Advances in using chitosan-based nanoparticles for<i>in vitro</i>and<i>in vivo</i>drug and gene delivery
Bibliographic record
Abstract
IMPORTANCE OF THE FIELD: This review aims to provide an overview of state-of-the-art chitosan-based nanosized carriers for the delivery of therapeutic agents. Chitosan nanocarriers are smart delivery systems owing to the possibility of their property alterations with various approaches, which would confer them with the possibility of spatiotemporal delivery features. AREAS COVERED IN THIS REVIEW: The focus of this review is principally on those aspects that have not often been addressed in other reviews. These include the influence of physicochemical properties of chitosan on delivery mechanisms and chitosan modification with a variety of ligand moieties specific for cell surface receptors to increase recognition and uptake of nanocarriers into cells through receptor-mediated endocytosis. Multiple examples that demonstrate the advantages of chitosan-based nanocarriers over other delivery systems of therapeutic agents are highlighted. Particular emphasis is given to the alteration of material properties by functionalization or combination with other polymers for their specific applications. Finally, structural and experimental parameters influencing transfection efficiency of chitosan-based nanocarriers are presented for both in vitro and in vivo gene delivery. WHAT THE READER WILL GAIN: The readers will acquire knowledge of parameters influencing the properties of the chitosan-based nanocarriers for delivery of therapeutic agents (genetic material or drugs) in vitro and in vivo. They will get a better idea of the strategies to be adapted to tune the characteristics of chitosan and chitosan derivatives for specific delivery applications. TAKE HOME MESSAGE: Chitosan is prone to chemical and physical modifications, and is very responsive to environmental stimuli such as temperature and pH. These features make chitosan a smart material with great potential for developing multifunctional nanocarrier systems to deliver large varieties of therapeutic agents administrated in multiple ways with reduced side effects.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".