Chitosan Films Loaded with Alginate Nanoparticles for Gentamicin Release on Demand
Bibliographic record
Abstract
If untreated, skin wounds can lead to severe complications. Depending on the type of injury, long-term antibiotic administration is often required, and this decreases patient compliance. This limitation could be addressed by applying dressings capable of preventing infections by controlling drug release to the wound site. In this research, biodegradable wound dressings were investigated, based on natural polymers chitosan and alginate and incorporating the broad-spectrum gentamicin as antibiotic. Specifically, gentamicin was loaded into alginate nanoparticles, which were then loaded into chitosan-based films. This approach aimed at obtaining a system capable of modulating antibiotic release. The obtained nanoparticles had an average diameter of 86 nm and polydispersity index of 0.15. Antibiotic loading was around 600 µg/mg, with loading efficiency close to 100%. Films incorporating nanoparticles were compared to control films, which contained only gentamicin. Results showed that nanoparticles incorporation decreased film's swelling in phosphate buffer saline, thus leading to a decrease in burst release while cytocompatibility for human dermal fibroblasts was maintained. Antibacterial activity was confirmed against both gram-positive and gram-negative bacteria. Moreover, the antibiotic was released as a function of pH, with distinct behavior at pHs ranging from 7.4 to 5.5. This indicates that alginate nanoparticles dispersed in chitosan films effectively release gentamicin on demand.
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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".