Modeling the effect of patient size on cerebral perfusion during veno-arterial extracorporeal membrane oxygenation.

Introduction: A well-known complication of veno-arterial extracorporeal membrane oxygenation (VA ECMO) is differential hypoxia, in which poorly-oxygenated blood ejected from the left ventricle mixes with and displaces well-oxygenated blood from the circuit, thereby causing cerebral hypoxia and ischemia. We sought to characterize the impact of patient size and anatomy on cerebral perfusion under a range of different VA ECMO flow conditions.

Methods: We use one-dimensional (1D) flow simulations to investigate mixing zone location and cerebral perfusion across 10 different levels of VA ECMO support in eight semi-idealized patient geometries, for a total of 80 scenarios.

Identifying constitutive and context-specific molecular-tension-sensitive protein recruitment within focal adhesions.

Mechanosensitive processes often rely on adhesion structures to strengthen, or mature, in response to applied loads. However, a limited understanding of how the molecular tensions that are experienced by a particular protein affect the recruitment of other proteins represents a major obstacle in the way of deciphering molecular mechanisms that underlie mechanosensitive processes. Here, we describe an imaging-based technique, termed fluorescence-tension co-localization (FTC), for studying molecular-tension-sensitive protein recruitment inside cells.

Global Sensitivity Analysis For Clinically Validated 1D Models of Fractional Flow Reserve.

Computation of Fractional Flow Reserve (FFR) through computational fluid dynamics (CFD) is used to guide intervention and often uses a number of clinically-derived metrics, but these patient-specific data could be costly and difficult to obtain. Understanding which parameters can be approximated from population averages and which parameters need to be patient-specific is important and remains largely unexplored. In this study, we performed a global sensitivity study on two 1D models of FFR to identify the most influential patient parameters.

Multiscale modeling of blood flow to assess neurological complications in patients supported by venoarterial extracorporeal membrane oxygenation.

Computational blood flow models in large arteries elucidate valuable relationships between cardiovascular diseases and hemodynamics, leading to improvements in treatment planning and clinical decision making. One such application with potential to benefit from simulation is venoarterial extracorporeal membrane oxygenation (VA-ECMO), a support system for patients with cardiopulmonary failure. VA-ECMO patients develop high rates of neurological complications, partially due to abnormal blood flow throughout the vasculature from the VA-ECMO system.

Accelerating massively parallel hemodynamic models of coarctation of the aorta using neural networks.

Comorbidities such as anemia or hypertension and physiological factors related to exertion can influence a patient's hemodynamics and increase the severity of many cardiovascular diseases. Observing and quantifying associations between these factors and hemodynamics can be difficult due to the multitude of co-existing conditions and blood flow parameters in real patient data. Machine learning-driven, physics-based simulations provide a means to understand how potentially correlated conditions may affect a particular patient.

Determining the impacts of venoarterial extracorporeal membrane oxygenation on cerebral oxygenation using a one-dimensional blood flow simulator.

Venoarterial extracorporeal membrane oxygenation (VA-ECMO) is a mechanical system that provides rapid and short-term support for patients with cardiac failure. In many patients, pulmonary function is also impaired, resulting in poorly-oxygenated cardiac outflow competing against well-oxygenated VA-ECMO outflow, a condition known as North-South syndrome. North-South syndrome is a primary concern because of its potential to cause cerebral hypoxia, which has a critical influence on neurological complications often seen in this patient population.

Suitability of lattice Boltzmann inlet and outlet boundary conditions for simulating flow in image-derived vasculature.

The lattice Boltzmann method (LBM) is a popular alternative to solving the Navier-Stokes equations for modeling blood flow. When simulating flow using the LBM, several choices for inlet and outlet boundary conditions exist. While boundary conditions in the LBM have been evaluated in idealized geometries, there have been no extensive comparisons in image-derived vasculature, where the geometries are highly complex.