Abstract 18964: Resveratrol Prevents Iron-overload Cardiomyopathy by up Regulating Serca2a and by Exerting Anti-oxidant Effects
Bibliographic record
Abstract
Introduction: Iron-overload cardiomyopathy is a world wide epidemic with high morbidity and mortality which has both acquired and genetic causes. Cardiac iron-deposition results in progressive myocardial damage and dysfunction due to increased oxidative stress. Hypothesis: We hypothesized that resveratrol supplementation prevents oxidative stress and iron-overload cardiomyopathy. Methods and Results: We developed an acquired iron-overload model by treating 10 week old male C57BL6 mice sub-acutely/chronicaly with iron-dextran (5 mg/25g) i.p. for 4 or 12 wks, and a genetic model by treating 4 wk old hemojuvelin (HJV) knockout male mice with high iron diet for 6 months. The natural antioxidant, resveratrol, was given at 190 mg/kg/day. Iron-overload hearts showed significant iron deposition. Hemodynamic (+dP/dt/-dP/dt max =1.0 vs 1.7) and echocardiographic (E/A: 1.46±0.043 vs 1.27±0.06, p 2+ transients were slowed in iron-overloaded cardiomyocytes and normalized with resveratrol treatment. In contrast, chronic iron-overload was associated with increased myocardial fibrosis in HJVKO and chronic iron-overload models. Resveratrol treatment prevented the development of diastolic dysfunction (E/A: 1.27±0.06 vs 1.67±0.13 and E’/A’:1.2±0.05 vs 0.80±0.02, p Conclusions: Cardiac iron-overload resulted in abnormal Ca 2+ cycling with reduced SERCA2a levels and increased myocardial fibrosis, leading to impaired myocardial relaxation and increased stiffness. Resveratrol therapy increased SERCA2a protein, decreased myocardial fibrosis leading to protection from iron-overload induced cardiac dysfunction.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".