A 3D microfluidic platform incorporating methacrylated gelatin hydrogels to study physiological cardiovascular cell–cell interactions
Bibliographic record
Abstract
The cardiovascular system is particularly well-suited to modelling with microfluidic technologies, and much progress has been made to create microfluidic devices that mimic the microvasculature. In contrast, microfluidic platforms that model larger blood vessels and heart valves are lacking, despite the clear potential benefits of improved physiological relevance and enhanced throughput over traditional cell culture technologies. To address this need, we developed a bilayer membrane microfluidic device to model the vascular/valvular three-dimensional environment. Key features of the platform include physiologically-relevant spatial arrangement of multiple cell types, fluid flow over an endothelial monolayer, a porous membrane that permits heterotypic cell interactions while maintaining cell compartmentalization, and a photopolymerizable gelatin methacrylate (gel-MA) hydrogel as a physiologically-relevant subendothelial 3D matrix. Processing guidelines were defined for successful in-channel polymerization of gel-MA hydrogels that were mechanically stable, had physiologically-relevant elastic moduli of 2-30 kPa, and supported over 80% primary cell viability for at least four days in culture. The platform was applied to investigate shear stress-regulated paracrine interactions between valvular endothelial cells and valvular interstitial cells. The presence of endothelial cells significantly suppressed interstitial cell pathological differentiation to α-smooth muscle actin-positive myofibroblasts, an effect that was enhanced when the endothelium was exposed to flow-induced shear stress. We expect this versatile organ-on-a-chip platform to have broad utility for mechanistic vascular and valvular biology studies and to be useful for drug screening in physiologically-relevant 3D cardiovascular microenvironments.
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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) | 0.001 | 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".