The Role of High-Density Lipoprotein in Cardiovascular Disease: Benefits, Functions, and Treatments
Bibliographic record
Abstract
There is evidence that high-density lipoprotein (HDL) can prevent the development of cardiovascular disease, particularly atherosclerotic. The beneficial effects of HDL on the arterial wall are largely due to its rich function. For example, one of the essential components of HDL's atherosclerotic protective characteristics is reverse cholesterol transport (RCT). The low-density lipoprotein (LDL) oxidation risk may be decreased attributable to the anti-oxidation function of HDL, which would also decrease the possibility of atherosclerosis. In recent years, the anti-inflammatory function of HDL has been demonstrated to modulate and participate in several inflammatory phenomena of atherosclerotic plaque formation. Since various functional features of HDL have gradually shown to have positive effects on the treatment and prevention of atherosclerotic plaque development, a growing number of researchers are working to develop effective strategies to boost HDL. Fortunately, several treatment options have developed to increase HDL levels, including drugs and dietary patterns. This article will focus on the functional properties of HDL levels and several therapeutic approaches to raise HDL levels.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".