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Bicuspid aortic valve (BAV) is the most common cardiac congenital abnormality with a high rate of concomitant aortic valve and ascending aorta (AAo) pathologic changes throughout the patient's lifetime. The etiology of BAV-related aortopathy was historically believed to be genetic. However, recent studies theorize that adverse hemodynamics secondary to BAVs also contribute to aortopathy, but their precise role, specifically, that of wall shear stress (WSS) magnitude and directionality remains controversial. Moreover, the primary therapeutic option for BAV patients is aortic valve replacement (AVR), but the role of improved post-AVR hemodynamics on aortopathy progression is also not well-understood. To address these issues, this study employs a computational fluid dynamics model to simulate personalized AAo hemodynamics before and after TAVR for a small cohort of 6 Left-Right fused BAV patients. Regional distributions of five hemodynamic metrics, namely, time-averaged wall shear stress (TAWSS) and oscillating shear index (OSI), divergence of wall shear (DWSS), helicity flux integral & endothelial cell activation potential (ECAP), which are hypothesized to be associated with potential aortic injury are computed in the root, proximal and distal ascending aorta. BAVs are characterized by strong, eccentric jets, with peak velocities exceeding 4 m/s and axially circulating flow away from the jets. Such conditions result in focused WSS loading along jet attachment regions on the lumen boundary and weaker, oscillating WSS on other regions. The jet attachment regions also show alternating streaks of positive and negative DWSS, which may increase risk for local tissue stretching. Large WSS magnitudes, strong helical flows and circumferential WSS have been previously implicated in the progression of BAV aortopathy. Post-intervention hemodynamics exhibit weaker, less eccentric jets. Significant reductions are observed in flow helicity, TAWSS and DWSS in localized regions of the proximal AAo. On the other hand, OSI increases post-intervention and ECAP is observed to be low in both pre- and post-intervention scenarios, although significant increases are also observed in this ECAP. These results indicate a significant alleviation of pathological hemodynamics post AVR. [ABSTRACT FROM AUTHOR]

Patients who receive transcatheter aortic valve replacement are at risk for leaflet thrombosis-related complications, and can benefit from continuous, longitudinal monitoring of the prosthesis. Conventional angiography modalities are expensive, hospital-centric and either invasive or employ potentially nephrotoxic contrast agents, which preclude their routine use. Heart sounds have been long recognized to contain valuable information about individual valve function, but the skill of auscultation is in decline due to its heavy reliance on the physician's proficiency leading to poor diagnostic repeatability. This subjectivity in diagnosis can be alleviated using machine learning techniques for anomaly detection. We present a computational and data-driven proof-of-concept analysis of a novel, auscultation-based technique for monitoring aortic valve, which is practical, non-invasive, and non-toxic. However, the underlying mechanisms leading to physiological and pathological heart sounds are not well-understood, which hinders development of such a technique. We first address this by performing direct numerical simulations of the complex interactions between turbulent blood flow in a canonical ascending aorta model and dynamic valve motion in 29 cases with healthy and stenotic valves. Using the turbulent pressure fluctuations on the aorta lumen boundary, we model the propagation of heart sounds, as elastic waves, through the patient's thorax. The heart sound may be recorded on the epidermal surface using a stethoscope/phonocardiograph. This approach allows us to correlate instantaneous hemodynamic phenomena and valve motion with the acoustic response. From this dataset we extract "acoustic signatures" of healthy and stenotic valves based on principal components of the recorded sound. These signatures are used to train a linear discriminant classifier by maximizing correlation between recorded heart sounds and valve status. We demonstrate that this classifier is capable of accurate prospective detection of anomalous valve function and that the principal component-based signatures capture prominent audible features of heart sounds, which have been historically used by physicians for diagnosis. Further development of such technology can enable inexpensive, safe and patient-centric at-home monitoring, and can extend beyond transcatheter valves to surgical as well as native valves. [ABSTRACT FROM AUTHOR]

Results from numerical simulations of two-dimensional, shear-thinning Carreau fluid flow over an unconfined circular cylinder are presented in this paper. Parametric sweeps are performed over the various Carreau model parameters, and trends of the time-averaged force coefficients and vortex characteristics are reported. In general, increased shear-thinning results in lower viscous forces on the body but greater pressure forces, resulting in a complex non-monotonic drag response. Lift forces generally increased with shear-thinning due to the dominant pressure contribution. The decrease in fluid viscosity also led to shorter vortex formation lengths and the consequent rise in the Strouhal frequency of vortex shedding. It is expected that these results will be useful for verification of computational models of unsteady non-Newtonian flows. [ABSTRACT FROM AUTHOR]