Production of Tissue-Engineered Skin Substitutes for Clinical Applications: Elimination of Serum
Bibliographic record
Abstract
Tissue-engineered skin substitutes (TESs) are used as a treatment for severe burn injuries. Their production requires culturing both keratinocytes and fibroblasts. The methods to grow these cells have evolved over the years, but bovine serum is still commonly used in the culture medium. Because of the drawbacks associated with the use of serum, it would be advantageous to use serum-free media for the production of TESs. In a previous study, we developed a serum-free medium (Surge SFM) for the culture of keratinocytes. Herein, we tested the use of this medium, together with a commercially available serum-free medium for fibroblasts (Prime XV), to produce serum-free TESs. Our results show that serum-free TESs are macroscopically and histologically similar to skin substitutes produced with conventional serum-containing media. TESs produced with either culture media expressed keratin 14, Ki-67, transglutaminase 1, filaggrin, type I and IV collagen, and fibronectin comparably. Mechanical properties, such as contraction and tensile strength, were comparable between TESs cultured with and without serum. Serum-free TESs were also successfully grafted onto athymic mice for a six-month period. In conclusion, Surge SFM and Prime XV serum-free media could be used to produce high quality clinical-grade skin substitutes.
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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".