Effect of n-3 fatty acids on the expression of inflammatory genes in THP-1 macrophages
Bibliographic record
Abstract
BACKGROUND: Uncontrolled inflammation participates in the development of inflammatory diseases. Beneficial effects of polyunsaturated fatty acids belonging to the n-3 family such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) on inflammation have been reported. The present study investigates the basal effects of EPA, DHA and a mixture EPA + DHA on the expression of 10 genes (AKT1, MAPK, NFKB, TNFA, IL1Β, MCP1, ALOX5, PTGS2, MGST1 and NOS2) related to inflammation in unstimulated cultured THP1 macrophages. Cells were incubated for 24 h with n-3 PUFAs (50 μM and 10 μM EPA, DHA, EPA + DHA). Expression levels of inflammatory genes were analyzed by real-time PCR. RESULTS: 50 μM, 10 μM EPA and 50 μM EPA + DHA decreased the expression of genes involved in the NF-κB pathway (MAPK, AKT1, and NFKB). Treatment with 50 μM, 10 μM EPA, 50 μM DHA and EPA + DHA decreased expression levels of cytokines genes IL1Β and MCP1. TNFA expression was decreased by 50 μM, 10 μM of EPA, DHA and with 50 μM EPA + DHA. Two genes involved in the fatty acid metabolism (PTGS2 and ALOX5) were also modulated by the n-3 FAs. 50 μM of DHA and EPA + DHA inhibited PTGS2 expression when the two concentrations of EPA, 50 μM DHA and EPA + DHA inhibited ALOX5 expression. Finally, the effects of n-3 FAs were studied among genes involved in the oxidative stress. 50 μM of each fatty acid increased MGST1 expression. Both concentration of EPA and 50 μM DHA decreased NOS2 expression. CONCLUSION: EPA seems to be more effective than DHA and EPA + DHA in modulating expression levels of selected inflammatory genes. The concentration of 50 μM was globally more effective than 10 μM.
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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) | 0.001 | 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".