HDL-C: clinical equipoise and vascular endothelial function
Bibliographic record
Abstract
Serum levels of HDL cholesterol represent a strong, and coherent cardiovascular risk marker seen across all populations, with higher levels of HDL cholesterol being associated with decreased incidence of coronary artery disease. The cardiovascular protective effects of HDL particles are attributed, in great part, to the ability of HDL particles to promote cellular cholesterol efflux from lipid-laden macrophages within the atherosclerotic plaque. HDL also has pleiotropic effects that protect the vascular wall, at least in vitro. These effects include potent anti-inflammatory and antioxidant properties and the modulation of vascular endothelial function. The mechanisms by which HDL exert their function on the vascular endothelium is dependent on HDL particle size, protein (proteome) and lipid (lipidome). The cooperative binding of HDL via SR-BI and G-coupled S1PR1-5 receptors mediates phosphorylation of endothelial nitric oxide synthase at residue 1177 through AKT signaling, preventing uncoupling of NADPH oxidation and nitric oxide synthesis and increasing endothelial nitric oxide synthase abundance. Furthermore, HDL can modulate the activation of NF-κB and the expression of cell adhesion molecules, an early step in endothelial dysfunction. In the present review the authors will focus on the controversies surrounding HDL, clinical treatments and vascular endothelial functions of HDL.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".