Critical experimental parameters related to the cytotoxicity of zinc oxide nanoparticles
Bibliographic record
Abstract
The increasing use of zinc oxide nanoparticles (ZnO-NPs) has raised concerns about their potential hazards to human and environmental health. In this study, the characterization and cytotoxicity of two ZnO-NPs products (Z-COTE and Z-COTE HP1) were investigated. The zinc content of Z-COTE and Z-COTE HP1 was 82.5 ± 7.3 and 80.1 ± 3.5 %, respectively. Both ZnO-NP samples contained sub-cytotoxic levels of iron and copper, and silicon was detected from the surface coating of Z-COTE HP1. All samples were highly agglomerated, and the primary particles appeared as variable polyhedral structures. There was no significant difference in size distribution or average diameter of Z-COTE (53 ± 23 nm) and Z-COTE HP1 (54 ± 26 nm). A dose-dependent cytotoxicity was observed 24 h after exposure to ZnO-NPs, and monocytes were more sensitive than lung epithelial cells or lymphoblasts in both human and mouse cells. There was a significant difference in cytotoxicity between nano- and fine-forms, but only at the threshold cytotoxic dose with cellular metabolism assays. Compared to uncoated ZnO-NPs, the surface coating with triethoxycaprylylsilane marginally attenuated cellular oxidative stress and protected cellular metabolic activity. These results demonstrate the importance of model cell type, dose selection, and cytotoxicity assessment methodology to accurately evaluate the potential toxicity of various nanoparticles in vitro.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".