Matrix-dependent adhesion of vascular and valvular endothelial cells in microfluidic channels
Bibliographic record
Abstract
The interactions between endothelial cells and the underlying extracellular matrix regulate adhesion and cellular responses to microenvironmental stimuli, including flow-induced shear stress. In this study, we investigated the adhesion properties of primary porcine aortic endothelial cells (PAECs) and valve endothelial cells (PAVECs) in a microfluidic network. Taking advantage of the parallel arrangement of the microchannels, we compared adhesion of PAECs and PAVECs to fibronectin and type I collagen, two prominent extracellular matrix proteins, over a broad range of concentrations. Cell spreading was measured morphologically, based on cytoplasmic staining with a vital dye, while adhesion strength was characterized by the number of cells attached after application of shear stresses of 11, 110, and 220 dyn cm(-2). Results showed that PAVECs were more well spread on fibronectin than on type I collagen (P < 0.0001), particularly for coating concentrations of 100, 200, and 500 microg mL(-1). PAVECs also withstood shear significantly better on fibronectin than on collagen for 500 microg mL(-1). PAECs were more well spread on collagen compared to PAVECs (P < 0.0001), but did not have significantly better adhesion strength. These results demonstrate that cell adhesion is both cell-type and matrix dependent. Furthermore, they reveal important phenotypic differences between vascular and valvular endothelium, with implications for endothelial mechanobiology and the design of microdevices and engineered 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 | 0.000 | 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".