High-density lipoproteins: multifunctional vanguards of the cardiovascular system
Bibliographic record
Abstract
The plasma level of high-density lipoprotein (HDL)-cholesterol is inversely correlated with coronary artery disease, the leading cause of death worldwide. HDL 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 or hormone synthesis in steroidogenic tissues. HDL particles also act on vascular processes by modulating vasomotor function, thrombosis, cell-adhesion molecule expression, platelet function, nitric oxide release, endothelial cell apoptosis and proliferation. Many of these effects involve signal transduction pathways and gene transcription. Several genetic disorders of HDLs have been characterized at the molecular level. The study of naturally occurring mutations has considerably enhanced understanding of the role of HDL particles. Some mutations causing HDL deficiency are associated with premature coronary artery disease, while others, paradoxically, may be associated with longevity. Modulation of HDL metabolism for therapeutic purposes must take into account, not only the cholesterol content of a particle but its lipid (especially phospholipid) composition, apolipoprotein content, size and charge. Current therapeutic strategies include the use of peroxisome proliferating activator receptor-alpha agonists (fibrates) that increase apolipoprotein AI production and increase lipoprotein lipase activity, statins that have a small effect on HDL-cholesterol but markedly reduce low-density lipoprotein-cholesterol, the cholesterol/HDL-cholesterol ratio and niacin that increases HDL-cholesterol. Potential therapeutic targets include inhibition of cholesteryl ester transfer protein, modulating the ATP-binding cassette A1 transporter, and decreasing HDL uptake by scavenger receptor-B1. Novel therapies include injection of purified apolipoprotien AI and short peptides taken orally, mimicking some of the biological effects of apolipoprotein AI.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".