IN UTERO GENE DELIVERY USING CHITOSAN-DNA NANOPARTICLES IN MICE
Bibliographic record
Abstract
Background:In utero gene therapy is a noveltherapy for monogenic disorders of the fetus. Viral vector-mediated gene transfer risks endogenous viral recombination, and random transgene insertion causing insertional mutagenesis. Enhanced biosafety alternatives include non-viral vectors such as the complex polymer, chitosan. The purpose of this study was toevaluate chitosan-mediated transfection in a murine model of fetal genetherapy. Methods: 1. Chitosan colloidal suspensions wereprepared, and particle size analysis in amniotic fluid (AF) was performed using a zetasizer. 2. Plasmid enhanced green fluorescent protein (eGFP)-chitosan constructs were prepared and protection from endogenous digestion in AF was analyzed by gel electrophoresis. 3. 0.25x105 HEK293T cells were transfected over 2 hours with 0.6 ?g of chitosan-peGFP in varying proportions of medium and AF. After 48 h, cells were directly imaged by fluorescence microscopy for eGFP expression and fluorescence activated cell sorting (FACS) sorting was done to determine transfection efficiency. 4. Amniotic sacs of time-mated CD-1 mice were injected with 30 ?L chitosan-peGFP (12.5 ug DNA) on G17. Following natural birth, pupswere sacrificed and tissues were examined for eGFP DNA and mRNA by DNA PCR, RT-PCR, and fluorescence microscopy. Results: Chitosan forms aggregates in AF, and although in vitro transfection efficiency is decreased by AF, eGFP-chitosan delivery into AF achieves transfection and transgene expression in lung and intestine of mice after birth. Conclusions: In utero delivery of eGFP plasmid by chitosan results in postnatal gene expression, and shows promise for non-viral gene transfer in animal models of fetal gene therapy. P.T.P.Y. is supported by a Child and Family–UBCMD/PhD Studentship Award.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".