Acute Toxicity of Metal Oxide Nanoparticles—Role of Intracellular Localization In Vitro in Lung Epithelial Cells
Bibliographic record
Abstract
Endocytic uptake and lysosomal localization are suggested to be the key mechanisms underlying the toxicity of metal oxide nanoparticles (MONPs), with dissolution in the acidic milieu driving the response. In this study, we aimed to investigate if MONPs of varying solubility are similarly sequestered intracellularly, including in lysosomes and the role of the acidic lysosomal milieu on toxicity induced by copper oxide (CuO) nanoparticles (NPs), nickel oxide (NiO) NPs, aluminum oxide (Al2O3) NPs, and titanium dioxide (TiO2) NPs of varying solubility in FE1 lung epithelial cells. Mitsui-7 multi-walled carbon nanotubes (MWCNTs) served as contrasts against particles. Enhanced darkfield hyperspectral imaging (EDF-HSI) with fluorescence microscopy was used to determine their potential association with lysosomes. The v-ATPase inhibitor Bafilomycin A1 (BaFA1) was used to assess the role of lysosomal acidification on toxicity. The results showed co-localization of all MONPs with lysosomes, with insoluble TiO2 NPs showing the greatest co-localization. However, only acute toxicity induced by soluble CuO NPs was affected by the presence of BaFA1, showing a 14% improvement in relative survival. In addition, all MONPs were found to be associated with large actin aggregates; however, treatment with insoluble TiO2 NPs, but not soluble CuO NPs, impaired the organization of F-actin and α-tubulin. These results indicate that MONPs are sequestered similarly intracellularly; however, the nature or magnitude of their toxicity is not similarly impacted by it. Future studies involving a broader variety of NPs are needed to fully understand the role of differential sequestration of NPs on cellular 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) | 0.000 | 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".