Factors Influencing the Efficacy, Longevity, and Safety of Electroporation-Assisted Plasmid-Based Gene Transfer into Mouse Muscles
Bibliographic record
Abstract
Intramuscular injection of plasmid is a potential alternative to viral vectors for the transfer of therapeutic genes into skeletal muscle fibers. The low efficiency of plasmid-based gene transfer can be enhanced by electroporation (EP) coupled with the intramuscular application of hyaluronidase. We have investigated several factors that can influence the efficiency of plasmid-based gene transfer. These factors include electrical parameters of EP, optimal use of hyaluronidase, age and strain of the host, and plasmid size. Muscles of very young and mature normal, mdx, and immunodeficient mice were injected with plasmids expressing beta-galactosidase, microdystrophin, full-length dystrophin, or full-length utrophin. Transfection efficiency, muscle fiber damage, and duration of transgene expression were analyzed. The best transfection level with the least collateral damage was attained at 175-200 V/cm. Pretreatment with hyaluronidase markedly increased transfection, which was also influenced by the plasmid size and the strain and the age of the mice. Even in immunodeficient mice, there was a significant late decline in transgene expression and plasmid DNA copies, although both still remained relatively high after 1 year. Thus, properly optimized EP-assisted plasmid-based gene transfer is a feasible, efficient, and safe method of gene replacement therapy for dystrophin deficiency of muscle but readministration may be necessary.
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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) | 0.000 | 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".