Janus Films Wound Dressing Comprising Electrospun Gelatin/PCL Nanofibers and Gelatin/Honey/Curcumin Thawed Layer
Bibliographic record
Abstract
A promising approach for wound treatment is using multilayer wound dressings that offer multifunctional properties. In this study, a bilayered electrospun/hydrogel gelatin-based scaffold integrated with honey and curcumin was developed to treat wounds under an in vivo study. The first layer consisted of an enzymatic cross-linked gelatin hydrogel containing honey and curcumin, which gelatin/PCL nanofibers reinforced. The physicochemical, mechanical, and biological properties of both layers were evaluated. Then, the bilayered wound dressing was compared to a commercial wound dressing in an in vivo study. The results showed that this strategy provided the wound dressing with a strength of 40 MPa, 70% elongation, 800% swelling rate, and 8 g/h/m 2 water vapor permeability. Furthermore, MTT and histopathological staining demonstrated that the bilayered wound dressing promoted wound closure accelerated collagen production and tissue granulation, and promoted immune system response and re-epithelialization compared to other groups. The presence of a nanofibrous layer on the surface of the wound dressing facilitated its use, and the inclusion of honey and gelatin in the hydrogel layer prevented adhesion to the wound tissue and allowed for easy replacement without damaging the wound bed. Overall, the bilayered dressing with multifunctional properties holds great potential for developing wound dressings.
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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".