Dynamic changes of the composition of plasma<scp>HDL</scp>particles in patients with cardiac disease: Spotlight on sphingosine‐1‐phosphate/serum amyloid A ratio
Bibliographic record
Abstract
Several epidemiological studies reported an inverse relationship between plasma high-density lipoprotein (HDL) cholesterol levels and atherosclerotic cardiovascular disease (ASCVD). However, therapeutic interventions targeted at raising HDL-cholesterol failed to improve cardiovascular outcomes, suggesting that HDL components distinct from cholesterol may account for the anti-atherothrombotic effects attributed to this lipoprotein. Sphingosine-1-phosphate (S1P) and the acute phase protein serum amyloid A (SAA) have been identified as integral constituents of HDL particles. Evidence suggests that S1P and SAA levels within HDL particles may be affected by inflammation and oxidative stress, which are coexisting processes underlying ASCVD. Because SAA, an inflammation-related marker, and S1P, an anti-atherothrombotic marker, have relatively clear opposite characteristics among the HDL-associated proteins, the approach of assessing the two markers simultaneously may provide new insights in clinical practice (S1P/SAA Index). This review focuses on evidence in support of the concept that the S1P/SAA Index may affect the HDL atheroprotective properties and may, therefore represent a potential target for therapeutic interventions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".