Heart Rate Reduction by Ivabradine Reduces Diastolic Dysfunction and Cardiac Fibrosis
Bibliographic record
Abstract
OBJECTIVES: To determine if heart rate (HR) reduction with ivabradine (IVA), a selective inhibitor of the pacemaker I(f) current, prevents cardiac dysfunction associated with dyslipidemia. METHODS: New Zealand White rabbits received either a standard diet, a 0.5% cholesterol-enriched diet only (CD), or a 0.5% CD with IVA (17 mg/kg/day) for 12 weeks. HR, left ventricular (LV) systolic function, diastolic function and LV regional myocardial performance index (MPI) were studied using echocardiography. Histological analysis included cardiac interstitial fibrosis and collagen type I fibers. Plasma levels of angiotensin II and aldosterone were quantified by immunoassays. RESULTS: IVA reduced HR by approximately 11%. IVA improved MPI and attenuated LV diastolic dysfunction (DD) (92% mild and 8% moderate DD with IVA vs. 54% mild and 46% moderate DD in CD group). IVA also reduced atrial fibrosis (p = 0.027), ventricular fibrosis (p = 0.0002) and ventricular collagen type I (p = 0.0042). IVA decreased plasma angiotensin II levels (p = 0.042), and both angiotensin II and aldosterone levels were correlated with HR (p = 0.038 and 0.008). CONCLUSION: Selective HR reduction with IVA reduces DD and cardiac fibrosis in hypercholesterolemic rabbits. These beneficial effects of IVA support testing pure HR reduction in patients with diastolic heart failure.
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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) | 0.000 | 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 | 0.001 |
| 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".