Gene Delivery into Human Skin In Vitro Using Biphasic Lipid Vesicles
Bibliographic record
Abstract
Topical gene delivery to the skin shows great potential for painless, non-invasive administration of novel vaccines and therapeutic agents. The challenge is to develop a pharmaceutically acceptable system that can deliver suitable amounts of plasmid DNA to produce the desired level of response. The purpose of this study was to quantitatively assess DNA delivery by a novel lipid-based biphasic delivery system into the viable layers of excised human skin. Biphasic lipid vesicle formulations, incorporating plasmid DNA were evaluated in vitro in flow-through diffusion cells. Fifty mg DNA formulation containing 10 microg DNA was applied to full-thickness human breast skin for 24 hours. Residual formulation was removed and the skin was washed with PBS, then tape-stripped, followed by DNase treatment to remove surface bound DNA. Skin samples were homogenised and digested overnight with Proteinase K. The resulting supernatant was used as a template for quantitative PCR. Three formulations yielded a significant degree of dermal absorption compared to the controls. Formulation 26-3-2-DNA indicated that approximately 1x10(9) copies of plasmid were absorbed per cm2 skin. Other formulations resulted in 5x10(6) copies/cm2 skin (17C3-1-DNA) and 5x10(8) copies/cm2 skin (26-3-1-DNA). Biphasic vesicles delivered significant quantities of plasmid DNA into the 'viable' layers of human skin in vitro. The successful delivery of this large (approximately 4,400 kDa) charged molecule through intact stratum corneum represents a major advance in transdermal macromolecule delivery.
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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) | 0.000 | 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".