Cerium Oxide Nanoparticles in Lung Acutely Induce Oxidative Stress, Inflammation, and DNA Damage in Various Organs of Mice
Bibliographic record
Abstract
CeO2 nanoparticles (CeO2 NPs) which are used as a diesel fuel additive are emitted in the particulate phase in the exhaust, posing a health concern. However, limited information exists regarding the in vivo acute toxicity of CeO2 NPs on multiple organs. Presently, we investigated the acute (24 h) effects of intratracheally instilled CeO2 NPs in mice (0.5 mg/kg) on oxidative stress, inflammation, and DNA damage in major organs including lung, heart, liver, kidneys, spleen, and brain. Lipid peroxidation measured by malondialdehyde production was increased in the lungs only, and reactive oxygen species were increased in the lung, heart, kidney, and brain. Superoxide dismutase activity was decreased in the lung, liver, and kidney, whereas glutathione increased in lung but it decreased in the kidney. Total nitric oxide was increased in the lung and spleen but it decreased in the heart. Tumour necrosis factor‐α increased in all organs studied. Interleukin‐ (IL‐) 6 increased in the lung, heart, liver, kidney, and spleen. IL‐1β augmented in the lung, heart, kidney, and spleen. Moreover, CeO2 NPs induced DNA damage, assessed by COMET assay, in all organs studied. Collectively, these findings indicate that pulmonary exposure to CeO2 NPs causes oxidative stress, inflammation, and DNA damage in multiple organs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".