Bibliographic record
Abstract
Cholesterol is transported around the body in the form of lipoprotein (lipid/protein) complexes, because it is almost insoluble in water. High-density lipoprotein (HDL) particles transport cholesterol from tissues back to the liver for excretion. Epidemiological studies have shown an inverse relationship between blood levels of HDL-cholesterol (HDL-c) and the incidence of clinically signifi cant atherosclerosis. The benefi cial effects of HDL in altering atherosclerotic disease are believed to involve elevated levels of HDL enhancing the effl ux of cholesterol from arterial walls, increasing transport of cholesterol from arteries to the liver for excretion. This reverse cholesterol transport (RCT) pathway is used to explain both HDL's role in lipid metabolism and the inverse association between HDL-c plasma concentration and the risk of cardiovascular disease. Based on the RCT model, ApoA-I is an attractive target for therapeutic intervention. Experimental manipulations to increase production of ApoA-I have been associated with reduced atherogenicity. There is a continuing need for novel therapies that increase the biosynthesis of HDL, to inhibit the progression of and even bring about regression of atherosclerosis. Small molecule compounds that increase the production of endogenous ApoA-I would be attractive therapeutic agents for treating dyslipidemias.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".