Effect of resveratrol on platelet activation in hypercholesterolemic rats: CD40–CD40L system as a potential target
Bibliographic record
Abstract
Our aim was to investigate whether trans-resveratrol (t-resveratrol), a red wine constituent known for its cardioprotective effects, was able to influence CD40 ligand (CD40L) and its receptor CD40 in platelets of hypercholesterolemic rats. Sixty Wistar rats were divided into 5 groups: control (C), ethanol (E), t-resveratrol (R), hypercholesterolemia (HC), and hypercholesterolemia plus t-resveratrol (HCR). Rats in the C, E, and R groups were fed a normal diet for 80 days. For 20 days before sacrifice, we intraperitoneally (i.p.) administered 0.1 mL ethanol (50% v/v) to the E group, and 0.1 mL t-resveratrol (20 mg·kg(-1)·day(-1)) to the R group. Rats in the HC and HCR groups were fed a 5% cholesterol diet for 80 days. Rats in the HCR group were administered i.p. 0.1 mL t-resveratrol (20 mg·kg(-1)·day(-1)) for 20 days before sacrifice. Serum levels of total cholesterol (TC), low-density lipoprotein (LDL-C), high-density lipoprotein (HDL-C), very low-density lipoprotein (VLDL-C), and total triglycerides (TG) were assayed with a commercial colorimetric kit. Platelet P-selectin, CD40, and CD40L expression was determined by flow cytometry. sCD40L and IL6 levels were measured by ELISA. In the HC group, we observed a significant increase in serum TC, LDL-C, VLDL-C, TG, sCD40, and IL-6 levels and platelet activation markers compared with levels in the control group. However, t-resveratrol administration to the HC group (HCR group) attenuated the increase in lipids, sCD40, and IL-6 and down-regulated platelet P-selectin, CD40, and CD40L expressions. A positive correlation was found for serum lipids and all the platelet activation markers. Our study showed that the CD40-CD40L dyad is up-regulated in the presence of hypercholesterolemia and that t-resveratrol administration down-regulated the increase.
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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 | 0.001 |
| 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".