A 3D-psoriatic skin model for dermatological testing: The impact of culture conditions
Bibliographic record
Abstract
Background Inadequate representation of the human tissue environment during a preclinical screen can result in inaccurate predictions of compound effects. Consequently, pharmaceutical investigators are searching for preclinical models that closely resemble original tissue for predicting clinical outcomes. Methods The current research aims to compare the impact of using serum-free medium instead of complete culture medium during the last step of psoriatic skin substitute reconstruction. Skin substitutes were produced according to the self-assembly approach. Results Serum-free conditions have no negative impact on the reconstruction of healthy or psoriatic skin substitutes presented in this study regarding their macroscopic or histological appearances. ATR-FTIR results showed no significant differences in the CH 2 bands between psoriatic substitutes cultured with or without serum, thus suggesting that serum deprivation did not have a negative impact on the lipid organization of their stratum corneum . Serum deprivation could even lead to a better organization of healthy skin substitute lipids. Percutaneous analyses demonstrated that psoriatic substitutes cultured in serum-free conditions showed a higher permeability to hydrocortisone compared to controls, while no significant differences in benzoic acid and caffeine penetration profiles were observed. Conclusions Results obtained with this 3D-psoriatic skin substitute demonstrate the potential and versatility of the model. It could offer good prediction of drug related toxicities at preclinical stages performed in order to avoid unexpected and costly findings in the clinic. General significance Together, these findings offer a new approach for one of the most important challenges of the 21st century, namely, prediction of drug toxicity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".