Needle-free Gene Delivery Through the Skin: An Overview of Recent Strategies
Bibliographic record
Abstract
Topical administration is attractive and non-invasive gene delivery approach. It is simple and allows repeated administration. In addition, the skin is active immune surveillance site. Topical gene therapy, although promising for treatment of cancer, dermatological disorders, vaccination and autoimmune disease, has not progressed yet to clinical trials. The inability of nucleic acids to survive the extraand intracellular environment and to permeate through the outermost layer of the skin, the stratum corneum, compromise the therapeutic outcomes of nucleic acids-based therapies. Nanostructured vehicles (e.g. transfersomes, niosomes, nanoemulsions, gemini-lipid nanoparticles and biphasic vesicles) have the ability to partially disrupt and perturb lipids that are found in the skin layers and deliver their nucleic acid cargos to their targeted subcellular compartments. However, the efficiency of these carriers is still inferior to other invasive methods (e.g. epidermal and intradermal injections). The goal of this review is to examine the critical parameters required to enhance the efficiency of the currently available nanostructured vehicles, for example, by combining them with minimally invasive techniques, such as, electroporation, iontophoresis, microneedles, ultrasound, gene gun and femtosecond laser. The recent advances in engineering these nanovectors will be discussed with a focus on their future prospects.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.002 |
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".