Chitosan–DNA Nanoparticles: Effect on DNA Integrity, Bacterial Transformation and Transfection Efficiency
Bibliographic record
Abstract
While somatic gene therapy has the potential to treat many genetic disorders, recent clinical trials suggest that an efficient and safe delivery vehicle for successful gene therapy is lacking. The current study examines the influence of two different preparation (the solvent evaporation method and the complex coacervation method) methods on the encapsulation of a model plasmid with chitosan. The ability of different molecular weights of chitosan to form nanoparticles with a plasmid, and particulated polymers to stabilize a plasmid in a supercoiled form, were examined by agarose gel electrophoresis. Protection of encapsulated pDNA offered by these nanoparticles from nuclease attack was confirmed by assessing degradation in the presence of DNase I, and the transformation of the plasmids with incubated nanoparticles were examined by beta-galactosidase assay. Model pDNA existed as a mixture of both supercoiled (84.2%) and open circular (15.8%) forms. Our results demonstrated that supercoiled forms decreased while open circular forms and fragmented linear forms increased during the preparation of formulations. F1 formulation prepared by the complex coacervation method protected the supercoiled form of pDNA effectively. There weren't any significant changes in nanoparticle size and zeta potential values at pH 5.5 for a period of 3 months, but differences in particle sizes were observed after lyophilization with a cryoprotective agent. The efficiency of nanoparticles mediated transformation to Escherichia coli cells was significantly higher than naked DNA or poly-L-lysine (PLL)-DNA polycation complexes. The transfection studies were performed in COS-7 cells. A 3-fold increase in gene expression was produced by nanoparticles as compared to the same amount of naked plasmid DNA (pDNA). These observations suggest that formulations with high molecular weight (HMW) chitosan can be an effective non-viral method of gene vector in animal studies.
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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 | 0.001 |
| 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) | 0.000 | 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 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".