An impedance-based high-throughput method for evaluating the cytotoxicity of nanoparticles
Bibliographic record
Abstract
Impedance-based assays can constitute a reliable alternative to the conventional methods used in nanotoxicology due to the important advantages of being label-free and monitoring the cells in real-time. In this study, the suitability of impedance-monitoring for the screening of nanoparticle (NP)-induced cytotoxicity was assessed. The effect of titanium dioxide (TiO 2 )-NPs on cellular proliferation, viability, spreading, and detachment from substrate was evaluated by continuous impedance-based measurements made with an xCELLigence system. Fibroblasts seeded in microelectrode-embedded E-plates were exposed to spherical anatase nano-TiO 2 (5, 10, and 40 nm in diameter) for up to 120 h. An alternative excitation signal (20 mV control voltage amplitude) was applied at 10, 25, and 50 kHz to the microelectrodes in the E-plates. Cells attached to the electrode surfaces act as insulators and lead to an increase in impedance. For validating the impedance-method, Trypan Blue exclusion and ultrahigh resolution imaging (URI) were employed. The general trend observed was a decrease in impedance following exposure to TiO 2 -NPs. Impedance-based results were in most instances in accordance with those from the Trypan Blue exclusion and URI assays indicating that the impedance-based approach has merit. Further studies are needed to validate it as a high-throughput method for evaluating NPs' cytotoxicity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".