Therapeutic effects of Alirocumab on atherosclerosis: mechanisms and clinical applications
Bibliographic record
Abstract
Abstract. Lower low-density lipoprotein cholesterol (LDL-C) is one of the most critical steps to alleviate atherosclerosis. LDL-C accumulation in the intima of arteries leads to oxidized LDL and the formation of foam cells, followed by a core area of atherosclerotic plaques. Proprotein convertase subtilisin/keeping type 9 (PCSK9) can increase the concentration of LDL-C levels by lowering the density of LDL receptors. Alirocumab, as a monoclonal antibody, is a PCSK9 inhibitor that decreases LDL-C levels and reduces the prevalence of cardiovascular disease. However, the immune response issues and mechanism of action of Alirocumab, as well as the safety and efficacy of Alirocumab, remain to be elucidated. This review explains the mechanism of action and process of atherosclerosis, together with the present applications of Alirocumab as PCSK9 in atherosclerosis. PCSK9 inhibitors reduce the density of LDL receptors and treat the effects of various diseases on atherosclerosis. Furthermore, Alirocumab has shown a decrease in protein cholesterol levels or percentage of atherosclerotic volume through multiple clinical trials, including ODYSSEY, PACMAN-AMI, and ARCHITECT. Further mechanistic research and clinical trials are required to approve the therapeutic role and promote clinical applications of Alirocumab.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".