Technique for Real-Time Measurements of Endothelial Permeability in a Microfluidic Membrane Chip Using Laser-Induced Fluorescence Detection
Bibliographic record
Abstract
Characterizing permeability of the endothelium that lines blood vessels and heart valves provides fundamental physiological information and is required to evaluate uptake of drugs and other biomolecules. However, current techniques used to measure permeability, such as Transwell insert assays, do not account for the recognized effects of fluid flow-induced shear stress on endothelial permeability or are inherently low-throughput. Here we report a novel on-chip technique in a two-layer membrane-based microfluidic platform to measure real-time permeability of endothelial cell monolayers on porous membranes. Bovine serum albumin (a model protein) conjugated with fluorescein isothiocyanate was delivered to an upper microchannel by pressure-driven flow and was forced to permeate a poly(ethylene terephthalate) membrane into a lower microchannel, where it was detected by laser-induced fluorescence. The concentration of the permeate at the point of detection varied with channel flow rates in agreement to less than 1% with theoretical analyses using a pore flow model. On the basis of the model, a sequential flow rate stepping scheme was developed and applied to obtain the permeability of cell-free and cell-bound membrane layers. This technique is a highly sensitive, novel microfluidic approach for measuring endothelial permeability in vitro, and the use of micrometer-sized channels offers the potential for parallelization and increased throughput compared to conventional shear-based permeability measurement methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".