Injectable Chitosan‐Platelet‐Rich Plasma Hybrid Biomaterial Improves Skin Wound Healing in Diabetic Rats
Bibliographic record
Abstract
ABSTRACT Diabetic foot ulcers are chronic wounds with poor healing outcomes, partly due to protease‐rich microenvironments that degrade regenerative cues. In this 28‐day study, a hybrid biomaterial combining fresh leukocyte‐rich platelet‐rich plasma with freeze‐dried chitosan (CS‐PRP) is used to treat full‐thickness skin excisional wounds in streptozotocin‐induced diabetic rats. CS‐PRP coagulates rapidly and chitosan remains detectable in the wound bed up to Day 28. Compared to control, CS‐PRP significantly accelerates wound closure throughout the study, including at Day 7 (52% vs. 37%, p < 0.001), with a more complete epidermal restoration. In addition, histological scoring reveals higher tissue quality in treated wounds at Day 28 (14.8±0.4 vs. 13.7±0.8, p < 0.01), with improved dermal reorganization. CS‐PRP enhances collagen deposition compared to control (59% vs. 24%, p < 0.001) and maturation while sustaining higher vascular density relative to native skin in all treated animals (1.1 to 3.1‐fold, p < 0.01) at Day 28. CS‐PRP supports diabetic wound healing across multiple tissue compartments. Indentation‐based mapping generates detailed spatial profiles of skin thickness and elasticity, which clearly highlight wound‐induced mechanical disruption but reveal no significant treatment‐related improvement. The simplicity, injectability, and biological activity of CS‐PRP position this product as a promising approach to enhance wound healing in diabetic skin.
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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 | 0.001 |
| 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".