siRNA Delivery with Chitosan: Influence of Chitosan Molecular Weight, Degree of Deacetylation, and Amine to Phosphate Ratio on in Vitro Silencing Efficiency, Hemocompatibility, Biodistribution, and in Vivo Efficacy
Bibliographic record
Abstract
Chitosan (CS) shows in vitro and in vivo efficacy for siRNA delivery but with contradictory findings for incompletely characterized systems. For understanding which parameters produce effective delivery, a library of precisely characterized chitosans was produced at different degrees of deacetylation (DDAs) and average molecular weights ( M n ). Encapsulation and transfection efficiencies were characterized in vitro. Formulations were selected to examine the influence of M n and N:P ratio on nanoparticle uptake, metabolic activity, genotoxicity, and in vitro transfection. Hemocompatibility and in vivo biodistribution were then investigated for different M n, N:P ratios, and doses. Nanoparticle uptake and gene silencing correlated with increased surface charge, which was obtained at high DDA and high M n . A minimum polymer length of ∼60–70 monomers (∼10 kDa) was required for stability and knockdown. In vitro knockdown was equivalent to lipid control with no metabolic or genotoxicity. An inhibitory effect of serum on biological performance was dependent on DDA, M n, and N:P. In vivo biodistribution in mice show accumulation of nanoparticles in kidney with 40–50% functional knockdown.
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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 | 0.000 |
| 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".