Hyaluronic Acid (HA)‐Based Silk Fibroin/Zinc Oxide Core–Shell Electrospun Dressing for Burn Wound Management
Bibliographic record
Abstract
Burn injuries represent a major life-threatening event that impacts the quality of life of patients, and places enormous demands on the global healthcare systems. This study introduces the fabrication and characterization of a novel wound dressing made of core-shell hyaluronic acid-silk fibroin/zinc oxide (ZO) nanofibers for treatment of burn injuries. The core-shell configuration enables loading ZO-an antibacterial agent-in the core of nanofibers, which in return improves the sustained release of the drug and maintains its bioactivity. Successful formation of core-shell nanofibers and loading of zinc oxide are confirmed by transmission electron microscopy, Fourier-transform infrared spectroscopy, and energy dispersive X-ray. The antibacterial activity of the dressings are examined against Escherichia coli and Staphylococcus aureus and it is shown that addition of ZO improves the antibacterial property of the dressing in a dose-dependent fashion. However, in vitro cytotoxicity studies show that high concentration of ZO (>3 wt%) is toxic to the cells. In vivo studies indicate that the wound dressings loaded with ZO (3 wt%) substantially improves the wound healing procedure and significantly reduces the inflammatory response at the wound site. Overall, the dressing introduced herein holds great promise for the management of burn injuries.
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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".