Anti-Atherosclerotic Molecules Targeting Oxidative Stress and Inflammation
Bibliographic record
Abstract
The accumulation of lipids within arteries remains to be the initial impulse for the pathogenesis of atherosclerosis; however, both inflammation and oxidative stress are considered to play a critical role in this process. Several lipid lowering drugs are used as the first line therapy in atherosclerosis; however, different agents have been found to exhibit beneficial effects which are independent of their lipid lowering activity. Both statins and fibrates have been reported to exert anti-inflammatory and anti-oxidative effects in addition to their anti-atherosclerotic actions. Furthermore, anti-hypertensive, anti-diabetic and anti-platelet drugs, which reduce oxidative stress and inflammation, have been shown to attenuate atherosclerosis. In addition, novel substances such as HDL-related agents, cyclopentenone prostaglandins, lipoprotein-associated phospholipase A(2) inhibitors, 5-lipoxygenase pathway inhibitors, acyl CoA: cholesterol acyltransferase inhibitors, analogues of probucol and lysophosphatidic acid antagonists have been developed for the treatment of atherosclerosis as a consequence of their actions on oxidative stress and inflammation. The present article reviews the involvement of inflammation and oxidative stress in the pathogenesis of atherosclerosis and focuses on the mechanisms of some clinically used as well as potential anti-atherosclerotic substances with anti-inflammatory and anti-oxidative properties.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".