Chitosan nanovectors for siRNA delivery: New horizons for nonviral gene therapy
Bibliographic record
Abstract
The growing interest in RNA-based therapeutics has positioned small interfering RNA (siRNA) as a promising tool for gene silencing with high specificity and efficacy. However, the successful clinical application of siRNA therapies requires efficient delivery systems to overcome extracellular and intracellular barriers. Chitosan, a naturally derived polysaccharide, has gained significant attention as a non-viral vector due to its biodegradability, biocompatibility, mucoadhesive properties, and capacity to enhance cellular uptake. These attributes make chitosan an attractive alternative to lipid-based nanoparticles, which currently dominate siRNA delivery platforms. Recent advancements in chitosan-based nanoformulations, including chemical modifications and functionalization strategies, have improved siRNA stability, targeting efficiency, and transfection potential, addressing key limitations such as low bioavailability and immunogenicity. Despite these advances, challenges remain in achieving optimal release kinetics, scalability, and consistent therapeutic efficacy. Future research efforts will focus on engineering chitosan derivatives with enhanced physicochemical properties, integrating multifunctional nanocarriers, and refining formulation strategies to bridge the gap between preclinical research and clinical translation. The continued development of chitosan-based siRNA therapeutics holds significant potential for advancing precision medicine and expanding treatment options for a variety of diseases, including cancer, metabolic disorders, and inflammatory conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".