Assessment of the Efficacy of an LL-37-Encapsulated Keratin Hydrogel for the Treatment of Full-Thickness Wounds
Bibliographic record
Abstract
Wound healing remains a burdensome healthcare problem due to moisture loss and bacterial infection. Advanced hydrogel dressings can help to resolve these issues by assisting and accelerating regenerative processes such as cell migration and angiogenesis because of the similarities between their composition and structure with natural skin. In this study, we aimed to develop a keratin-based hydrogel dressing and investigate the impact of the delivery of LL-37 antimicrobial peptide using this hydrogel in treating full-thickness rat wounds. Therefore, oxidized (keratose) and reduced (kerateine) keratins were utilized to prepare 10% (w/v) hydrogels with different ratios of keratose and kerateine. The mechanical properties of these hydrogels with compressive modulus of 6–32 kPa and tan δ <1 render them suitable for wound healing applications. Also, sustained release of LL-37 from the keratin hydrogel was achieved, which can lead to superior wound healing. In vitro studies confirmed that LL-37 containing 25:75% of keratose/kerateine (L-KO25:KN75) would result in significant fibroblast proliferation (∼85% on day 7), adhesion (∼90 cells/HPF), and migration (73% scratch closure after 12 h and complete closure after 24 h). Also, L-KO25:KN75 is capable of eradicating both Gram-negative and Gram-positive bacteria after 18 h. According to in vivo assessment of L-KO25:KN75, wound closure at day 21 was >98% and microvessel density (>30 vessels/HPF at day 14) was significantly superior in comparison to other treatment groups. The mRNA expression of VEGF and IL-6 was also increased in the L-KO25:KN75-treated group and contributed to proper wound healing. Therefore, the LL-37-containing keratin hydrogel ameliorated wound closure, and also angiogenesis was enhanced as a result of LL-37 delivery. These results suggested that the L-KO25:KN75 hydrogel could be a sustainable substitute for skin tissue regeneration in medical applications.
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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".