Effect of atorvastatin on the angiogenic responsiveness of coronary endothelial cells in normal and streptozotocin (STZ) induced diabetic rats
Bibliographic record
Abstract
Atorvastatin, a lipid lowering agent, possesses various pleiotropic vasculoprotective effects, but its role in coronary angiogenesis is still controversial. Our objective was to study the effects of atorvastatin on the angiogenic responsiveness of coronary endothelial cells (cEC) from normal and diabetic rats. Male Wistar rats were distributed among 9 groups; (i) normal rats, (ii) 30 day diabetic rats, (iii) 60 day diabetic rats, (iv) normal rats administered a low dose of atorvastatin (1 mg/kg body mass, per oral (p.o.), for 15 days); (v) 30 day diabetic rats administered a low dose of atorvastatin; (vi) 60 day diabetic rats administered a low dose of atorvastatin; (vii) normal rats administered a high dose of atorvastatin (5 mg/kg, p.o., for 15 days); (viii) 30 day diabetic rats administered a high dose of atorvastatin; (ix) 60 day diabetic rats administered a high dose of atorvastatin. Each group was further divided into 2 subgroups, (i) sham ischemia-reperfusion and (ii) rats hearts that underwent ischemia-reperfusion. Angiogenic responsiveness the and nitric oxide (NO) releasing properties of the subgroups of cECs were studied using a chorioallantoic membrane assay and the Griess method, respectively. Atorvastatin treatment significantly increased VEGF-induced angiogenic responsiveness and the NO-releasing properties of cECs from all of the subgroups, compared with their respective non-treated subgroups except for the late-phase diabetic rat hearts that underwent ischemia-reperfusion, and the high dose of atorvastatin treatment groups. These effects of atorvastatin were significantly inhibited by pretreatment of cECs with l-NAME, wortmannin, and chelerythrine. Thus, treatment with a low dose of atorvastatin improves the angiogenic responsiveness of the cECs from normal and diabetic rats, in the presence of VEGF, via activation of eNOS-NO release.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".