan were effective in improving hemodynamics, slow LV dilatation, hypertrophy, and dysfunction. Gene expression analysis confirmed the lack of effects of the nifedipine treatment at the molecular level. CONCLUSIONS: Using an animal model of severe AR, we found that vasodilators targeting the renin-angiotensin system were effective to slow the development of LV remodeling and to preserve LV function. As recently shown in the most recent human clinical trial, nifedipine was totally ineffective. Targeting the renin-angiotensin system seems a promising avenue in the treatment of this disease, and clinical trials should be carefully designed to re-evaluate the effectiveness of angiotensin I-converting enzyme inhibitors or angiotensin II receptor blockers in AR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".