Production of a Bilayered Self-Assembled Skin Substitute Using a Tissue-Engineered Acellular Dermal Matrix
Bibliographic record
Abstract
Our bilayered self-assembled skin substitutes (SASS) are skin substitutes showing a structure and functionality very similar to native human skin. These constructs are used, in life-threatening burn wounds, as permanent autologous grafts for the treatment of such affected patients even though their production is exacting. We thus intended to shorten their current production time to improve their clinical applicability. A self-assembled decellularized dermal matrix (DM) was used. It allowed the production of an autologous skin substitute from patient's cells. The characterization of SASS reconstructed using a decellularized dermal matrix (SASS-DM) was performed by histology, immunofluorescence, transmission electron microscopy, and uniaxial tensile analysis. Using the SASS-DM, it was possible to reduce the standard production time from about 8 to 4 and a half weeks. The structure, cell differentiation, and mechanical properties of the new skin substitutes were shown to be similar to the SASS. The decellularization process had no influence on the final microstructure and mechanical properties of the DM. This model, by enabling the production of a skin substitute in a shorter time frame without compromising its intrinsic tissue properties, represents a promising addition to the currently available burn and wound treatments.
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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".