Angiotensin-converting enzyme inhibitor captopril prevents volume overload cardiomyopathy in experimental chronic aortic valve regurgitation
Bibliographic record
Abstract
The efficacy of angiotensin-converting enzyme inhibitors (ACEIs) in the treatment of chronic aortic regurgitation (AR) is not well established and remains controversial. The mechanisms by which ACEIs may protect against left-ventricular (LV) volume overload are not well understood, and clinical trials performed until now have yielded conflicting results. This study was therefore performed to assess the effectiveness of two different doses of the ACEI captopril in a rat model of chronic AR. We compared the effects of a 6-month low-dose (LD) (25 mg/kg) or higher dose (HD) (75 mg/kg) treatment with captopril on LV function and hypertrophy in Wistar rats with severe AR. Untreated animals developed LV eccentric hypertrophy and systolic dysfunction. LD treatment did not prevent hypertrophy and provided modest protection against systolic dysfunction. HD treatment preserved LV systolic function and dimensions and tended to slow hypertrophy. The cardiac index remained high and similar among all AR groups, treated or not. Tissue renin-angiotensin system (RAS) analysis revealed that ACE activity was increased in the LVs of AR animals and that only HD treatment significantly decreased angiotensin II receptor mRNA levels. Fibronectin expression was increased in the LV or AR animals, but HD treatment almost completely reversed this increase. The ACE inhibitor captopril was effective at high doses in this model of severe AR. These effects might be related to the modulation of tissue RAS and the control of fibrosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".