Bibliographic record
Abstract
This review aims to provide an overview on the properties of high-density lipoproteins (HDLs) and their cardioprotective effects. Emergent HDL therapies will be presented in the context of the current understanding of HDL function, metabolism, and protective antiatherosclerotic properties. The epidemiological association between levels of HDL-C or its major apolipoprotein (apoA-I) is strong, graded, and coherent across populations. HDL particles mediate cellular cholesterol efflux, have antioxidant properties, and modulate vascular inflammation and vasomotor function and thrombosis. A link of causality has been cast into doubt with Mendelian randomization data suggesting that genes causing HDL-C deficiency are not associated with increased cardiovascular risk, nor are genes associated with increased HDL-C, with a protective effect. Despite encouraging data from small studies, drugs that increase HDL-C levels have not shown an effect on major cardiovascular end-points in large-scale clinical trials. It is likely that the cholesterol mass within HDL particles is a poor biomarker of therapeutic efficacy. In the present review, we will focus on novel therapeutic avenues and potential biomarkers of HDL function. A better understanding of HDL antiatherogenic functions including reverse cholesterol transport, vascular protective and antioxidation effects will allow novel insight on novel, emergent therapies for cardiovascular prevention.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".