A compartmentalized microfluidic chip with crisscross microgrooves and electrophysiological electrodes for modeling the blood–retinal barrier
Bibliographic record
Abstract
The interconnection of different tissue-tissue interfaces may extend organ-on-chips to a new generation of sophisticated models capable of recapitulating more complex organ-level functions. Single interfaces are largely recreated in organ-on-chips by culturing the cells on opposite sides of a porous membrane that splits a chamber in two or by connecting the cells of two adjacent compartments through microchannels. However, it is difficult to interconnect more than one interface using these approaches. To address this challenge, we present a novel microfluidic device where cells are arranged in parallel compartments and are highly interconnected through a grid of microgrooves, which facilitates paracrine signaling and heterotypic cell-cell contact between multiple tissues. In addition, the device includes electrodes on the substrate for the measurement of transepithelial electrical resistance (TEER). Unlike conventional methods for measuring the TEER where electrodes are on each side of the cell barrier, a method with only electrodes on the substrate has been validated. As a proof-of-concept, we have used the device to mimic the structure of the blood-retinal barrier by co-culturing primary human retinal endothelial cells (HREC), a human neuroblastoma cell line (SH-SY5Y), and a human retinal pigment epithelial cell line (ARPE-19). Cell barrier formations were assessed by a permeability assay, TEER measurements, and ZO-1 expression. These results validate the proposed microfluidic device with microgrooves as a promising in vitro tool for the compartmentalization and monitoring of barrier tissues.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".