Alpha-Linolenic Acid Modulates T Cell Incorporation in a 3D Tissue-Engineered Psoriatic Skin Model
Bibliographic record
Abstract
Psoriasis is an autoimmune skin disease with an increased number of leukocytes infiltrating the dermal and epidermal compartments compared with normal skin. N-3 polyunsaturated fatty acids (n-3 PUFAs) are frequently used in the clinic in order to attenuate the symptoms of psoriasis. For psoriatic patients, a supplementation of the diet with alpha-linolenic acid (ALA) reduces the activation of T cell signaling pathways, leading to a significant reduction in inflammatory cytokine secretion. However, the precise mechanism of action of n-3 PUFAs in psoriasis is still not understood. In the present study, we elucidated the bioaction of ALA on the adaptive immune component of psoriasis by using a psoriatic skin model produced with the addition of activated T cells. Healthy and psoriatic skin substitutes were produced according to the self-assembly method, using culture media supplemented with 10 μM of ALA. T cells were isolated from blood samples using a negative selection isolation method. ALA supplementation regulated the hyperproliferation and abnormal cell differentiation of psoriatic keratinocytes stimulated by T cells. Additionally, the exogenous ALA was correctly incorporated into the phospholipids of keratinocytes, which resulted in increased levels of ALA, eicosapentaenoic acid (EPA) and n-3 docosapentaenoic acid (n-3 DPA). The infiltration of T cells into the epidermis was reduced when ALA was added to the culture medium, and significant decreases in the levels of inflammatory cytokines and chemokines such as CXCL1, interleukin-6 (IL-6) and interleukin-8 (IL-8) were consequently measured in psoriatic substitutes supplemented with this n-3 PUFA. Altogether, our results showed that in this psoriatic skin model enriched with T cells, ALA exerted its beneficial effect by decreasing the quantities of inflammatory mediators released by T cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".