Injectable Chitosan‐Platelet‐Rich Plasma Hybrid Biomaterial Improves Skin Wound Healing in Diabetic Rats
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
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.
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 | 0.000 | 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) | 0.000 | 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