Electrospun Upconverting Nanofibrous Hybrids with Smart NIR-Light-Controlled Drug Release for Wound Dressing
Bibliographic record
Abstract
Chronic wounds present a high risk of infection due to delayed and incomplete healing, leading to increased health risks and financial burden to health-care systems. Numerous approaches to promote wound healing have been extensively explored, especially the development of effective wound dressing materials embedded with therapeutic drug molecules. Despite advances made in this area, a remaining challenge to be addressed is the controlled, on-demand release of therapeutic molecules using noncytotoxic stimulus, for example, near-infrared (NIR) excitation. Here, we report a platform that allows for the development of electrospun poly(vinyl alcohol) (PVA) fibrous hybrids embedded with upconverting nanoparticles (UCNPs) and UV-cleavable levofloxacin conjugates for wound dressings. Upon irradiation with NIR light, the excited UCNPs emit UV light around 365 nm, which can cleave the o -nitrobenzyl (ONB) linkage of the levofloxacin conjugates in the PVA fiber, leading to controlled drug release. The release was observed to be triggered only under NIR and UV irradiation, with no effect in the dark. Furthermore, the antibacterial effect against Escherichia coli and Staphylococcus aureus was successfully demonstrated, highlighting the versatility of the electrospun upconverting fiber platform. The development of antibacterial fibrous meshes with on-demand release of encapsulated drugs is imperative for precise treatment of wound infections.
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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".