Nanoparticles Encapsulated with LL37 and Serpin A1 Promotes Wound Healing and Synergistically Enhances Antibacterial Activity
Bibliographic record
Abstract
Wound care is a serious healthcare concern, often complicated by prolonged inflammation and bacterial infection, which contributes significantly to mortality and morbidity. Agents commonly used to treat chronic wound infections are limited due to toxicity of the therapy, multifactorial etiology of chronic wounds, deep skin infections, lack of sustained controlled delivery of drugs, and development of drug resistance. LL37 is an endogenous host defense peptide possessing antimicrobial activity and is involved in the modulation of wound healing. Serpin A1 (A1) is an elastase inhibitor and has been shown to demonstrate wound-healing properties. Hence, our goal was to develop a topical combination nanomedicine for the controlled sustained delivery of LL37 and A1 at precise synergistic ratio combinations that will significantly promote wound closure, reduce bacterial contamination, and enhance anti-inflammatory activity. We have successfully developed the first solid lipid nanoparticle (SLN) formulation that can simultaneously deliver LL37 and A1 at specific ratios resulting in accelerated wound healing by promoting wound closure in BJ fibroblast cells and keratinocytes as well as synergistically enhancing antibacterial activity against S. aureus and E. coli in comparison to LL37 or A1 alone.
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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".