Plant food anthocyanins inhibit platelet granule secretion in hypercholesterolaemia: Involving the signalling pathway of PI3K–Akt
Bibliographic record
Abstract
Controlling platelet granule secretion has been considered an effective strategy to dampen thrombosis and prevent atherosclerosis. Anthocyanins are natural plant pigments and possess a wide range of biological activities, including cardiovascular protective activity. In the present study we explored the effects and the potential mechanisms of anthocyanins on platelet granule secretion in hypercholesterolemia. In a randomised, double-blind clinical trial, 150 hypercholesterolaemic individuals were treated with purified anthocyanins (320 mg/day) or placebo for 24 weeks. Anthocyanins consumption significantly reduced plasma levels of β-thromboglobulin (β-TG), soluble P-selectin, and of Regulated on Activation Normal T cell Expressed and Secreted (RANTES) as compared with the placebo. A minor reduction in platelet factor 4 (PF4) and transforming growth factor β1 (TGF-β1) levels were also observed. In in vitro experiments, we observed that puriӿed anthocyanin mixture, as well as its two main anthocyanin components, delphinidin-3-glucoside (Dp-3-g) and cyanidin-3-glucoside (Cy-3g) directly inhibited platelet á-granule, dense granule, and lysosome secretion evaluated by P-selectin, RANTES, β-TG, PF4, TGF-β1, serotonin, ATP, and CD63 release. Further, anthocyanins inhibited platelet PI3K/Akt activation and consequently attenuated eNOS phosphorylation and cGMP production, thus interrupting MAPK activation. LY294002, a PI3K inhibitor, did not cause additional inhibitory efficacy, indicating that anthocyanin-induced effects may be involved in inhibition of the PI3K/Akt signalling pathway. These results provide evidence that by inhibiting platelet granule secretion, anthocyanins may be a potent cardioprotective agent.
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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".