Glucose Oxidase Incorporated Collagen Matrices for Dermal Wound Repair in Diabetic Rat Models: A Biochemical Study
Bibliographic record
Abstract
Impaired wound healing in diabetes is a well-documented phenomenon. Emerging data favor the involvement of free radicals in the pathogenesis of diabetic wound healing. We investigated the beneficial role of the sustained release of reactive oxygen species (ROS) in diabetic dermal wound healing. In order to achieve the sustained delivery of ROS in the wound bed, we have incorporated glucose oxidase in the collagen matrix (GOIC), which is applied to the healing diabetic wound. Our in vitro proteolysis studies on incorporated GOIC show increased stability against the proteases in the collagen matrix. In this study, GOIC film and collagen film (CF) are used as dressing material on the wound of streptozotocin-induced diabetic rats. A significant increase in ROS (p < 0.05) was observed in the fibroblast of GOIC group during the inflammation period compared to the CF and control groups. This elevated level up regulated the antioxidant status in the granulation tissue and improved cellular proliferation in the GOIC group. Interestingly, our biochemical parameters nitric oxide, hydroxyproline, uronic acid, protein, and DNA content in the healing wound showed that there is an increase in proliferation of cells in GOIC when compared to the control and CF groups. In addition, evidence from wound contraction and histology reveals faster healing in the GOIC group. Our observations document that GOIC matrices could be effectively used for diabetic wound healing therapy.
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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".