Effectiveness of β-Blockade in Experimental Chronic Aortic Regurgitation
Bibliographic record
Abstract
BACKGROUND: Past studies have suggested that the adrenergic system becomes abnormally activated in chronic volume overload, such as in severe aortic valve regurgitation (AR). However, the effectiveness of agents directed against this adrenergic activation has never been adequately tested in chronic AR. We therefore tested the effects of metoprolol treatment on the left ventricular (LV) function and remodeling in severe chronic AR in rats. METHODS AND RESULTS: Severe AR was created in adult male Wistar rats by retrograde puncture of the aortic leaflets under echocardiographic guidance. Two weeks later, some animals received metoprolol treatment (25 mg/kg) orally for 24 weeks, and some were left untreated. LV dimensions, ejection fraction, and filling parameters were evaluated by echocardiography. Hearts were harvested at 1, 2, 14, and 180 days for the evaluation of hypertrophy, beta-adrenergic receptor status, and extracellular matrix remodeling. We found that metoprolol treatment prevented LV dilatation and preserved the ejection fraction and filling parameters compared with untreated animals. Metoprolol increased the expression of beta1-adrenoreceptor mRNA and reduced G protein receptor kinase 2 levels. Collagen I and III mRNA levels were reduced. Cardiac myocyte hypertrophy was also prevented. CONCLUSIONS: In our experimental model of severe AR, metoprolol treatment had a significant beneficial global effect on LV remodeling and function. These results suggest that the adrenergic system is important in the development of volume-overload cardiomyopathy in AR and that adrenergic-blocking agents may play a role in the treatment of this disease.
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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".