Cholesterol-induced stimulation of platelet aggregation is prevented by a hempseed-enriched dietThis article is one of a selection of papers published in the special issue Bridging the Gap: Where Progress in Cardiovascular and Neurophysiologic Research Meet.
Bibliographic record
Abstract
Hypercholesterolemia indirectly increases the risk for myocardial infarction by enhancing the ability of platelets to aggregate. Diets enriched with polyunsaturated fatty acids (PUFAs) have been shown to reduce the detrimental effects of cholesterol on platelet aggregation. This study investigated whether dietary hempseed, a rich source of PUFAs, inhibits platelet aggregation under normal and hypercholesterolemic conditions. Male New Zealand white rabbits were fed one of 6 dietary interventions: regular control diet (RG); control diet + 10% hempseed (HP); control diet + 10% partially delipidated hempseed (DHP); control diet + 0.5% cholesterol (OL); control diet + 0.5% cholesterol + 10% hempseed (OLHP); control diet + 5% coconut oil (CO). After 8 weeks, blood was collected to measure ADP- and collagen-induced platelet aggregation and plasma levels of fatty acids, cholesterol, and triglycerides. The hempseed-fed animals (HP and OLHP) displayed elevated plasma levels of PUFAs and a prominent enhancement in 18:3n-6 (gamma-linolenic acid, GLA) levels, a unique PUFA found in hempseed. The cholesterol-supplemented groups (OL and OLHP) had significantly elevated plasma levels of cholesterol and triglycerides, but platelet aggregation was significantly augmented only in the OL group. The addition of hempseed to this diet (OLHP) normalized aggregation. The direct addition of GLA to the OL platelet samples blocked the cholesterol-induced stimulation of platelet aggregation. The results of this study demonstrate that when hempseed is added to a cholesterol-enriched diet, cholesterol-induced platelet aggregation returns to control levels. This normalization is not due to a reduction in plasma cholesterol levels, but may be partly due to increased levels of plasma GLA.
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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) | 0.001 | 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".