Effects of Silica and Titanium Oxide Particles on a Human Neural Stem Cell Line: Morphology, Mitochondrial Activity, and Gene Expression of Differentiation Markers
Bibliographic record
Abstract
Several in vivo studies suggest that nanoparticles (smaller than 100 nm) have the ability to reach the brain tissue. Moreover, some nanoparticles can penetrate into the brains of murine fetuses through the placenta by intravenous administration to pregnant mice. However, it is not clear whether the penetrated nanoparticles affect neurogenesis or brain function. To evaluate its effects on neural stem cells, we assayed a human neural stem cell (hNSCs) line exposed in vitro to three types of silica particles (30 nm, 70 nm, and <44 µm) and two types of titanium oxide particles (80 nm and < 44 µm). Our results show that hNSCs aggregated and exhibited abnormal morphology when exposed to the particles at concentrations = 0.1 mg/mL for 7 days. Moreover, all the particles affected the gene expression of Nestin (stem cell marker) and neurofilament heavy polypeptide (NF-H, neuron marker) at 0.1 mg/mL. In contrast, only 30-nm silica particles at 1.0 mg/mL significantly reduced mitochondrial activity. Notably, 30-nm silica particles exhibited acute membrane permeability at concentrations =62.5 µg/mL in 24 h. Although these concentrations are higher than the expected concentrations of nanoparticles in the brain from in vivo experiments in a short period, these thresholds may indicate the potential toxicity of accumulated particles for long-term usage or continuous exposure.
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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".