Inhibition of macrophage‐derived foam cell formation by ezetimibe via the caveolin‐1/<scp>MAPK</scp> pathway
Bibliographic record
Abstract
Ezetimibe, a selective inhibitor of intestinal cholesterol absorption, effectively reduces plasma cholesterol, but its effect on atherosclerosis is unclear. Foam cell formation has been implicated as a key mediator during the development of atherosclerosis. The purpose of this study was to investigate the effects of ezetimibe on foam cell formation and explore the underlying mechanism. The results presented here show that ezetimibe reduces atherosclerotic lesions in apolipoprotein E deficient (apoE-/-) mice by lowering cholesterol levels. Treatment of macrophages with Chol:MβCD resulted in foam cell formation, which was concentration-dependently inhibited by the presence of ezetimibe. Mechanically, ezetimibe treatment downregulated the expression of CD36 and scavenger receptor class B1 (SR-B1), but upregulated the expression of apoE and caveolin-1 in macrophage-derived foam cells, which kept consistent with our microarray results. Moreover, treatment with ezetimibe abrogated the increase of phospho-extracellular signal regulated kinase (ERK) 1/2 and their nuclear accumulation in foam cells. Inhibition of the MAPK pathway by the MEK inhibitor PD98059 attenuated the inhibitory effect of ezetimibe on the expression of p-ERK1/2 and caveolin-1. Taken together, these results showed that ezetimibe suppressed foam cell formation via the caveolin-1/MAPK signalling pathway, suggesting that inhibition of foam cell formation might be a novel mechanism underlying the anti-atherosclerotic effect of ezetimibe.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".