Losartan and acute myocardial infarction in insulin-resistant Zucker fatty rats: reduced ventricular arrhythmias and improved survival
Bibliographic record
Abstract
Insulin resistance (IR) and diabetes increase the risk of acute myocardial infarction (MI). Angiotensin receptor blockers (ARBs) have been shown to reduce the risk of cardiovascular events in patients with hypertension and diabetes, and to be beneficial after a large MI. Whether pretreatment with ARBs is beneficial in acute MI is unknown. We evaluated whether pre-, peri-, and post-MI treatment with the ARB losartan improved the outcome in the IR Zucker fatty rat (ZFR). ZFR (n=264) received either losartan (3 mg/kg daily) or vehicle for 7 d prior to MI. Early (24 h) protocol (n=31): ventricular arrhythmias were evaluated post-MI using continuous ambulatory ECG monitoring. Late (38 d) protocol (n=233): losartan was increased to 10 mg/kg daily 10 d post-MI and to 30 mg/kg daily 20 d post-MI. Blood glucose, cardiac hemodynamics and remodeling, GLUT-4, fetal gene expression, and survival were evaluated. In large-MI rats, losartan improved early survival (43% vs. 27% in controls, p=0.01) and late survival (23% vs.15% in controls, p=0.02). Improved early survival was associated with a reduction in ventricular arrhythmias. Losartan reduced pulmonary congestion, cardiac hypertrophy, and fetal gene expression in the absence of statistically significant changes in ventricular dilatation and hemodynamics. Blood glucose and cardiac GLUT-4 expression did not change with losartan. In IR ZFR, losartan improves post-MI survival, likely as a result of an early reduction in ventricular arrhythmias. There was also an associated reduction in pulmonary congestion, hypertrophy, and fetal gene expression.
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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 | 0.001 |
| 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 | 0.001 | 0.002 |
| 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".