Preparation of Polyurethane/Pluronic F127 Nanofibers Containing Peppermint Extract Loaded Gelatin Nanoparticles for Diabetic Wounds Healing: Characterization, In Vitro, and In Vivo Studies
Bibliographic record
Abstract
Diabetic ulcer is regarded as one of the most prevalent chronic diseases. The healing of these ulcers enhances with the use of herbal extracts containing wound dressings with high antibacterial property and creating a nano-sized controlled release system. In this study, new peppermint extract was incorporated in the polyurethane- (PU-) based nanofibers for diabetic wound healing. The peppermint extract was used as an herbal antimicrobial and anti-inflammatory agent. The absorption ability of the wound dressing was enhanced by addition of F127 pluronic into the polymer matrix. The release of the extract was optimized by crosslinking the extract with gelatin nanoparticles (CGN) and their eventual incorporation into the nanofibers. The release of the extract was also controlled through direct addition of the extract into the PU matrix. The results showed that the release of extract from nanofibers was continued during 144 hours. The prepared wound dressing had a maximum absorption of 410.65% and an antibacterial property of 99.9% against Staphylococcus aureus and Escherichia coli bacteria. An in vivo study indicated on significant improving in wound healing after the use of the extract as an effective compound. On day 14, the average healing rate for samples covered by conventional gauze bandage, PU/F127, PU/F/15 (contained extract), and PU/F/15/10 (contained extract and CGN) prepared with different nanoparticle concentrations of 5 and 10 was 47.1 ± 0.2, 56.4 ± 0.4, 65.14 ± 0.2, and 90.55 ± 0.15%, respectively. Histopathological studies indicated that the wound treated with the extract containing nanofibers showed a considerable inflammation reduction at day 14. Additionally, this group showed more resemblance to normal skin with a thin epidermis presence of normal rete ridges and rejuvenation of skin appendages. Neovascularization and collagen deposition were higher in wounds treated with the extract containing nanofibrous wound dressing compared to the other groups.
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.
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) | 0.000 | 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 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".