The Prevention of Diabetic Cardiomyopathy by Non-Mitogenic Acidic Fibroblast Growth Factor Is Probably Mediated by the Suppression of Oxidative Stress and Damage
Bibliographic record
Abstract
BACKGROUND: Emerging evidence showed the beneficial effect of acidic fibroblast growth factor (aFGF) on heart diseases. The present study investigated whether non-mitogenic aFGF (nm-aFGF) can prevent diabetic cardiomyopathy and the underlying mechanisms, if any. METHODOLOGY/PRINCIPAL FINDINGS: Type 1 diabetes was induced in mice by multiple intraperitoneal injections of low-dose streptozotocin. Hyperglycemic and age-matched control mice were treated with or without nm-aFGF at 10 µg/kg daily for 1 and 6 months. Blood pressure and cardiac function were assessed. Cardiac H9c2 cell, human microvascular endothelial cells, and rat cardiomyocytes were exposed to high glucose (25 mM) for mimicking an in vitro diabetic condition for mechanistic studies. Oxidative stress, DNA damage, cardiac hypertrophy and fibrosis were assessed by real-time qPCR, immunofluorescent staining, Western blotting, and pathological examination. Nm-aFGF significantly prevented diabetes-induced hypertension and cardiac dysfunction at 6 months. Mechanistic studies demonstrated that nm-aFGF showed the similar preventive effect as the native aFGF on high glucose-induced oxidative stress (increase generation of reactive oxygen species) and damage (cellular DNA oxidation), cell hypertrophy, and fibrotic response (increased mRNA expression of fibronectin) in three kinds of cells. These in vitro findings were recaptured by examining the heart of the diabetic mice with and without nm-aFGF. CONCLUSIONS: These results suggest that nm-aFGF can prevent diabetic cardiomyopathy, probably through attenuation of cardiac oxidative stress, hypertrophy, and 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) | 0.000 | 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 | 0.000 |
| 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".