Microneedle/nanoencapsulation-mediated transdermal delivery: Mechanistic insights
Bibliographic record
Abstract
A systematic study was undertaken to gain more insight into the mechanism of transdermal delivery of nanoencapsulated model dyes across microneedle (MN)-treated skin, a complex process not yet explored. Rhodamine B (Rh B) and fluorescein isothiocyanate (FITC) as model hydrophilic and hydrophobic small/medium-size molecules, respectively, were encapsulated in poly lactic-co-glycolic acid (PLGA) nanoparticles (NPs) and delivered through full thickness porcine skin pretreated with MN array. Permeation through MN-treated skin was affected by physicochemical characteristics of NPs and the encapsulated dyes. Dye flux was enhanced by smaller particle size, hydrophilicity, and negative zeta potential of NPs. Regarding encapsulated dyes, solubility at physiological pH and potential interaction with skin proteins proved to outweigh molecular weight as determinants of skin permeation. Data were verified using confocal laser scanning microscopy imaging. Findings coupled with the literature data are supportive of a mechanism involving influx of NPs, particularly of smaller size, deep into MN-created channels, generating depot dye-rich reservoirs. Molecular diffusion of the released dye across viable skin layers proceeds at a rate determined by its molecular characteristics. Data obtained provide mechanistic information of importance to the development of formulation strategies for more effective intradermal and transdermal MN-mediated delivery of nanoencapsulated therapeutic agents.
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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 | 0.001 |
| 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".