Effect of n‐3 fatty acids and their derivatives on the expression of inflammatory genes (1034.15)
Bibliographic record
Abstract
Background: Uncontrolled inflammation participates in the development of chronic inflammatory diseases. Beneficial effects of the consumption of long chain n‐3 polyunsaturated fatty acids (PUFAs), mostly eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), on inflammation have been reported. Lipid mediators produced from EPA and DHA (resolvins) play a central role in the resolution of inflammation. Objective: To study the effects of EPA, DHA, resolvin D1 (RvD1) and resolvin D2 (RvD2) on the expression of inflammatory genes in cultured THP1 macrophages. Methods: Cells were incubated for 6h and 24h in the presence of n‐3 PUFAs (EPA, DHA; 100, 50, 25, 10 μM and resolvins (RvD1, RvD2; 1μM, 100, 50, 25, 10 nM). Total RNA was isolated and TNF‐alpha, IL‐6 and IL‐10 mRNA levels were analyzed by real‐time PCR. Results: In THP1 cells, EPA, DHA, RvD1 and RvD2 down‐regulated IL‐6 and TNF‐alpha gene expression, and up‐regulated IL‐10 expression in a time‐dependent manner with higher extent observed after 24h. TNF‐alpha, IL‐6 and IL‐10 gene expression demonstrated a dose‐dependent response following treatment with EPA and DHA (10, 25 and 50 μM). No association was found between gene expression and resolvins concentrations in dose‐response experiments. Conclusion: We demonstrate a time‐ and dose‐dependent regulation of TNF‐alpha, IL‐6 and IL‐10 gene expression levels in macrophages under EPA and DHA treatment. Grant Funding Source : NSERC
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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) | 0.004 | 0.001 |
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".