Influence of surface charge of gold nanorods on skin penetration
Bibliographic record
Abstract
BACKGROUND/PURPOSE: The skin plays an important role as a protective barrier against toxic environments and also is a route of drug administration. In spite of evidence for and interest in the skin penetration of nanoparticles, no study has examined the effect of nanoparticle surface charge on percutaneous absorption. In this study, we investigated the effect of surface charges of gold nanorods (GNs) on skin penetration. METHODS: Using transmission electron microscopy (TEM) and image analysis, we quantitatively measured the ability of GNs to penetrate the skin. RESULTS: Our results showed that the area density of the electron-dense dots of GNs, which penetrated into the stratum corneum, significantly increased for negatively charged GNs compared to those with a positive charge (P < 0.01). To investigate the percutanoues absorption of charged GNs, in vitro skin permeation studies were carried out using a Franz-type diffusion cell (FDC). The penetration of GNs through the skin was quantified by inductively coupled plasma mass spectrometry. Consistent with TEM observations, our penetration study using an FDC also revealed that negative particles were frequently detected in samples of receptor fluid at 48 h after exposure (P < 0.01). CONCLUSION: Together our results showed that anionic GNs penetrate skin better than cationic GNs.
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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 | 0.001 |
| 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".