Characterizing the effects of titanium dioxide and silver nanoparticles released from painted surfaces due to weathering on zebrafish (<i>Danio rerio</i>)
Bibliographic record
Abstract
Silver (nAg) and titanium dioxide nanoparticles (nTiO2) are common engineered nanoparticles (ENPs) added into paint for their antimicrobial and whitening properties, respectively. Weathering of outdoor painted surfaces can release such ENPs, though little is known about the potential effects of released ENPs on aquatic species. The objective of this study was to characterize the toxicity of nAg and nTiO2 released from painted panels using fish liver cells (CRL2643) and zebrafish embryos (OECD 236 embryotoxicity test). Cells and embryos were exposed to suspensions of pristine nAg or nTiO2, panels (unpainted or painted with nAg or nTiO2) or base paint, after sonication. Cell viability and gene expression were assessed using resazurin assay and qPCR, respectively, while embryo mortality and deformities were scored visually via microscopic examination. In the cell studies, both paint-released nanoparticles did not affect viability, but paint-released nAg resulted in differential expression of a few genes including gclc and ncf1. In embryos, paint-released nAg increased mortality and incidence of deformities, whereas paint-released nTiO2 resulted in differential expression of several genes including gclc, ncf1, txnrd1, gpx1b, and cyp1c1 but without major phenotypic abnormalities. Comparing the two types of exposures, paint-released exposures affected both molecular (gene expression) and apical (embryotoxicity) endpoints, while pristine exposures affected the expression of some genes but had no apical effects. The differing effects of paint-released and pristine nanoparticle exposures suggest that further research is needed to further understand how paint coatings (and the products of their weathering and aging) may influence nanoparticle toxicity to aquatic organisms.
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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".