Advanced Oxidation Protein Products Exacerbates Lipid Accumulation and Atherosclerosis Through Downregulation of ATP-Binding Cassette Transporter A1 and G1 Expression in Apolipoprotein E Knockout Mice
Bibliographic record
Abstract
BACKGROUND: Both clinical data and basic science studies suggest that advanced oxidation protein products (AOPPs) may contribute to the progression of atherosclerosis. The aim of this study was to investigate the effects of AOPPs on ATP-binding cassette transporter (ABC) A1 and ABCG1 expression, lipid accumulation and atherosclerotic lesions in apolipoprotein E knockout (apoE-KO) mice. METHODS AND RESULTS: Male 8-week-old apoE-KO mice were fed a high-fat/high-cholesterol diet. Mice received intraperitoneal injections of AOPPs (5 mg/kg) and/or Janus Kinase (JAK) inhibitor AG-490 (5 mg/kg) once every other day for 8 weeks. As shown in our data, AOPPs increased lipid levels of plasma, and promoted advanced lesions in the aortic regions in apoE-KO mice. The ABCA1, ABCG1 and liver X receptor alpha (LXRα) expression were downregulated in apoE-KO mice treated with AOPPs, whereas the lesions in the aortas were decreased, and the ABCA1, ABCG1 and LXRα expression were upregulated in mice treated with AOPPs plus AG-490, compared to the mice treated with AOPPs only. The ABCA1 and LXRα expressions of aortas, liver and intestine were downregulated in the AOPPs group, while the expressions were upregulated in the AOPPs-plus-AG-490 group when compared to the AOPPs group. The same results can be also observed in peritoneal macrophages. CONCLUSIONS: AOPPs increase accumulation of lipids and exacerbate atherosclerosis through downregulation of ABCA1 and ABCG1 expression, and the JAK-LXRα signaling pathway in apoE-KO mice.
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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 | 0.001 |
| 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) | 0.002 | 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".