In vivo imaging of immune cell dynamics in skin in response to zinc-oxide nanoparticle exposure
Bibliographic record
Abstract
Zinc oxide (ZnO) nanoparticles (NPs) are widely used in cosmetic and sunscreen products which are applied topically to the skin. Despite their widespread use, the safety and biological response of these particles remains an active area of investigation. In this paper we present methods based on in vivo multiphoton microscopy (MPM) in skin to address relevant questions about the potential toxicity and immunological response of ZnO NPs. Registration of time-lapse volumetric MPM images allows the same skin site to be tracked across multiple days for visualizing and quantifying cellular and structural changes in response to NP exposure. Making use of the unique optical properties of ZnO enables high contrast detection of the NPs in the presence of strong autofluorescence and second harmonic generation (SHG) background from the skin. A green fluorescent protein (GFP) bone marrow (BM) transplanted mouse model is used to visualize and assess the dynamic response of BM-derived immune cells. These cells are visualized to assess the potential for ZnO NPs to interact with immune cells and elicit an immune reaction in skin. We investigate both topical and dermal exposure of the ZnO NPs. The methods and findings presented in this paper demonstrate a novel approach for tracking ZnO NPs in vivo and for visualizing the cellular response of the exposed tissue to assess the immunological response and potential toxicity of these particles.
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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".