Optimization and integration of nanosilver on polycaprolactone nanofibrous mesh for bacterial inhibition and wound healing in vitro and in vivo
Bibliographic record
Abstract
Abstract: Bacterial infection is a major hurdle to wound healing, and the overuse of antibiotics have led to global issue, such as emergence of multidrug-resistant bacteria, even “super bacteria”. On the contrary, nanosilver (NS) can kill bacteria without causing resistant bacterial strains. In this study, NS was simply generated in situ on the polycaprolactone (PCL) nanofibrous mesh using an environmentally benign and mussel-inspired dopamine (DA). Scanning electron microscopy showed that NS uniformly formed on the nanofibers of PCL mesh. Fourier transform infrared spectroscopy revealed the step-by-step preparation of pristine PCL mesh, including DA coating and NS formation, which were further verified by water contact angle changing from hydrophobic to hydrophilic. To optimize the NS dose, the antibacterial activity of PCL/NS against Staphylococcus aureus , Escherichia coli and Acinetobacter baumannii was detected by bacterial suspension assay, and the cytotoxicity of NS was evaluated using cellular morphology observation and Cell Counting Kit-8 (CCK8) assay. Then, inductively coupled plasma atomic emission spectrometry exhibited that the optimized PCL/NS had a safe and sustained silver release. Moreover, PCL/NS could effectively inhibit bacterial infection in an infectious murine full-thickness skin wound model. As demonstrated by the enhanced level of proliferating cell nuclear antigen (PCNA) in keratinocytes and longer length of neo-formed epidermis, PCL/NS accelerated wound healing by promoting re-epithelialization via enhancing keratinocyte proliferation in infectious wounds. Keywords: polycaprolactone nanofibrous mesh, mussel inspired, nanosilver, anti-infection activity, wound healing, re-epithelialization
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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) | 0.000 | 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".