Advances in Skin Substitutes—Potential of Tissue Engineered Skin for Facilitating Anti-Fibrotic Healing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 it