Effect of atorvastatin on atherosclerotic plaque formation and platelet activation in hypercholesterolemic rats
Bibliographic record
Abstract
We aimed to investigate whether atorvastatin influenced the CD40-CD40L pathway in atherosclerosis formation in rats fed a high cholesterol diet. Thirty-six male Wistar rats were divided among 4 groups as follows: control (C), statin (S), 5% cholesterol fed (HC), and statin-administered hypercholesterolemic (HCS). Serum levels of lipids, soluble CD40L, platelet factor 4, and interleukin-6 were assayed with commercial kits. The number of platelets expressing surface P-selectin, CD40, and CD40L were determined by flow cytometry. Aortas were examined for fatty streaks. In the HC group, we observed a significant increase in serum lipid levels and platelet activation markers compared with the control group. Rats in the HCS group had a significant decrease in lipid levels and downregulation in the number of platelets expressing surface P-selectin, CD40, and CD40L compared with the HC group. We observed decreased fatty streak formations in aortas in HCS rats. A positive correlation was found for platelet activation markers and atherosclerotic fatty streak formations. Regression analysis revealed that the predictor of atherosclerosis was CD40L. Our study suggests that in a rat hypercholesterolemic model, statin treatment may influence the CD40-CD40L dyad, and that this effect is parallelled by a suppression of progression of atherosclerotic plaque formation.
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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 | 0.000 |
| 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) | 0.001 | 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".