Low molecular weight chitosan nanoparticulate system at low N:P ratio for nontoxic polynucleotide delivery
Bibliographic record
Abstract
Chitosan, a natural polymer, is a promising system for the therapeutic delivery of both plasmid DNA and synthetic small interfering RNA. Reports attempting to identify the optimal parameters of chitosan for synthetic small interfering RNA delivery were inconclusive with high molecular weight at high amine-to-phosphate (N:P) ratios apparently required for efficient transfection. Here we show, for the first time, that low molecular weight chitosan (LMW-CS) formulations at low N:P ratios are suitable for the in vitro delivery of small interfering RNA. LMW-CS nanoparticles at low N:P ratios were positively charged (ζ-potential ~20 mV) with an average size below 100 nm as demonstrated by dynamic light scattering and environmental scanning electron microscopy, respectively. Nanoparticles were spherical, a shape promoting decreased cytotoxicity and enhanced cellular uptake. Nanoparticle stability was effective for at least 20 hours at N:P ratios above two in a slightly acidic pH of 6.5. At a higher basic pH of 8, these nanoparticles were unravelled due to chitosan neutralization, exposing their polynucleotide cargo. Cellular uptake ranged from 50% to 95% in six different cell lines as measured by cytometry. Increasing chitosan molecular weight improved nanoparticle stability as well as the ability of nanoparticles to protect the oligonucleotide cargo from nucleases at supraphysiological concentrations. The highest knockdown efficiency was obtained with the specific formulation 92-10-5 that combines sufficient nuclease protection with effective intracellular release. This system attained >70% knockdown of the messenger RNA, similar to commercially available lipoplexes, without apparent cytotoxicity. Contrary to previous reports, our data demonstrate that LMW-CS at low N:P ratios are efficient and nontoxic polynucleotide delivery systems capable of transfecting a plethora of cell lines.
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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".