Unchanged pulmonary toxicity of ZnO nanoparticles formulated in a liquid matrix for glass coating
Bibliographic record
Abstract
The inclusion of nanoparticles can increase the quality of certain products. One application is the inclusion of Zinc oxide (ZnO) nanoparticles in a glass coating matrix to produce a UV-absorbing coating for glass sheets. Yet, the question is whether the inclusion of ZnO in the matrix induces toxicity at low exposure levels. To test this, mice were given single intratracheal instillation of 1) ZnO powder (ZnO), 2) ZnO in a glass matrix coating in its liquid phase (ZnO-Matrix), and 3) the matrix with no ZnO (Matrix). Doses of ZnO were 0.23, 0.67, and 2 µg ZnO/mouse. ZnO Matrix doses had equal amounts of ZnO, while Matrix was adjusted to have an equal volume of matrix as ZnO Matrix. Post-exposure periods were 1, 3, or 28 d. Endpoints were pulmonary inflammation as bronchoalveolar lavage (BAL) fluid cellularity, genotoxicity in lung and liver, measured by comet assay, histopathology of lung and liver, and global gene expression in lung using microarrays. Neutrophil numbers were increased to a similar extent with ZnO and ZnO-Matrix at 1 and 3 d. Only weak genotoxicity without dose-response effects was observed in the lung. Lung histology showed an earlier onset of inflammation in material-exposed groups as compared to controls. Microarray analysis showed a stronger response in terms of the number of differentially regulated genes in ZnO-Matrix exposed mice as compared to Matrix only. Activated canonical pathways included inflammatory and cardiovascular ones. In conclusion, the pulmonary toxicity of ZnO was not changed by formulation in a liquid matrix for glass coating.
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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".