Effect of different concentrations of omega-3 fatty acids on stimulated THP-1 macrophages
Bibliographic record
Abstract
BACKGROUND: Inflammation plays a central role in chronic diseases occurring in the contemporary society. The health benefits of omega-3 (n-3) fatty acids (FAs), mostly eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), have been reported. However, their mechanisms of action are poorly understood. We explored dose and time effects of EPA, DHA, and a mixture of EPA + DHA on the expression of inflammatory genes in stimulated macrophages. METHODS: Lipopolysaccharide was used to stimulate human THP-1 macrophages. Cells were incubated in different conditions in the presence of n-3 FAs and LPS, and mRNA levels of inflammatory genes were measured by real-time PCR. Cytokine levels in culture media were measured. RESULTS: The mixture of EPA + DHA had a more effective inhibitory effect than either DHA or EPA alone, DHA being more potent than EPA. For both EPA and DHA, 75 μM of FAs had a more important anti-inflammatory effect than 10 or 50 μM. For gene expression, EPA had the greater action during the post-incubation (after LPS treatment) condition while DHA and EPA + DHA were more potent during the co-incubation (n-3 FAs and LPS). Cytokine concentrations decreased more markedly in the co-incubation condition. CONCLUSIONS: These results suggest that in stimulated macrophages, expression levels of genes involved in inflammation are influenced by the dose, the type of n-3 FAs, and the time of incubation.
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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) | 0.002 | 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".