Effect of titanium dioxide nanoparticles on DNA methylation of human peripheral blood mononuclear cells
Bibliographic record
Abstract
Abstract The aim of the current study was to investigate the effect of well-characterized TiO2 nanoparticles on DNA methylation of peripheral blood mononuclear cells (PBMCs) in vitro. Maximum non-toxic concentration of nanoparticles for PBMCs was determined by MTT assay. The effect of TiO2 nanoparticles at concentrations of 25–100 μg/ml on DNA methylation of PBMCs was investigated by measuring the %5-mC alterations through an ELISA assay. The physicochemical analysis showed that the TiO2 nanoparticles were crystalline, pure and in the anatase phase. Peaks related to Ti-O tensile vibrations were observed in the range of 1510 cm−1. The size of nanoparticles was in the range of 39–74 nm with an average hydrodynamic diameter of 43.82 nm. According to the results of the MTT test, 100 μg/ml was found to be maximum non-toxic concentration. The %5-mC in treated PBMCs revealed that TiO2 nanoparticles could lead to DNA hypomethylation in PBMCs. The %5-mC difference compared with the negative control was found to be 2.07 ± 1.02% (P = 0.03). The difference of %5-mC between the 25 and 100 μg/ml concentration of nanoparticles was statistically significant (P = 0.02). The results of the current study show that the TiO2 nanoparticles cause DNA hypomethylation in PBMCs in a dose-response manner. Therefore, it is recommended to evaluate the effects of cytotoxicity and epigenotoxicity of commonly used nanoparticles before their use.
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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".