Silver Sulfadiazine Encapsulated Polycaprolactone‐Zein Hybrid Nanofibers as a Wound Dressing
Bibliographic record
Abstract
ABSTRACT Recently, there has been a lot of interest in the incorporation of silver sulfadiazine (SSD) in nanofibrous polymer matrixes as an antibacterial wound dressing biomaterial. This study introduces the fabrication and characterization of a novel wound dressing made of polycaprolactone‐zein (PCL‐ZI) hybrid nanofibers loaded with different concentrations of SSD for wound regeneration applications. Successful formation of porous nanofibers and loading of SSD are confirmed by scanning electron microscopy (SEM) and Fourier‐transform infrared spectroscopy (FTIR), respectively. Nanofibers with a mean diameter ranging from 550 to 750 nm were fabricated. Hybrid nanofibers demonstrated better surface wettability than pure PCL nanofibers with desirable porosity (above 60%) and tensile strength of 0.92–3.02 MPa. Furthermore, our findings showed concentration‐dependent antibacterial properties and a prolonged release characteristic of SSD for up to 96 h. Antibacterial tests were used to assess the nanofibers' antibacterial ability versus Gram‐positive ( Staphylococcus aureus ) and Gram‐negative ( Escherichia coli ) bacterial strains. The results showed that SSD‐containing fibers well inhibited bacterial growth, indicating the bactericidal effect of SSD. In vitro cell studies indicated that high concentrations of SSD induced some degree of toxicity to the skin cells. As an example, after 7 days of cell culturing, in vitro findings showed that the incorporation of SSD up to 0.5 wt.% resulted in 97% cell viability compared to 83% viability with a high amount of SSD (1 wt.%). Taken together, the introduced antibacterial hybrid nanofiber scaffold herein holds great promise as a wound dressing for skin damage healing.
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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".