A novel method for synthesizing PEGylated chitosan nanoparticles: strategy, preparation, and in vitro analysis
Bibliographic record
Abstract
Meenakshi Malhotra, Ciaran Lane, Catherine Tomaro-Duchesneau, Shyamali Saha, Satya PrakashBiomedical Technology and Cell Therapy Research Laboratory, Departments of Biomedical Engineering and Physiology, Artificial Cells and Organs Research Center, Faculty of Medicine, McGill University, Quebec, CanadaAbstract: Preparation of poly (ethylene glycol) (PEG)-grafted chitosan is essential for improving the biocompatibility and water solubility of chitosan. Presently available methods for this have limitations. This article describes a new method for preparing PEGylated chitosan nanoparticles. For this chitosan was chemoselectively modified using a novel scheme at the C6 position of its repeating units by PEG. The amine groups at the C2 position of the chitosan were protected using phthalic anhydride. Sodium hydride was used to catalyze the etherification reaction between chlorinated chitosan and methyl-PEG, and PEG-grafted chitosan was successfully synthesized. Each step was characterized using 13C nuclear magnetic resonance and Fourier transform infrared. After PEGylation the phthaloylated chitosan was successfully deprotected using hydrazine monohydrate. The synthetic scheme proposed demonstrates a new method for grafting PEG onto chitosan with a moderate degree of substitution. The potential of this polymer in nanoparticle preparation using an ionic gelation method and its gene delivery potentials were investigated by complexing a fluorescently labeled control siRNA. The result showed that suitable nanoparticles can be synthesized using this polymer and that they have capacity to carry genes and provide adequate transfection efficacy with no toxicity when tested in neuronal cells.Keywords: nanoparticles, chitosan, poly (ethylene glycol), polymeric membrane, gene delivery 
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".