Coating-dependent induction of cytotoxicity and genotoxicity of iron oxide nanoparticles
Bibliographic record
Abstract
Surface coatings of nanoparticles (NPs) are known to influence advantageous features of NPs as well as potential toxicity. Iron oxide (Fe3O4) NPs are applied for both medical diagnostics and targeted drug delivery. We investigated the potential cytotoxicity and genotoxicity of uncoated iron oxide (U-Fe3O4) NPs in comparison with oleate-coated iron oxide (OC-Fe3O4) NPs. Testing was performed in vitro in human lymphoblastoid TK6 cells and in primary human blood cells. For cytotoxicity testing, relative growth activity, trypan blue exclusion, (3)H-thymidine incorporation and cytokinesis-block proliferation index were assessed. Genotoxicity was evaluated by the alkaline comet assay for detection of strand breaks and oxidized purines. Particle characterization was performed in the culture medium. Cellular uptake, morphology and pathology were evaluated by electron microscopy. U-Fe3O4 NPs were found not to be cytotoxic (considering interference of NPs with proliferation test) or genotoxic under our experimental conditions. In contrast, OC-Fe3O4 NPs were cytotoxic in a dose-dependent manner, and also induced DNA damage, indicating genotoxic potential. Intrinsic properties of sodium oleate were excluded as a cause of the toxic effect. Electron microscopy data were consistent with the cytotoxicity results. Coating clearly changed the behaviour and cellular uptake of the NPs, inducing pathological morphological changes in the cells.
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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".