Electrospun cellulose acetate/gelatin nanofibrous wound dressing containing berberine for diabetic foot ulcer healing: in vitro and in vivo studies
Bibliographic record
Abstract
Abstract Functional wound dressing with tailored physicochemical and biological properties is vital for diabetic foot ulcer (DFU) treatment. Our main objective in the current study was to fabricate Cellulose Acetate/Gelatin (CA/Gel) electrospun mat loaded with berberine (Beri) as the DFU-specific wound dressing. The wound healing efficacy of the fabricated dressings was evaluated in streptozotocin-induced diabetic rats. The results demonstrated an average nanofiber diameter of 502 ± 150 nm, and the tensile strength, contact angle, porosity, water vapor permeability and water uptake ratio of CA/Gel nanofibers were around 2.83 ± 0.08 MPa, 58.07 ± 2.35°, 78.17 ± 1.04%, 11.23 ± 1.05 mg/cm 2 /hr, and 12.78 ± 0.32%, respectively, while these values for CA/Gel/Beri nanofibers were 2.69 ± 0.05 MPa, 56.93 ± 1°, 76.17 ± 0.76%, 10.17 ± 0.21 mg/cm 2 /hr, and 14.37 ± 0.42%, respectively. The antibacterial evaluations demonstrated that the dressings exhibited potent antibacterial activity. The collagen density of 88.8 ± 6.7% and the angiogenesis score of 19.8 ± 3.8 obtained in the animal studies indicate a proper wound healing. These findings implied that the incorporation of berberine did not compromise the physical properties of dressing, while improving the biological activities. In conclusion, our results indicated that the prepared mat is a proper wound dressing for DFU management and treatment.
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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".