Quercetin-Crosslinked Chitosan Films for Controlled Release of Antimicrobial Drugs
Bibliographic record
Abstract
Natural polymer-based films, due to their favorable biological and mechanical properties, have demonstrated great potential as coatings for biomedical applications. Among them, chitosan films have been widely studied both as coating materials and as controlled drug release systems. Crosslinkers are often used to tune chitosan’s crosslinking degree and thus to control the drug release kinetics. For this purpose, quercetin, a plant-derived natural polyphenol, has gained attention as a crosslinker, mainly for its intrinsic anti-inflammatory, antioxidant, and antibacterial features. In this study, chitosan films crosslinked with three different concentrations of quercetin (10, 20, and 30% w/w) have been used as controlled release systems for the delivery of the antibacterial drug trimethoprim (TMP, 10% w/w). Physicochemical and antimicrobial properties were investigated. Surface wettability and composition of the films were assessed by contact angle measurements, X-ray photoelectron spectroscopy (XPS), and Fourier-transform infrared spectroscopy (FTIR), respectively. The release kinetic of TMP in phosphate-buffered saline (PBS) and 2-(N-morpholino) ethanesulfonic acid (MES) was studied over time. Finally, antibacterial properties were assessed on E. coli and S. aureus through Kirby–Bauer disc diffusion and micro-dilution broth assays. Results show that quercetin, at the tested concentrations, clearly increases the crosslinking degree in a dose-dependent manner, thus influencing the release kinetic of the loaded TMP while maintaining its bactericidal effects. In conclusion, this work demonstrates that quercetin-crosslinked chitosan films represent a promising strategy for the design of antibiotic-releasing coatings for biomedical applications.
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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".