N-eicosapentaenoyl-ethanolamine decreases the proliferation of psoriatic keratinocytes in a reconstructed psoriatic skin model
Bibliographic record
Abstract
Abstract Psoriasis is an inflammatory skin disease that is characterized by keratinocyte hyperproliferation, abnormal epidermal differentiation and dysregulated lipid metabolism. Some lipid mediators of the N-acylethanolamines (NAEs) and monoacylglycerols (MAGs) can bind to cannabinoid (CB) receptors and are referred to as part of the endocannabinoidome. Their implication in psoriasis remains unknown. The aim of the present study was to characterize the endocannabinoid system and evaluate the effects of n-3-derived NAEs, namely N-eicosapentaenoyl-ethanolamine (EPEA), in psoriatic keratinocytes using a psoriatic skin model produced by tissue engineering, following the self-assembly method. Psoriatic skin substitutes had lower FAAH2 expression and higher MAGL, ABHD6 and ABHD12 expression compared with healthy skin substitutes. Treatments with alpha-linolenic acid (ALA) increased the levels of EPEA and 1/2-docosapentaenoyl-glycerol, showing that levels of n-3 polyunsaturated fatty acids modulate related NAE and MAG levels. Treatments of the psoriatic substitutes with 10 μM of EPEA for 7 days resulted in decreased epidermal thickness and number of Ki67 positive keratinocytes, both indicating decreased proliferation of psoriatic keratinocytes. EPEA effects on keratinocyte proliferation were inhibited by the CB1 receptor antagonist rimonabant. Exogenous EPEA also diminished some inflammatory features of psoriasis. In summary, n-3-derived NAEs can reduce the psoriatic phenotype of a reconstructed psoriatic skin model.
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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 | 0.000 |
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".