An <i>In-Situ</i> Forming Skin Substitute Improves Healing Outcome in a Hypertrophic Scar Model
Bibliographic record
Abstract
Wound repair requires a sequential series of biological events that begins with the deposition of a temporary scaffold within which cells can repair the skin. Without a scaffold, repair is essentially impossible. Aberrant wound healing, such as hypertrophic scarring or nonhealing, has a tremendous burden on healthcare and quality of life. Timely wound closure dramatically reduces the risk of infection and scarring. Cellular skin substitutes are opportune to meet this need. Our goal was to create an in-situ forming scaffold that can be easily combined with cells to rapidly form a dermal substitute within the wound bed. In this study, we evaluated the application of a polyvinyl alcohol-collagen-glycosaminoglycan-based biohybrid scaffold system in full-thickness wounds on a rabbit fibrotic ear model. Punch wounds (6 mm) were either untreated or filled with an acellular scaffold, a scaffold containing xenofibroblasts, or a scaffold containing xenofibroblasts expressing indoleamine 2,3-dioxygenase (IDO). Results demonstrated that (1) both acellular and IDO-expressing fibroblast in-situ forming scaffolds significantly reduced scar elevation index (1.24±0.05 and 1.25±0.03; p<0.05) and improved overall healing quality compared with xenofibroblast scaffolds and untreated wounds; (2) IDO-expressing fibroblast scaffolds significantly reduced T-cell infiltration into the scaffold-engrafted area (p<0.05); and (3) both IDO-expressing and acellular in-situ forming scaffolds demonstrated increased vessel-like and nerve-like structures (p<0.05). The results demonstrated that the use of the in-situ forming scaffold, and even more so when delivering IDO-expressing cells, improved healing outcome in full-thickness hypertrophic rabbit ear wounds.
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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".