On-Line Monitoring of Cell Growth and Cytotoxicity Using Electric Cell-Substrate Impedance Sensing (ECIS)
Bibliographic record
Abstract
An on-line and continuous technique based on electric cell-substrate impedance sensing (ECIS) was developed for measuring the concentration and time response function of fibroblastic V79 cells exposed to mercury chloride and 1,3,5-trinitrobenzene (TNB). Attachment, spreading and proliferation of V79 fibroblastic cells cultured on a microarray of small gold electrodes precoated with fibronectin were detected as resistance changes. The response function was derived to reflect the resistance change as a result of cell attachment, spreading, mitosis and cytotoxicity effect. Exposure of V79 cells to mercury chloride or TNB led to alterations in cell behavior, and therefore, chemical cytotoxicity was easily screened by measuring the response function of the attached and spread cells in the presence of inhibitor. The half inhibition concentration, the required concentration to achieve 50% inhibition, was obtained from the response function to provide information about cytotoxicity during the course of the assay. A simple mathematical model was developed to describe the responses of ECIS that were related to the attachment, spreading, and proliferation of V79 fibroblastic cells. The novel results of this paper are mainly characterized by the systematic study of several parameters including the cell number, detection limit, sensor sensitivity, and cytotoxicity, and they may motivate further research and study of ECIS sensors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".