Measuring Cell Growth and Junction Development in Epithelial Cells Using Electric Cell-Substrate Impedance Sensing (ECIS)
Bibliographic record
Abstract
, the properties of tight junctions). The method provides simultaneous information on cell number and tight junction function by detecting electric parameters of cells grown on electrodes. Samples are probed with small alternating current (AC) over a range of frequencies, and changes in capacitance and impedance are measured over time. Capacitance reflects the degree of electrode coverage by cells, that correlates with cell number, and can be used to assess cell proliferation or migration. Impedance values inform about barrier function. Obtaining real-time simultaneous information on these parameters is unique to this system and is of great value for addressing fundamental questions such as the role of tight junction proteins in cell growth and migration. This protocol describes the use of ECIS to follow cell growth and tight junction-dependent barrier generation in tubular epithelial cells. We used this method to explore how depleting claudin-2, a tight junction protein affects tubular cell growth and barrier function. During the process, cells are transfected with control or claudin-2-specific siRNA, and 24h later plated on electrodes. ECIS automatically collects information on cell growth and barrier as the monolayer develops. The data are initially analyzed using the ECIS software and exported into a graph software for further processing.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".