Electroporation-Enhanced Nonviral Gene Transfer for the Prevention or Treatment of Immunological, Endocrine and Neoplastic Diseases
Bibliographic record
Abstract
Nonviral gene transfer is markedly enhanced by the application of in vivo electroporation (also denoted electro-gene transfer or electrokinetic enhancement). This approach is safe and can be used to deliver nucleic acid fragments, oligonucleotides, siRNA, and plasmids to a wide variety of tissues, such as skeletal muscle, skin and liver. In this review, we address the principles of electroporation and demonstrate its effectiveness in disease models. Electroporation has been shown to be equally applicable to small and large animals (rodents, dogs, pigs, other farm animals and primates), and this addresses one of the major problems in gene therapy, that of scalability to humans. Gene transfer can be optimized and tissue injury minimized by the selection of appropriate electrical parameters. We and others have applied this approach in preclinical autoimmune and/or inflammatory diseases to deliver either cytokines, anti-inflammatory agents or immunoregulatory molecules. Electroporation is also effective for the intratumoral delivery of therapeutic vectors. It strongly boost DNA vaccination against infectious agents (e.g., hepatitis B virus, human immunodeficiency virus-1) or tumor antigens (e.g., HER-2/neu, carcinoembryonic antigen). In addition, we found that electroporation-enhanced DNA vaccination against islet-cell antigens ameliorated autoimmune diabetes. One of the most likely future applications, however, may be in intramuscular gene transfer for systemic delivery of either endocrine hormones (e.g., growth hormone releasing hormone and leptin), hematopoietic factors (e.g., erythropoietin, GM-CSF), antibodies, enzymes, or numerous other protein drugs. In vivo electroporation has been performed in humans, and it seems likely it could be applied clinically for nonviral gene therapy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".