Ultrafine chitosan nanoparticles as an efficient nucleic acid delivery system targeting neuronal cells
Bibliographic record
Abstract
BACKGROUND: Cell transfection with nanoscaled cationic polymeric particles using Chitosan has been extensively explored. Because of its properties such as cationic charges, biocompatibility, biodegradability, and low toxicity, it has been used as a potential gene, siRNA, protein (including antibodies), and drug carrier system. METHOD: This work describes the development of chitosan nanoparticles of a 20-nm diameter for a potential siRNA delivery application. The particles were prepared using an ionic gelation method, using sodium tripolyphosphate as a cross-linker. The effect of variation in pH was investigated on particle size and surface charge. Gene loading efficiency by chitosan nanoparticles was performed by varying weight ratios of chitosan: siRNA. Transfection efficiency was evaluated on Neuro2a cells. RESULTS: It was observed that 20-nm-sized nanoscale complexes induced significant transfection in neuronal cells. CONCLUSION: These particles have potential in the delivery of siRNA to neural tissues.
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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 | 0.000 |
| 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".