Lipidomics of Bioactive Lipids in Acute Coronary Syndromes
Bibliographic record
Abstract
Acute coronary syndrome (ACS) refers to ischemic conditions that occur as a result of atherosclerotic plaque rupture and thrombus formation. It has been shown that lipid peroxidation may cause plaque instability by inducing inflammation, apoptosis, and neovascularization. There is some evidence showing that these oxidized lipids may have a prognostic value in ACS. For instance, higher levels of oxidized phospholipids on apo B-100 lipoproteins (OxPL/apoB) predicted cardiovascular events independent of traditional risk factors, C-reactive protein (hsCRP), and the Framingham Risk Score (FRS). A recent cross-sectional study showed that levels of oxylipins, namely 8,9-DiHETrE and 16-HETE, were significantly associated with cardiovascular and cerebrovascular events, respectively. They found that with every 1 nmol/L increase in the concentrations of 8,9-DiHETrE, the odds of ACS increased by 454-fold. As lipid peroxidation makes heterogonous pools of secondary products, therefore, rapid multi-analyte quantification methods are needed for their assessment. Conventional lipid assessment methods such as chemical reagents or immunoassays lack specificity and sensitivity. Lipidomics may provide another layer of a detailed molecular level to lipid assessment, which may eventually lead to exploring novel biomarkers and/or new treatment options. Here, we will briefly review the lipidomics of bioactive lipids in ACS.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".