Resveratrol Treatment of Mice With Pressure-Overload–Induced Heart Failure Improves Diastolic Function and Cardiac Energy Metabolism
Bibliographic record
Abstract
BACKGROUND: Although resveratrol has multiple beneficial cardiovascular effects, whether resveratrol can be used for the treatment and management of heart failure (HF) remains unclear. In the current study, we determined whether resveratrol treatment of mice with established HF could lessen the detrimental phenotype associated with pressure-overload-induced HF and identified physiological and molecular mechanisms contributing to this. METHODS AND RESULTS: C57Bl/6 mice were subjected to either sham or transverse aortic constriction surgery to induce HF. Three weeks post surgery, a cohort of mice with established HF (% ejection fraction <45) was administered resveratrol (≈320 mg/kg per day). Despite a lack of improvement in ejection fraction, resveratrol treatment significantly increased median survival of mice with HF, lessened cardiac fibrosis, reduced gene expression of several disease markers for hypertrophy and extracellular matrix remodeling that were upregulated in HF, promoted beneficial remodeling, and improved diastolic function. Resveratrol treatment of mice with established HF also restored the levels of mitochondrial oxidative phosphorylation complexes, restored cardiac AMP-activated protein kinase activation, and improved myocardial insulin sensitivity to promote glucose metabolism and significantly improved myocardial energetic status. Finally, noncardiac symptoms of HF, such as peripheral insulin sensitivity, vascular function, and physical activity, were improved with resveratrol treatment. CONCLUSIONS: Resveratrol treatment of mice with established HF lessens the severity of the HF phenotype by lessening cardiac fibrosis, improving molecular and structural remodeling of the heart, and enhancing diastolic function, vascular function, and energy metabolism.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".