Recent Applications of Chitosan and Its Derivatives in Antibacterial, Anticancer, Wound Healing, and Tissue Engineering Fields
Bibliographic record
Abstract
Chitosan, a versatile biopolymer derived from chitin, has garnered significant attention in various biomedical applications due to its unique properties, such as biocompatibility, biodegradability, and mucoadhesiveness. This review provides an overview of the diverse applications of chitosan and its derivatives in the antibacterial, anticancer, wound healing, and tissue engineering fields. In antibacterial applications, chitosan exhibits potent antimicrobial properties by disrupting microbial membranes and DNA, making it a promising natural preservative and agent against bacterial infections. Its role in cancer therapy involves the development of chitosan-based nanocarriers for targeted drug delivery, enhancing therapeutic efficacy while minimising side effects. Chitosan also plays a crucial role in wound healing by promoting cell proliferation, angiogenesis, and regulating inflammatory responses. Additionally, chitosan serves as a multifunctional scaffold in tissue engineering, facilitating the regeneration of diverse tissues such as cartilage, bone, and neural tissue by promoting cell adhesion and proliferation. The extensive range of applications for chitosan in pharmaceutical and biomedical sciences is not only highlighted by the comprehensive scope of this review, but it also establishes it as a fundamental component for forthcoming research in biomedicine.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".