Novel chitosan and bacterial cellulose biocomposites tailored with polymeric nanoparticles for modern wound dressing development
Bibliographic record
Abstract
Dressing biomaterials play a key role in wound management keeping a moisture medium and protecting against external factors. Natural and synthetic materials could be used as dressings where chitosan and bacterial cellulose is one of the most important solutions. These biopolymers have been used for wound dressing based on their non-toxic, biodegradable, and biocompatible features. In this study, biocomposites based on bacterial cellulose and chitosan membranes tailored with antimicrobial loaded poly(N-isopropylacrylamide)/polyvinyl alcohol nanoparticles were prepared. Core-shell polymeric nanoparticles, bacterial cellulose/chitosan membranes, and biocomposites were independently loaded with silver sulfadiazine, a well-known sulfonamide antibacterial agent used in the therapy of mild-to-moderate infections for sensitive organisms. The chemistry, structure, morphology, and size distribution were investigated by Fourier transformed infrared spectroscopy (FTIR-ATR), RAMAN spectroscopy, Scanning electron (SEM) and Transmission electron microscopy (TEM), and Dynamic light scattering (DLS). In vitro release behaviors of silver sulfadiazine from polymeric nanoparticles and biocomposites were investigated. The biological investigations revealed good biocompatibility of both the nanoparticles and the biocomposites in terms of human dermal fibroblasts viability and proliferation potential. Finally, the drug-loaded polymeric biomaterials showed promising characteristics, proving their high potential as an alternative support to develop a biocompatible and antibacterial wound dressing.
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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) | 0.001 | 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".