Dynamic Monitoring of Cytotoxicity on Microelectronic Sensors
Bibliographic record
Abstract
A real-time cell electronic sensing (RT-CES) system was used for label-free, dynamic measurement of cell responses to cytotoxicants. Cells were grown onto the surfaces of microelectronic sensors, which are comprised of circle-on-line electrode arrays and are integrated into the bottom surfaces of the microtiter plate. Changes in cell status such as cell number, viability, morphology, and adherence were monitored and quantified by detecting sensor electrical impedance. For cell quantification and viability measurement, the data generated on the RT-CES system correlated well with those from the colorimetric (MTT) assay. For cytotoxicity assessment, cells growing on microelectronic sensors were treated with different cytotoxicants, such as arsenic, mercury, and sodium dichromate. The dynamic responses of the cells to the toxicants were continuously monitored by the RT-CES system. On the basis of the IC50 values, the RT-CES system displays an equal sensitivity to the neutral red uptake assay at specific time points. Furthermore, because the RT-CES system provides real-time information regarding the state of cell morphology and adhesion in addition to cell number, we were able to discern a previously unreported effect of arsenic on NIH 3T3 cells prior to cell death. Also, using the RT-CES system, we were able to monitor cytotoxicity effects that occur within a minute of compound addition. Taken together, the RT-CES system allows for real-time, continuous monitoring and quantitative recording of the whole assay process and provides new insight into the cell-toxicant interaction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".