LIPOPROTEINS INHIBIT PLATELET AGGREGATION AND ARACHIDONIC ACID METABOLISM IN EXPERIMENTAL HYPERCHOLESTEROLAEMIA
Bibliographic record
Abstract
1. Human plasma contains unidentified components that inhibit arachidonic acid (AA) metabolism. In the present study, we investigated whether plasma from rabbits fed a normal or high-cholesterol diet for 16 weeks also inhibits AA metabolism. Specifically, we studied the effects of plasma on platelet aggregation and on the production of AA metabolites, tri-hydroxyeicosatrienoic acid, 12-hydroxyeicosatetraenoic acid and thromboxane B(2). 2. Haematological and lipid profiles were altered by a high-cholesterol diet. Platelets from hypercholesterolaemic rabbits showed enhanced aggregatory sensitivity to AA and platelet-activating factor. However, plasma from hypercholesterolaemic and control rabbits, when added to the incubation mixture, significantly inhibited platelet aggregation and eicosanoid production. 3. High- and low-density lipoprotein (HDL and LDL, respectively) concentrations increased several-fold in plasma with cholesterol feeding. When added directly to the incubation mixture, both HDL and LDL inhibited platelet aggregation, as well as AA metabolism. 4. Haptoglobin, albumin and Cohn's fraction IV, but not globulins, exhibited antiplatelet and anti-AA metabolism activities. Their concentrations in plasma were not affected by cholesterol feeding. 5. We conclude that LDL and HDL account for at least some of the inhibition of AA metabolism produced by plasma.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".