Development of Radiosterilized Porcine Skin Electrosprayed with Silver Nanoparticles Prevents Infections in Deep Burns
Bibliographic record
Abstract
Extensive burns represent a significant challenge in biomedicine due to the multiple systemic and localized complications resulting from the major skin barrier loss. The functionalization of xenografts with nanostructured antibacterial agents proposes a fast and accessible application to restore barrier function and prevent localized bacterial contamination. Based on this, the objective of this work was to functionalize a xenograft by electrospray deposition with silver nanoparticles (AgNPs) and to evaluate its antibiofilm and cytotoxic effects on human fibroblasts. Initially, AgNPs were synthesized by a green microwave route with sizes of 2.1, 6.8, and 12.2 nm and concentrations of 0.055, 0.167, and 0.500 M, respectively. The AgNPs showed a size relationship directly proportional to the concentration of AgNO3, with a spherical and homogeneous distribution determined by high-resolution transmission electron microscopy. The surface functionalization of radiosterilized porcine skin (RPS) via electrospray deposition with the three AgNP concentrations (0.055, 0.167, and 0.500 M) in the epidermis and the dermis showed a uniform distribution on both surfaces by energy-dispersive X-ray spectroscopy. The antibiofilm assays of clinical multidrug-resistant Pseudomonas aeruginosa showed significant effects at the concentrations of 0.167 and 0.500 M, with a log reduction of 1.3 and 2.6, respectively. Additionally, viability experiments with human dermal fibroblasts (HDF) exposed to AgNPs released from functionalized porcine skin showed favorable tolerance, with retention of viability more significant than 90% for concentrations of 0.05 and 0.167 M after 24 h exposure. Antibacterial activity combined with excellent biocompatibility makes this biomaterial a candidate for antibacterial protection by inhibiting bacterial biofilms in deep burns during early stages of development.
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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".