Advances in Skin Substitutes—Potential of Tissue Engineered Skin for Facilitating Anti-Fibrotic Healing
Bibliographic record
Abstract
Skin protects the body from exogenous substances and functions as a barrier to fluid loss and trauma. The skin comprises of epidermal, dermal and hypodermal layers, which mainly contain keratinocytes, fibroblasts and adipocytes, respectively, typically embedded on extracellular matrix made up of glycosaminoglycans and fibrous proteins. When the integrity of skin is compromised due to injury as in burns the coverage of skin has to be restored to facilitate repair and regeneration. Skin substitutes are preferred for wound coverage when the loss of skin is extensive especially in the case of second or third degree burns. Different kinds of skin substitutes with different features are commercially available; they can be classified into acellular skin substitutes, those with cultured epidermal cells and no dermal components, those with only dermal components, and tissue engineered substitutes that contain both epidermal and dermal components. Typically, adult wounds heal by fibrosis. Most organs are affected by fibrosis, with chronic fibrotic diseases estimated to be a leading cause of morbidity and mortality. In the skin, fibroproliferative disorders such as hypertrophic scars and keloid formation cause cosmetic and functional problems. Dermal fibroblasts are understood to be heterogeneous; this may have implications on post-burn wound healing since studies have shown that superficial and deep dermal fibroblasts are anti-fibrotic and pro-fibrotic, respectively. Selective use of superficial dermal fibroblasts rather than the conventional heterogeneous dermal fibroblasts may prove beneficial for post-burn wound healing.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".