Application of an instructive hydrogel accelerates re-epithelialization of xenografted human skin wounds
Bibliographic record
Abstract
Poor quality (eg. excessive scarring) or delayed closure of skin wounds can have profound physical and pyschosocial effects on patients as well as pose an enormous economic burden on the healthcare system. An effective means of improving both the rate and quality of wound healing is needed for all patients suffering from skin injury. Despite wound care being a multi-billion-dollar industry, effective treatments aimed at rapidly restoring the skin barrier function or mitigating the severity of fibrotic scar remain elusive. Previously, a hydrogel conjugated angiopoietin-1 derived peptide (QHREDGS; Q-peptide) was shown to increase keratinocyte migration and improve wound healing in diabetic mice. Here, we evaluated the effect of this Q-Peptide Hydrogel on human skin wound healing using a mouse xenograft model. First, we confirmed that the Q-Peptide Hydrogel promoted the migration of adult human keratinocytes and modulated their cytokine profile in vitro. Next, utilizing our human to mouse split-thickness skin xenograft model, we found improved healing of wounded human epidermis following Q-Peptide Hydrogel treatment. Importantly, Q-Peptide Hydrogel treatment enhanced this wound re-epithelialization via increased keratinocyte migration and survival, rather than a sustained increase in proliferation. Overall, these data provide strong evidence that topical application of QHREDGS peptide-modified hydrogels results in accelerated wound closure that may lead to improved outcomes for patients.
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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".