A microfluidic membrane device to mimic critical components of the vascular microenvironment
Bibliographic record
Abstract
Vascular function, homeostasis, and pathological development are regulated by the endothelial cells that line blood vessels. Endothelial function is influenced by the integrated effects of multiple factors, including hemodynamic conditions, soluble and insoluble biochemical signals, and interactions with other cell types. Here, we present a membrane microfluidic device that recapitulates key components of the vascular microenvironment, including hemodynamic shear stress, circulating cytokines, extracellular matrix proteins, and multiple interacting cells. The utility of the device was demonstrated by measuring monocyte adhesion to and transmigration through a porcine aortic endothelial cell monolayer. Endothelial cells grown in the membrane microchannels and subjected to 20 dynes∕cm(2) shear stress remained viable, attached, and confluent for several days. Consistent with the data from macroscale systems, 25 ng∕ml tumor necrosis factor (TNF)-α significantly increased RAW264.7 monocyte adhesion. Preconditioning endothelial cells for 24 h under static or 20 dynes∕cm(2) shear stress conditions did not influence TNF-α-induced monocyte attachment. In contrast, simultaneous application of TNF-α and 20 dynes∕cm(2) shear stress caused increased monocyte adhesion compared with endothelial cells treated with TNF-α under static conditions. THP-1 monocytic cells migrated across an activated endothelium, with increased diapedesis in response to monocyte chemoattractant protein (MCP)-1 in the lower channel of the device. This microfluidic platform can be used to study complex cell-matrix and cell-cell interactions in environments that mimic those in native and tissue engineered blood vessels, and offers the potential for parallelization and increased throughput over conventional macroscale systems.
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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 | 0.001 | 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".