Structural and Functional Validation of a Full-Thickness Self-Assembled Skin Equivalent for Disease Modeling
Bibliographic record
Abstract
Recently, various types of in vitro-reconstructed 3D skin models have been developed for drug testing and disease modeling. Herein, we structurally and functionally validated a self-assembled reconstructed skin equivalent (RSE) and developed an IL-17a-induced in vitro psoriasis-like model using a self-assembled RSE. The tissue engineering approach was used to construct the self-assembled RSE. The dermal layer was generated using fibroblasts secreting their own ECM, and the epidermal layer was reconstructed by seeding keratinocytes on the dermal layer. To generate the psoriatic model, IL-17A was added to the culture medium during the air-liquid interface culture period. Self-assembled RSE resulted in a fully differentiated epidermal layer, a well-established basement membrane, and dermal collagen deposition. In addition, self-assembled RSE was tested for 20 reference chemicals according to the Performance Standard of OECD TG439 and showed overall sensitivity, specificity, and accuracy of 100%, 90%, and 95%, respectively. The IL-17a-treated psoriatic RSE model exhibited psoriatic epidermal characteristics, such as epidermal hyperproliferation, parakeratosis, and increased expression of KRT6, KRT17, hBD2, and S100A9. Thus, our results suggest that a self-assembled RSE that structurally and functionally mimics the human skin has a great potential for testing various drugs or cosmetic ingredients and modeling inflammatory skin diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".