High-Sensitivity Permeation Analysis of Ultrasmall Nanoparticles Across the Skin by Positron Emission Tomography
Bibliographic record
Abstract
Ultrasmall nanoparticles (US-NPs; <20 nm in hydrodynamic size) are now included in a variety of pharmacological and cosmetic products, and new technologies are needed to detect at high sensitivity the passage of small doses of these products across biological barriers such as the skin. In this work, a diffusion cell adapted to positron emission tomography (PET), a highly sensitive imaging technology, was developed to measure the passage of gold NPs (AuNPs) in skin samples in continuous mode. US-AuNPs (3.2 nm diam.; TEM) were functionalized with deferoxamine (DFO) and radiolabeled with 89Zr(IV) (half-life: 3.3 days, matching the timeline of diffusion tests). The physicochemical properties of the functionalized US-AuNPs (US-AuNPs-PEG-DFO) were characterized by FTIR (DFO grafting; hydroxamate peaks: 1629.0 cm–1, 1569.0 cm–1), XPS (presence of the O═C–N C 1s peak of DFO at 287.49 eV), and TGA (organic mass fraction). The passage of US-AuNPs-PEG-DFO-89Zr(IV) in skin samples was measured by PET, and the diffusion parameters were extracted thereby. The signals of radioactive US-AuNPs-PEG-DFO-89Zr(IV) leaving the donor compartment, passing through the skin, and entering the acceptor compartment were detected in continuous at concentrations as low as 2.2 nM of Au. The high-sensitivity acquisitions performed in continuous allowed for the first time to extract the lag time to the start of permeation, the lag time to start of the steady state, the diffusion coefficients, and the influx data for AuNPs permeating into the skin. PET could represent a highly valuable tool for the development of nanoparticle-containing topical formulations of drugs and cosmetics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".