Infection control with antimicrobial solid-state ZnO nanoparticles on silk fibroin gauze
Bibliographic record
Abstract
Abstract Traditional antibiotic treatments for wound infections pose risks associated with microbial resistance, necessitating the exploration of innovative approaches such as nanoparticles as the next generation of antibiotics. In this study, we present a paradigm shift approach for acute and chronic wound care by developing an active wound dressing capable of protecting and eradicating bacteria from the injury site. The focus of this research is on the electroless deposition of large zinc oxide nanoparticles (ZnO NPs) onto spined silk fibroin gauze, targeting a particle size range of approximately 200 nm to minimize cytotoxity. The biocompatibility and antimicrobial efficacy of the ZnO NP-embedded silk wound dressing were evaluated against gram-positive (Staphylococcus aureus) and gram-negative (Pseudomonas aeruginosa) bacteria. The results demonstrate that the ZnO NPs integrated within the silk wound dressing exhibit biocompatibility with 70% cell viability and control microorganism growth against S. aureus and P. aeruginosa, gradually from first 24 hours of exposure. By targeting larger particle size, only the release of a substantial amount of zinc ions were released without generating toxic reactive oxygen species (ROS) that could harm both bacteria and cells. These findings underscore the therapeutic potential of utilizing bioresorbable wound dressings functionalized with large ZnO NPs, thus revolutionizing the landscape of clinical wound care.
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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".