Eicosapentaenoic acid reduces the proportion of IL-17A–producing T cells in a 3D psoriatic skin model
Bibliographic record
Abstract
Psoriasis is a skin disease presenting as erythematous lesions with accentuated proliferation of epidermal keratinocytes, infiltration of leukocytes, and dysregulated lipid metabolism. T cells play essential roles in the disease. n-3 polyunsaturated fatty acids are anti-inflammatory metabolites, which exert an immunosuppressive effect on healthy T cells. However, the precise mechanistic processes of n-3 polyunsaturated fatty acids on T cells in psoriasis are still unrevealed. In this study, we aimed to evaluate the action of eicosapentaenoic acid (EPA) on T cells in a psoriatic skin model produced with T cells. A coculture of psoriatic keratinocytes and polarized T cells was prepared using culture media, which was either supplemented with 10 μM EPA or left unsupplemented. Healthy and psoriatic skin substitutes were produced according to the self-assembly method. In the coculture model, EPA reduced the proportion of IL-17A-positive cells, while increasing that of FOXP3-positive cells, suggesting an increase in the polarization of regulatory T cells. In the 3D psoriatic skin model, EPA normalized the proliferation of psoriatic keratinocytes and diminished the levels of IL-17A. The expression of the proteins of the signal transducer and activator of transcription was influenced following EPA supplementation with downregulation of the phosphorylation levels of signal transducer and activator of transcription 3 in the dermis. Finally, the NFκB signaling pathway was modified in the EPA-supplemented substitutes with an increase in Fas amounts. Ultimately, our results suggest that in this psoriatic model, EPA exerts its anti-inflammatory action by decreasing the proportion of IL-17A-producing T cells.
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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 | 0.001 |
| 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 | 0.002 |
| 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".