Genetics and Prevention: A New Look at High-Density Lipoprotein Cholesterol
Bibliographic record
Abstract
Plasma level of high-density lipoprotein cholesterol is inversely correlated with coronary artery disease. High-density lipoprotein particles are thought to mediate the uptake of peripheral cholesterol and, through exchange of core lipids with other lipoproteins or selective uptake by specific receptors, return this cholesterol to the liver for bile acid secretion. During the past decade, high-density lipoprotein particles have been found to modulate thrombosis, cell adhesion molecule expression, vasomotor function, platelet function, and endothelial cell apoptosis and proliferation. Many of these effects involve the signal transduction pathway and gene transcription. Genetic disorders of high-density lipoproteins have been characterized at the molecular level. Mutations within the genes involved in the structure and metabolism of high-density lipoproteins can cause high-density lipoprotein deficiency or elevations in high-density lipoprotein cholesterol levels. Some mutations causing high-density lipoprotein deficiency are associated with premature coronary artery disease, whereas others, paradoxically, may be associated with longevity. Modulation of high-density lipoprotein metabolism for therapeutic purposes must take into account not only the cholesterol content of the particle but also its lipid (including phospholipid) composition, apolipoprotein content, size, and charge.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".