Simvastatin inhibited oxLDL-induced proatherogenic effects through calpain-1–PPARγ–CD36 pathway
Bibliographic record
Abstract
We previously reported that simvastatin, an inhibitor of HMG-CoA reductase, inhibits atherosclerosis in rats. The present study was designed to investigate the effect of simvastatin on mouse peritoneal macrophage foam cell formation, the early feature of atherosclerosis, and explore its mechanisms. The results showed that simvastatin decreased cholesterol content and DiI–oxLDL (1,1′-didodecyl 3,3,3′,3′-indocarbocyanine perchlorate – oxidized low-density lipoprotein) uptake, reduced the levels of tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) in the medium, down-regulated the mRNA and protein expression of CD36 (a fatty acid receptor), and reduced the mRNA expressions of peroxisome proliferator-activated receptor gamma (PPARγ), TNF-α, and IL-6 in macrophages treated with oxLDL. However, PPARγ agonist troglitazone partly abolished the effects of simvastatin on foam cells. In addition, simvastatin reduced the protein expression of calpain-1, a Ca2+-sensitive cysteine protease, in oxLDL-treated macrophages. Furthermore, PD150606, a specific calpain inhibitor, reduced mRNA expressions of PPARγ and CD36 in macrophages treated with oxLDL. Combination of simvastatin and PD150606 had no further effect on mRNA expression of PPARγ and CD36 compared with either alone. However, over-expression of calpain-1 in macrophages partly reversed the simvastatin effects, including cell cholesterol content, mRNA expressions of PPARγ, and CD36. The results suggested that simvastatin inhibits foam cell formation of oxLDL-treated macrophages through a calpain-1–PPARγ–CD36 pathway.
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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) | 0.001 | 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 | 0.001 |
| 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".