Amphotericin B- and Levofloxacin-Loaded Chitosan Films for Potential Use in Antimicrobial Wound Dressings: Analytical Method Development and Its Application
Bibliographic record
Abstract
Levofloxacin (LVX) and amphotericin B (AMB) have been widely used to treat bacterial and fungal infections in the clinic. Herein, we report, for the first time, chitosan films loaded with AMB and LVX as wound dressings to combat antimicrobial infections. Additionally, we developed and validated a high-performance liquid chromatography (HPLC) method coupled with a UV detector to simultaneously quantify both AMB and LVX. The method is easy, precise, accurate and linear for both drugs at a concentration range of 0.7–5 µg/mL. The validated method was used to analyse the drug release, ex vivo deposition and permeation from the chitosan films. LVX was released completely from the chitosan film after a week, while approximately 60% of the AMB was released. Ex vivo deposition study revealed that, after 24-hour application, 20.96 ± 13.54 µg of LVX and approximately 0.35 ± 0.04 µg of AMB was deposited in porcine skin. Approximately 0.58 ± 0.16 µg of LVX permeated through the skin. AMB was undetectable in the receptor compartment due to its poor solubility and permeability. Furthermore, chitosan films loaded with AMB and LVX were found to be able to inhibit the growth of both Candida albicans and Staphylococcus aureus, indicating their potential for antimicrobial 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".