Comprehensive Assessment of Rosiglitazone on Cardiac Safety in a Rat Model of Heart Failure
Bibliographic record
Abstract
The antidiabetic drugs, PPARγ‐modulator class, are associated with severe cardiovascular risk. This liability has not been sufficiently explored in animal models. The present study was designed to assess cardiac safety biomarkers associated with a PPARγ agonist, rosiglitazone (Rosi), in rat heart failure (HF) model. HF was induced by coronary artery ligation and confirmed by ultrasound (US). Sham or HF rats were treated by low (3 mg/kg) or high (45 mg/kg) doses of Rosi, po, for 28 days. Biomarkers for heart structure and function (US), myocardial fibrosis, neuroendocrine biomarkers (BNP, aldosterone) and metabolic variables (glucose and insulin) were monitored. Targeted low‐density gene array was deployed to assess genomic responses. HF was marked by increase in BNP and ANP, low ejection fraction (30‐40%). Rosi effect was confirmed by increase in plasma adiponectin. Genomic analysis of model/treatment effects confirmed suppression by Rosi on ACE and TNFα expression. No evidence on exacerbation of cardiac function, structure and genomic change was noted in HF. ENaCs and Na + /Cl − /K + transporters remained intact while marked increase in PPARγ expression was noted. Our data suggest that Rosi had no adverse effect on cardiac function in rat HF; instead, protective effect was suggested by decrease in ACE and ANP. These data suggest that cardiovascular risk of PPARγ need to be studied in non‐rodent models.
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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 | 0.000 | 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".