A Novel Alternative for the Treatment of Diabetic Foot Wounds:A Three Dimensional Porous Dermal Matrix
Bibliographic record
Abstract
Diabetic wounds tend to heal slowly and the healing process can be very complicated due to polymicrobial infection and heavy exudate formation which place patients at a higher risk for limb amputation [1].The investigations in wound care field revealed bioactive dermal matrices produced from a variety of proteins of extracellular matrix (ECM) have the ability to promote the healing process [2].In addition reduction/termination of the persistent inflammation and elimination of free radicals by the introduction of an antioxidant could be an important strategy to improve healing [3].Therefore a novel alternative for the treatment of diabetic foot wounds consisting of a three dimensional collagen-laminin porous dermal matrix impregnated with resveratrol (RSV)-loaded hyaluronic acid (HA) and dipalmitoylphosphatidylcholine (DPPC) microparticles was evaluated.Characterization, in vitro release, microbiological, ex vivo and in vivo studies were performed.Spherical microparticles of 30.2±0.3 μm were obtained with a RSV encapsulation efficacy of 98.7%.Scanning electron microscopy (SEM) and confocal laser scanning microscopy showed that particles were well dispersed in the dermal matrix from the surface to deeper layers.Collagenase degraded dermal matrix, however the addition of RSV loaded microparticles delayed the degradation time.The release of RSV was sustained and reached 70% after 6 h.Histological changes and antioxidant parameters in different treatment groups were investigated in full-thickness excision diabetic rat model.The highest healing score was obtained with the dermal matrix impregnated with RSV-microparticles with an increased antioxidant activity.Collagen-laminin dermal matrix with RSV microparticles can be an effective and safe option for the treatment of diabetic wounds requiring long recovery.
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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".