Chitosan nanoparticles: a versatile frontier in drug delivery and wound healing across multiple routes
Bibliographic record
Abstract
Abstract The domain of nanoscience has observed significant advancements over the former two decades. Researchers in nanomedicine field have been rigorously exploring the employment of natural biodegradable polymers for targeted drug delivery (TDD). Chitosan (CS), acquired from the deacetylation of chitin, is a naturally occurring amino polysaccharide, whose features of non-toxicity, prolonged retention time, biocompatibility, increased bioavailability, and biodegradability have hastened extensive study into diverse applications. The presence of amino and hydroxyl groups within CS is crucial for its noteworthy characteristics, comprising mucoadhesion, improvement of permeation, drug’s-controlled release, in situ gel preparation, and antimicrobial activity. CS nanoparticles (CS NPs) portray a safe and competent class of nanocarrier systems, demonstrating the controlled release of drugs and preciseness in TDD, and are found hopeful for treating wounds. However, safety concerns such as potential toxicity, immune response, and hemocompatibility must be carefully evaluated to ensure their suitability for clinical applications. This article explores the potential of CS NPs as versatile carriers for TDD, reporting essential challenges in both therapeutic domains, and progressing the advancement of innovative treatments. By connecting drug delivery and wound healing, our review addresses a critical convergence, fostering developments that can certainly affect treatment and recovery of patient. The initial part of the review will shed light on the extraction sources and notable attributes of CS. Additionally, we have presented recent research findings on how CS NPs are being utilized for drug delivery via different routes of administration. Further, we have endeavored to represent the latest investigations on the applications of CS NPs in wound healing.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".