Lowering the n-6/n-3 PUFAs ratio inhibits the formation of THP-1 macrophage-derived foam cell
Bibliographic record
Abstract
BACKGROUND: The balance between n-6 and n-3 PUFAs is an important determinant in the risk for cardiovascular disease. The study was to investigate the influence of the n-6 and n-3 PUFAs ratio on the formation of THP-1 monocyte-derived foam cells and explore the probable mechanism of anti-atherosclerosis. METHODS: THP-1 monocyte cells were cultured with PMA and ox-LDL to establish a foam-cell model, while treated with different ratios of n-6 to n-3 PUFAs for 48 h. The cholesterol of foam cells was measured by a cholesterol assay kit. The levels of IL-6 and TNFα in supernatant were detected with ELISA methods. The expressions of CD36, ABCA1, ACAT1, PPARγ and LXRα mRNA were detected with real-time PCR. RESULTS: Compared with the foam cell model group, the low and middle ratio of n-6 to n-3 PUFAs groups decreased the intracellular concentration of cholesterol (P < 0.01), but the high n-6/n-3 PUFAs ratio did not. Fatty acids decreased the level of IL-6 and TNFα in supernatant in a ratio-dependent manner. Fatty acids treatment also decreased the expressions of CD36、ACTA1、PPARγ、LXRα mRNA in a ratio-dependent manner. CONCLUSIONS: Lowering the ratios of n-6 to n-3 PUFAs can decrease the secretion of inflammatory cytokines then reduce the expressions of CD36 and ACAT1 mRNA. As well, it can decrease the expressions of CD36 mRNA through the PPARγ pathway. This leads to less cholesterol ingestion into the cells and decreased synthesis of cholesteryl ester, which inhibits the formation of the foam cells, further preventing the occurrence and development of atherosclerosis.
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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 | 0.001 |
| 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".