Fabrication of KR-12 peptide-containing hyaluronic acid immobilized fibrous eggshell membrane effectively kills multi-drug-resistant bacteria, promotes angiogenesis and accelerates re-epithelialization
Bibliographic record
Abstract
Menglong Liu,1 Tengfei Liu,1 Xiaorong Zhang,1 Zhiwen Jian,2 Hesheng Xia,2 Jiacai Yang,1 Xiaohong Hu,1 Malcolm Xing,1,3 Gaoxing Luo,1 Jun Wu1,41Institute of Burn Research, State Key Laboratory of Trauma, Burn and Combined Injury, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, People’s Republic of China; 2State Key Laboratory of Polymer Materials Engineering, Polymer Research Institute of Sichuan University, Chengdu, 610065, People’s Republic of China; 3Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB R3T 2N2, Canada; 4Department of Burns, the First Affiliated Hospital, SunYat-Sen University, Guangzhou 510080, People’s Republic of ChinaBackground: Designing a wound dressing that effectively prevents multi-drug-resistant bacterial infection and promotes angiogenesis and re-epithelialization is of great significance for wound management.Methods and results: In this study, a biocompatible composite membrane comprising biomimetic polydopamine-modified eggshell membrane nano/microfibres coated with KR-12 antimicrobial peptide and hyaluronic acid (HA) was developed in an eco-friendly manner. The physicochemical properties of the composite membrane were thoroughly characterized, and the results showed that the surface hydrophilicity and water absorption ability of the composite membrane were improved after the successive conjugation of the HA and the KR-12 peptide. Furthermore, the in vitrobiological results revealed that the composite membrane had excellent antibacterial activity against Gram-positive Staphylococcus aureus, methicillin-resistant Staphylococcus aureus (MRSA) and Gram-negative Escherichia coli, and it could prevent MRSA biofilm formation on its surface. Additionally, it promoted the proliferation of keratinocytes and human umbilical vein endothelial cells and increased the secretion of VEGF. Finally, an in vivo animal study indicated that the composite membrane could promote wound healing via accelerating angiogenesis and re-epithelialization, which were demonstrated by the enhanced expression of angiogenetic markers (CD31 and VEGF) and keratinocyte proliferation marker (PCNA), respectively.Conclusion: These results indicated that the composite membrane is a potential candidate of wound dressingsKeywords: antimicrobial peptides, hyaluronic acid, fibrous eggshell membrane, antibacterial activity, angiogenesis, re-epithelialization
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".