A Microfluidic Barrier-on-Chip Platform with Integrated Porous Membrane Cell–Substrate Impedance Spectroscopy
Bibliographic record
Abstract
Organ-on-chip (OOC) systems that recapitulate microenvironmental features like coculture, fluid shear stress, and extracellular matrix are useful for modeling biological barriers. OOC barrier integrity measurements are often done by trans-endothelial/epithelial electrical resistance (TEER) measurement, but this approach is confounded by nonuniform current distribution and interference from biomaterials typical to such systems. We addressed this gap by incorporating gold leaf porous membrane electrical cell–substrate impedance sensing (PM-ECIS) electrodes (diameters of 250, 500, or 750 μm) into a biocompatible tape-based barrier-on-chip (BOC) platform. PM-ECIS measurements were robust to fluid shear (5 dyn/cm 2 ) in cell-free devices, yet highly sensitive to flow-induced changes in an endothelial barrier model. Perfusion (0.06 dyn/cm 2 ) corresponded to significant decreases in impedance at 40 kHz ( p < 0.01 for 750, 500 μm electrodes) and resistance at 4 kHz ( p < 0.05 for all electrode sizes) relative to static control, with minimum values reached 6.5–9.5 h after flow induction. We also demonstrated that PM-ECIS is robust to the presence of hydrogel, and unlike chopstick TEER, has the measurement sensitivity to detect human brain microvascular endothelial monolayers in a hydrogel coculture model. The sensitive, noninvasive, real-time measurements of barrier function in microfluidic PM-ECIS setups makes it well-suited for OOC applications that include features like 3D coculture, biomaterials, and shear stress.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".