Assessment of the toxic potential of engineered metal oxide nanomaterials using an acellular model: citrated rat blood plasma
Bibliographic record
Abstract
Citrated Sprague–Dawley rat blood plasma was used as a biologically relevant exposure medium to assess the acellular toxic potential of two metal oxide engineered nanomaterials (ENMs), zinc oxide (nZnO), and cerium oxide (nCeO2). Plasma was incubated at 37 °C for up to 48 h with ENM concentrations ranging between 0 and 200 mg/L. The degree of ENM-induced oxidation was assessed by assaying for reactive oxygen species (ROS) levels using dichlorofluorescein (DCF), pH, ferric reducing ability of plasma (FRAP), lipase activity, malondialdehyde (MDA), and protein carbonyls (PC). Whereas previous in vitro studies showed linear-positive correlations between ENM concentration and oxidative damage, our results suggested that low concentrations were generally pro-oxidant and higher concentrations appeared antioxidant or protective, as indicated by DCF fluorescence trends. nZnO and nCeO2 also affected pH in a manner dependent on concentration and elemental composition; higher nZnO concentrations maintained a more alkaline pH, while nCeO2 tended to decrease pH. No other biomarkers of oxidative damage (FRAP, MDA, PC, lipase activity) showed changes at any ENM concentration or time-point tested. Differential dissolution of the two ENMs was also observed, where as much as ∼31.3% of nZnO was instantaneously dissolved to Zn2+ and only negligible nCeO2 was degraded. The results suggest that the direct oxidative potential of nZnO and nCeO2 in citrated rat blood plasma is low, and that a physiological or immune response is needed to generate appreciable damage biomarkers. The data also highlight the need for careful consideration when selecting a model for assessing ENM toxicity.
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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) | 0.001 | 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 | 0.001 |
| 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".