Low molecular weight chitosan conjugated with folate for siRNA delivery in vitro: optimization studies
Bibliographic record
Abstract
The low transfection efficiency of chitosan is one of its drawbacks as a gene delivery carrier. Low molecular weight chitosan may help to form small-sized polymer-DNA or small interfering RNA (siRNA) complexes. Folate conjugation may improve gene transfection efficiency because of the promoted uptake of folate receptor-bearing cells. In the present study, chitosan was conjugated with folate and investigated for its efficacy as a delivery vector for siRNA in vitro. We demonstrate that the molecular weight of chitosan has a major influence on its biological and physicochemical properties, and very low molecular weight chitosan (below 10 kDa) has difficulty in forming stable complexes with siRNA. In this study, chitosan 25 kDa and 50 kDa completely absorbed siRNA and formed nanoparticles (≤220 nm) at a chitosan to siRNA weight ratio of 50:1. The introduction of a folate ligand onto chitosan decreased nanoparticle toxicity. Compared with chitosan-siRNA, folate-chitosan-siRNA nanoparticles improved gene silencing transfection efficiency. Therefore, folate-chitosan shows potential as a viable candidate vector for safe and efficient siRNA delivery.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".