Modulation of the Lipid Profile of Reconstructed Skin Substitutes after Essential Fatty Acid Supplementation Affects Testosterone Permeability
Bibliographic record
Abstract
Skin models with efficient skin barrier function are required for percutaneous absorption studies. The contribution of media supplementation with n-3 and n-6 polyunsaturated fatty acids (PUFAs) to the development of the skin barrier function of in vitro skin models remains incompletely understood. To investigate whether PUFAs, alpha-linolenic acid (ALA, n-3 PUFA) and linoleic acid (LA, n-6 PUFA), could enhance the impermeability of a three-dimensional reconstructed human skin model, skin substitutes were produced according to the self-assembly method using culture media supplemented with either 10 μM ALA or 10 μM LA. The impact of PUFAs on skin permeability was studied by using a Franz cell diffusion system to assess the percutaneous absorption of testosterone and benzoic acid. Our findings showed that ALA supplementation induced a decrease in the absorption of testosterone, while LA supplementation did not significantly influence the penetration of testosterone and benzoic acid under present experimental conditions. Both ALA and LA were incorporated into phospholipids of the skin substitutes, resulting in an increase in n-3 total PUFAs or n-6 total PUFAs. Collectively, these results revealed the under-estimated impact of n-3 PUFA supplementation as well as the importance of the n-6 to n-3 ratio on the formation of the skin barrier of in vitro reconstructed human skin models.
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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".