Biomechanics of Wound Healing in an Equine Limb Model: Effect of Location and Treatment with a Peptide-Modified Collagen–Chitosan Hydrogel
Bibliographic record
Abstract
The equine distal limb wound healing model, characterized by delayed re-epithelialization and a fibroproliferative response to wounding similar to that observed in humans, is a valuable tool for the study of biomaterials poised for translation into both the veterinary and human medical markets. In the current study, we developed a novel method of biaxial biomechanical testing to assess the functional outcomes of healed wounds in a modified equine model and discovered significant functional and structural differences in both unwounded and injured skin at different locations on the distal limb that must be considered when using this model in future work. Namely, the medial skin was thicker and displayed earlier collagen engagement, medial wounds experienced a greater proportion of wound contraction during closure, and proximal wounds produced significantly more exuberant granulation tissue. Using this new knowledge of the equine model of aberrant wound healing, we then investigated the effect of a peptide-modified collagen-chitosan hydrogel on wound healing. Here, we found that a single treatment with the QHREDGS (glutamine-histidine-arginine-glutamic acid-aspartic acid-glycine-serine) peptide-modified hydrogel (Q-peptide hydrogel) resulted in a higher rate of wound closure and was able to modulate the biomechanical function toward a more compliant healed tissue without observable negative effects. Thus, we conclude that the use of a Q-peptide hydrogel provides a safe and effective means of improving the rate and quality of wound healing in a large animal model.
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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) | 0.001 | 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".