Utilization of an Artery-on-a-Chip to Unravel Novel Regulators and Therapeutic Targets in Vascular Diseases.

In this study, organ-on-chip technology is used to develop an in vitro model of medium-to-large size arteries, the artery-on-a-chip (AoC), with the objective to recapitulate the structure of the arterial wall and the relevant hemodynamic forces affecting luminal cells. AoCs exposed either to in vivo-like shear stress values or kept in static conditions are assessed to generate a panel of novel genes modulated by shear stress. Considering the crucial role played by shear stress alterations in carotid arteries affected by atherosclerosis (CAD) and abdominal aortic aneurysms (AAA) disease development/progression, a patient cohort of hemodynamically relevant specimens is utilized, consisting of diseased and non-diseased (internal control) vessel regions from the same patient. Genes activated by shear stress follow the same expression pattern in non-diseased segments of human vessels. Single cell RNA sequencing (scRNA-seq) enables to discriminate the unique cell subpopulations between non-diseased and diseased vessel portions, revealing an enrichment of flow activated genes in structural cells originating from non-diseased specimens. Furthermore, the AoC served as a platform for drug-testing. It reproduced the effects of a therapeutic agent (lenvatinib) previously used in preclinical AAA studies, therefore extending the understanding of its therapeutic effect through a multicellular structure.

Modeling hemodynamics in intracranial aneurysms: Comparing accuracy of CFD solvers based on finite element and finite volume schemes.

Image-based computational fluid dynamics (CFD) has shown potential to aid in the clinical management of intracranial aneurysms, but its adoption in the clinical practice has been missing, partially because of lack of accuracy assessment and sensitivity analysis. To numerically solve the flow-governing equations, CFD solvers generally rely on 2 spatial discretization schemes: finite volume (FV) and finite element (FE). Since increasingly accurate numerical solutions are obtained by different means, accuracies and computational costs of FV and FE formulations cannot be compared directly. To this end, in this study, we benchmark 2 representative CFD solvers in simulating flow in a patient-specific intracranial aneurysm model: (1) ANSYS Fluent, a commercial FV-based solver, and (2) VMTKLab multidGetto, a discontinuous Galerkin (dG) FE-based solver. The FV solver's accuracy is improved by increasing the spatial mesh resolution (134k, 1.1m, 8.6m, and 68.5m tetrahedral element meshes). The dGFE solver accuracy is increased by increasing the degree of polynomials (first, second, third, and fourth degree) on the base 134k tetrahedral element mesh. Solutions from best FV and dGFE approximations are used as baseline for error quantification. On average, velocity errors for second-best approximations are approximately 1 cm/s for a [0,125] cm/s velocity magnitude field. Results show that high-order dGFE provides better accuracy per degree of freedom but worse accuracy per Jacobian nonzero entry as compared with FV. Cross-comparison of velocity errors demonstrates asymptotic convergence of both solvers to the same numerical solution. Nevertheless, the discrepancy between underresolved velocity fields suggests that mesh independence is reached following different paths.