Arsenic trioxide alleviates atherosclerosis by inhibiting CD36-induced endocytosis and TLR4/NF-κB-induced inflammation in macrophage and ApoE-/- mice
Bibliographic record
Abstract
BACKGROUND: Inflammation and lipid accumulation are key events in atherosclerosis progression. Despite arsenic trioxide's (ATO) toxicity, at appropriate doses, it is a useful treatment for various diseases treatment. ATO prevents vascular restenosis; however, its effects on atherosclerotic plaque development and instability remain unclear. METHODS: mice were fed high-fat diet for 4 months, and starting at the third month, ATO was intravenously administered every other day. Atherosclerotic lesion size, histological characteristics, and related protein and lipid profiles were assessed using samples from the aorta, carotid artery, and serum. The anti-inflammatory and anti-pyroptosis effects of ATO were investigated by stimulating RAW264.7 and THP-1 cell lines with oxidized low-density lipoprotein (ox-LDL) or lipopolysaccharide (LPS). RESULTS: mice. In the serum and aortic plaques, ATO reduced the levels of pro-inflammatory factors, including interleukin (IL) 6 and tumor necrosis factor α, but increased IL-10 levels. Mechanistically, ATO promoted the CD36-mediated internalization of ox-LDL in a peroxisome proliferator-activated receptor γ-dependent manner. Furthermore, ATO downregulated Toll-like receptor 4 (TLR4) expression in plaques and macrophages and inhibited p65 nuclear translocation and IκBα degradation. ATO reduced macrophage pyroptosis by downregulating NLR family pyrin domain-containing 3 (NLRP3) expression and caspase 1 activation. CONCLUSION: ATO has potential atheroprotective effects, especially in macrophages. The mechanisms were inhibition of CD36-mediated foam cell formation and suppression of inflammatory responses and pyroptosis mediated by TLR4/nuclear factor κB and NLRP3 activation. Our findings provide evidence supporting the potential atheroprotective value of ATO.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".