Nature of aortic annulus: Influence of annulus dynamic on the aortic valve hemodynamics.

Accurate imaging reports of the aortic valve indicate that the diameter of the aortic annulus changes regularly during a cardiac cycle. Most of these studies aim to demonstrate the proper method for estimating the aortic annulus diameter before performing TAVR surgery, revealing that the aortic annulus is dynamic and not constant throughout the cardiac cycle. This raises the question of how fixing the aortic annulus might affect valve function, which is a question that still needs to be addressed. Therefore, the present study seeks to address this question and elucidate the dynamic impact of the aortic annulus on aortic valve hemodynamics. Two computational models based on this hypothesis were created and solved, and then their results were compared. Both models are identical, except for the intrinsic dynamic nature of the aortic annulus. One model consists of the dynamic behavior, and the other simulates a fixed annulus, resembling the effect of a TAVR operation, SAVR, or any phenomenon that eliminates the dynamic nature of the annulus. Our research findings indicate that the dynamic nature of the annulus enhances blood flow (+2.7 %), increases mean velocity (+11.9) and kinetic energy density (+34 %), prolongs momentum retention during systole, stabilizes the flow jet at the end of systole, reduces the required pressure to keep the leaflets open (-40.9 % at 0.3s), and sustains ventricular pressure superiority (+9.4 %) over the aorta for a longer duration (+17.7 % of systole), a crucial factor in preventing backflow during aortic valve closure. Based on these results, more attention should be paid to the dynamic nature of the annulus.

Fluid-Solid Interaction Analysis for Developing In-Situ Strain and Flow Sensors for Prosthetic Valve Monitoring.

Transcatheter aortic valve implantation (TAVI) was initially developed for adult patients, but there is a growing interest to expand this procedure to younger individuals with longer life expectancies. However, the gradual degradation of biological valve leaflets in transcatheter heart valves (THV) presents significant challenges for this extension. This study aimed to establish a multiphysics computational framework to analyze structural and flow measurements of TAVI and evaluate the integration of optical fiber and photoplethysmography (PPG) sensors for monitoring valve function. A two-way fluid-solid interaction (FSI) analysis was performed on an idealized aortic vessel before and after the virtual deployment of the SAPIEN 3 Ultra (S3) THV. Subsequently, an analytical analysis was conducted to estimate the PPG signal using computational flow predictions and to analyze the effect of different pressure gradients and distances between PPG sensors. Circumferential strain estimates from the embedded optical fiber in the FSI model were highest in the sinus of Valsalva; however, the optimal fiber positioning was found to be distal to the sino-tubular junction to minimize bending effects. The findings also demonstrated that positioning PPG sensors both upstream and downstream of the bioprosthesis can be used to effectively assess the pressure gradient across the valve. We concluded that computational modeling allows sensor design to quantify vessel wall strain and pressure gradients across valve leaflets, with the ultimate goal of developing low-cost monitoring systems for detecting valve deterioration.

Can mechanical heart valves perform similarly to tissue valves? An in vitro study.

Current surgical aortic valve (AV) replacement options include bioprosthetic and mechanical heart valves (MHVs), each with inherent limitations. Bioprosthetic valves offer superior hemodynamics but suffer from durability issues, typically initiating deterioration within 7-8 years. MHVs, while durable, necessitate lifelong anticoagulation therapy, presenting risks such as severe bleeding and thromboembolic events. The need for anticoagulants is caused by non-physiological flow through the hinge area during the closed phase and large spikes of regional backflow velocity (RBV) during the closing phase that produces high shear events. This study introduces the iValve, a novel MHV designed to combine the hemodynamic benefits of bioprosthetic valves with the durability of MHVs without requiring anticoagulation. The iValve features eye-like leaflets, a saddle-shaped housing, and an optimized hinge design to enhance blood flow and minimize thrombotic risk. Fabricated using 6061-T6 aluminum and polyether ether ketone (PEEK), twelve iValve iterations were evaluated for their opening and closing dynamics. The reported top-performing prototypes demonstrated competitive performance against industry standards. The proposed iValve prototype exhibited a mean RBV of -4.34 m/s with no spikes in RBV, performing similarly to bioprosthetic valves and significantly outperforming existing MHVs. The iValve's optimized design showed a 7-10% reduction in closing time and a substantial decrease in RBV spikes, potentially reducing the need for anticoagulation therapy. This study highlights the iValve's potential to revolutionize prosthetic heart valve technology by offering a durable, hemodynamically superior solution that mitigates the drawbacks of current MHVs.

An intelligent aortic valve model for complete cardiac cycle.

The aortic valve (AV) is crucial for cardiovascular (CV) hemodynamic, impacting cardiac output (CO) and left ventricular volumetric flow rate (LVQ). Its nonlinear behavior challenges standard LVQ prediction methods as well as CO one. This study presents a novel approach for modeling the AV in the CV system, offering an improved method for estimating crucial parameters like LVQ across various AV conditions, including aortic stenosis (AS). The model, based on AV channel length during the entire cardiac phase, introduces a time-varying AV resistance (TV-AVR) parameterized by the pressure ratio across the AV and LVQ, enabling the simulation of both healthy and AS-related conditions. To validate this model, in vitro measurements are compared using a hybrid mock circulatory loop device. An unconventional use of a convolutional neural network (CNN) corrects the model's estimates, eliminating the need for labeled datasets. This approach, incorporating real-time learning and transforming 1-D CV signals into 2-D tensors, significantly improves the accuracy of LVQ measurements, achieving an error rate of less than 3.41 ± 4.84% for CO in healthy conditions and 2.83 ± 1.35% in AS cases-a 33.13% enhancement over linear diode models. These results underscore the potential of this approach for enhancing the diagnosis, prediction, and treatment of AV diseases. The key contributions of the proposed method encompass nonlinear TV-AVR estimation, investigation of transient CV responses, prediction of instantaneous CO, development of a flexible framework for noninvasive measurements integration, and the introduction of an adjustable resistance model using an extended Kalman filter (EKF) and CNN combination, all without requiring labeled data.

A lumped parameter model for evaluating coronary artery blood supply capacity.

The coronary artery constitutes a vital vascular system that sustains cardiac function, with its primary role being the conveyance of indispensable nutrients to the myocardial tissue. When coronary artery disease occurs, it will affect the blood supply of the heart and induce myocardial ischemia. Therefore, it is of great significance to numerically simulate the coronary artery and evaluate its blood supply capacity. In this article, the coronary artery lumped parameter model was derived based on the relationship between circuit system parameters and cardiovascular system parameters, and the blood supply capacity of the coronary artery in healthy and stenosis states was studied. The aortic root pressure calculated by the aortic valve fluid-structure interaction (AV FSI) simulator was employed as the inlet boundary condition. To emulate the physiological phenomenon of sudden pressure drops resulting from an abrupt reduction in blood vessel radius, a head loss model was connected at the coronary artery's entrance. For each coronary artery outlet, the symmetric structured tree model was appended to simulate the terminal impedance of the missing downstream coronary arteries. The particle swarm optimization (PSO) algorithm was used to optimize the blood flow viscous resistance, blood flow inertia, and vascular compliance of the coronary artery model. In the stenosis states, the relative flow and fractional flow reserve (FFR) calculated by numerical simulation corresponded to the published literature data. It was anticipated that the proposed model can be readily adapted for clinical application, serving as a valuable reference for diagnosing and treating patients.

On the role of aortic valve architecture for physiological hemodynamics and valve replacement, Part I: Flow configuration and vortex dynamics.

Aortic valve replacement has become an increasing concern due to the rising prevalence of aortic stenosis in an ageing population. Existing replacement options have limitations, necessitating the development of improved prosthetic aortic valves. In this study, flow characteristics during systole in a stenotic aortic valve case are compared with those downstream of two newly designed surgical bioprosthetic aortic valves (BioAVs). To do so, advanced three-dimensional fluid-structure interaction simulations are conducted and dedicated analysis methods to investigate jet flow configuration and vortex dynamics are developed. Our findings reveal that the stenotic case maintains a high jet flow eccentricity due to a fixed orifice geometry, resulting in flow separation and increased vortex stretching and tilting in the commissural low-flow regions. One BioAV design introduces non-axisymmetric leaflet motion, which reduces the maximum jet velocity and forms more vortical structures. The other BioAV design produces a fixed symmetric triangular jet shape due to non-moving leaflets and exhibits favourable vorticity attenuation, revealed by negative temporally and spatially averaged projected vortex stretching values, and significantly reduced drag. Therefore, this study highlights the benefits of custom-designed aortic valves in the context of their replacement through comprehensive and novel flow analyses. The results emphasise the importance of analysing jet flow, vortical structures, momentum balance and vorticity transport for thoroughly evaluating aortic valve performance.

Changes in aorta hemodynamics in Left-Right Type 1 bicuspid aortic valve patients after replacement with bioprosthetic valves: An in-silico study.

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.

Smoothed particle hydrodynamics based FSI simulation of the native and mechanical heart valves in a patient-specific aortic model.

The failure of the aortic heart valve is common, resulting in deterioration of the pumping function of the heart. For the end stage valve failure, bi-leaflet mechanical valve (most popular artificial valve) is implanted. However, due to its non-physiological behaviour, a significant alteration is observed in the normal haemodynamics of the aorta. While in-vivo experimentation of a human heart valve (native and artificial) is a formidable task, in-silico study using computational fluid dynamics (CFD) with fluid structure interaction (FSI) is an effective and economic tool for investigating the haemodynamics of natural and artificial heart valves. In the present work, a haemodynamic model of a natural and mechanical heart valve has been developed using meshless particle-based smoothed particle hydrodynamics (SPH). In order to further enhance its clinical relevance, this study employs a patient-specific vascular geometry and presents a successful validation against traditional finite volume method and 4D magnetic resonance imaging (MRI) data. The results have demonstrated that SPH is ideally suited to simulate the heart valve function due to its Lagrangian description of motion, which is a favourable feature for FSI. In addition, a novel methodology for the estimation of the wall shear stress (WSS) and other related haemodynamic parameters have been proposed from the SPH perspective. Finally, a detailed comparison of the haemodynamic parameters has been carried out for both native and mechanical aortic valve, with a particular emphasis on the clinical risks associated with the mechanical valve.

Three-dimensional fluid-structure interaction simulation of the Wheatley aortic valve.

Valvular heart diseases (such as stenosis and regurgitation) are recognized as a rapidly growing cause of global deaths and major contributors to disability. The most effective treatment for these pathologies is the replacement of the natural valve with a prosthetic one. Our work considers an innovative design for prosthetic aortic valves that combines the reliability and durability of artificial valves with the flexibility of tissue valves. It consists of a rigid support and three polymer leaflets which can be cut from an extruded flat sheet, and is referred to hereafter as the Wheatley aortic valve (WAV). As a first step towards the understanding of the mechanical behavior of the WAV, we report here on the implementation of a numerical model built with the ICFD multi-physics solver of the LS-DYNA software. The model is calibrated and validated using data from a basic pulsatile-flow experiment in a water-filled straight tube. Sensitivity to model parameters (contact parameters, mesh size, etc.) and to design parameters (height, material constants) is studied. The numerical data allow us to describe the leaflet motion and the liquid flow in great detail, and to investigate the possible failure modes in cases of unfavorable operational conditions (in particular, if the leaflet height is inadequate). In future work the numerical model developed here will be used to assess the thrombogenic properties of the valve under physiological conditions.

Wall shear stress and pressure patterns in aortic stenosis patients with and without aortic dilation captured by high-performance image-based computational fluid dynamics.

Spatial patterns of elevated wall shear stress and pressure due to blood flow past aortic stenosis (AS) are studied using GPU-accelerated patient-specific computational fluid dynamics. Three cases of moderate to severe AS, one with a dilated ascending aorta and two within the normal range (root diameter less than 4cm) are simulated for physiological waveforms obtained from echocardiography. The computational framework is built based on sharp-interface Immersed Boundary Method, where aortic geometries segmented from CT angiograms are integrated into a high-order incompressible Navier-Stokes solver. The key question addressed here is, given the presence of turbulence due to AS which increases wall shear stress (WSS) levels, why some AS patients undergo much less aortic dilation. Recent case studies of AS have linked the existence of an elevated WSS hotspot (due to impingement of AS on the aortic wall) to the dilation process. Herein we further investigate the WSS distribution for cases with and without dilation to understand the possible hemodynamics which may impact the dilation process. We show that the spatial distribution of elevated WSS is significantly more focused for the case with dilation than those without dilation. We further show that this focal area accommodates a persistent pocket of high pressure, which may have contributed to the dilation process through an increased wall-normal forcing. The cases without dilation, on the contrary, showed a rather oscillatory pressure behaviour, with no persistent pressure "buildup" effect. We further argue that a more proximal branching of the aortic arch could explain the lack of a focal area of elevated WSS and pressure, because it interferes with the impingement process due to fluid suction effects. These phenomena are further illustrated using an idealized aortic geometry. We finally show that a restored inflow eliminates the focal area of elevated WSS and pressure zone from the ascending aorta.

Modeling isovolumetric phases in cardiac flows by an Augmented Resistive Immersed Implicit Surface method.

A major challenge in the computational fluid dynamics modeling of the heart function is the simulation of isovolumetric phases when the hemodynamics problem is driven by a prescribed boundary displacement. During such phases, both atrioventricular and semilunar valves are closed: consequently, the ventricular pressure may not be uniquely defined, and spurious oscillations may arise in numerical simulations. These oscillations can strongly affect valve dynamics models driven by the blood flow, making unlikely to recovering physiological dynamics. Hence, prescribed opening and closing times are usually employed, or the isovolumetric phases are neglected altogether. In this article, we propose a suitable modification of the Resistive Immersed Implicit Surface (RIIS) method (Fedele et al., Biomech Model Mechanobiol 2017, 16, 1779-1803) by introducing a reaction term to correctly capture the pressure transients during isovolumetric phases. The method, that we call Augmented RIIS (ARIIS) method, extends the previously proposed ARIS method (This et al., Int J Numer Methods Biomed Eng 2020, 36, e3223) to the case of a mesh which is not body-fitted to the valves. We test the proposed method on two different benchmark problems, including a new simplified problem that retains all the characteristics of a heart cycle. We apply the ARIIS method to a fluid dynamics simulation of a realistic left heart geometry, and we show that ARIIS allows to correctly simulate isovolumetric phases, differently from standard RIIS method. Finally, we demonstrate that by the new method the cardiac valves can open and close without prescribing any opening/closing times.

Real-Time Seismocardiogram Feature Extraction Using Adaptive Gaussian Mixture Models.

Wearable systems can provide accurate cardiovascular evaluations by estimating hemodynamic indices in real-time. Key hemodynamic parameters can be non-invasively estimated using the seismocardiogram (SCG), a cardiomechanical signal whose features link to cardiac events like aortic valve opening (AO) and closing (AC). However, tracking a single SCG feature is unreliable due to physiological changes, motion artifacts, and external vibrations. This work proposes an adaptable Gaussian Mixture Model (GMM) to track multiple AO/AC correlated features in quasi-real-time from the SCG. The GMM calculates the likelihood of an extremum being an AO/AC feature for each SCG beat. The Dijkstra algorithm selects heartbeat-related extrema, and a Kalman filter updates the GMM parameters while filtering features. Tracking accuracy is tested on a porcine hypovolemia dataset with varying noise levels. Blood volume loss estimation accuracy is also evaluated using the tracked features on a previously developed model. Experimental results show a 4.5 ms tracking latency and average root mean square errors (RMSE) of 1.47 ms for AO and 7.67 ms for AC at 10 dB noise, and 6.18 ms for AO and 15.3 ms for AC at -10 dB noise. When considering all AO/AC correlated features, the combined RMSE remains in similar ranges, specifically 2.70 ms for AO and 11.91 ms for AC at 10 dB noise, and 7.50 ms for AO and 16.35 ms for AC at -10 dB noise. The proposed algorithm offers low latency and RMSE for all tracked features, making it suitable for real-time processing. These systems enable accurate, timely extraction of hemodynamic indices for many cardiovascular monitoring applications, including trauma care in field settings.

Electrocardiogram to Determine Mitral and Aortic Valve Opening and Closure.

PURPOSE: Knowledge of the timing of cardiac valve opening and closing is important in cardiac physiology. The relationship between valve motion and electrocardiogram (ECG) is often assumed, however is not clearly defined. Here we investigate the accuracy of cardiac valve timing estimated using only the ECG, compared to Doppler echocardiography (DE) flow imaging as the gold standard. METHODS: DE was obtained in 37 patients with simultaneous ECG recording. ECG was digitally processed and identifiable features (QRS, T, P waves) were examined as potential reference points to determine opening and closure of aortic and mitral valves, as compared to DE outflow and inflow measurement. Timing offset of the cardiac valves opening and closure between ECG features and DE was measured from derivation set (n = 19). The obtained mean offset in combination with the ECG features model was then evaluated on a validation set (n = 18). Using the same approach, additional measurement was also done for the right sided valves. RESULTS: From the derivation set, we found a fixed offset of 22 ± 9 ms, 2 ± 13 ms, 90 ± 26 ms, and - 2 ± - 27 ms when comparing S to aortic valve opening, Tend to aortic valve closure, Tend to mitral valve opening, and R to mitral valve closure respectively. Application of this model to the validation set showed good estimation of aortic and mitral valve opening and closure timing value, with low model absolute error (median of the mean absolute error of the four events = 19 ms compared to the gold standard DE measurement). For the right-sided (tricuspid and pulmonic) valves in our patient set, there was considerably higher median of the mean absolute error of 42 ms for the model. CONCLUSION: ECG features can be used to estimate aortic and mitral valve timings with good accuracy as compared to DE, allowing useful hemodynamic information to be derived from this easily available test.

A parametric geometry model of the aortic valve for subject-specific blood flow simulations using a resistive approach.

Cardiac valves simulation is one of the most complex tasks in cardiovascular modeling. Fluid-structure interaction is not only highly computationally demanding but also requires knowledge of the mechanical properties of the tissue. Therefore, an alternative is to include valves as resistive flow obstacles, prescribing the geometry (and its possible changes) in a simple way, but, at the same time, with a geometry complex enough to reproduce both healthy and pathological configurations. In this work, we present a generalized parametric model of the aortic valve to obtain patient-specific geometries that can be included into blood flow simulations using a resistive immersed implicit surface (RIIS) approach. Numerical tests are presented for geometry generation and flow simulations in aortic stenosis patients whose parameters are extracted from ECG-gated CT images.

Effect of aortic curvature on bioprosthetic aortic valve performance.

Transvalvular pressure gradient (ΔP) after aortic valve replacement is an important surrogate of aortic bioprostheses performance. Invasive ΔP is often measured after transcatheter aortic valve replacement to exclude patient-prosthetic mismatch. However, invasive aortic pressures are usually recorded in the pressure recovery (PR) zone downstream of the valve, potentially resulting in ΔP underestimation compared to noninvasive measurements. PR was extensively studied in straight ascending aortas. However, the impact of various aortic arch configurations on ΔP has not been explored. PR was assessed in a pulse duplicating simulator at various cardiac conditions of cardiac output, heart rates and pressures. Three different aortic geometries with identical root dimensions but with different aortic arches were used: (1) curvature 1, (2) curvature 2, and (3) straight aortic models. Instantaneous pressure and peak ΔP measurements were recorded incrementally along the models for each cardiac condition. The models with aortic arches produced two distinct PR zones (after the valve and after the aortic arch), whereas the model without an aortic arch produced only one PR zone (after the valve). The trend of the pressure and ΔP curves for each model was independent of the cardiac condition used, but the individually measured pressure magnitudes did change with different conditions. In this study, we illustrated the differences in PR between distinct aortic curvatures and straight aorta. PR affects pressure and ΔP measurements. These effects are clear when recording aortic pressures by catheterization and echocardiography.

Semi-Automated Construction of Patient-Specific Aortic Valves from Computed Tomography Images.

This paper presents a semi-automatic method for the construction of volumetric models of the aortic valve using computed tomography angiography images. Although the aortic valve typically cannot be segmented directly from a computed tomography angiography image, the method described herein uses manually selected samples of an aortic segmentation derived from this image to inform the construction. These samples capture certain physiologic landmarks and are used to construct a volumetric valve model. As a demonstration of the capabilities of this method, valve models for 25 pediatric patients are created. A selected valve anatomy is used to perform fluid-structure interaction simulations using the immersed finite element/difference method with physiologic driving and loading conditions. Simulation results demonstrate this method creates a functional valve that opens and closes normally and generates pressure and flow waveforms that are similar to those observed clinically.

Modelling the rate-dependency of the mechanical behaviour of the aortic heart valve: An experimentally guided theoretical framework.

A theoretical framework, based on extant experimental findings, is presented to devise a novel viscous dissipation function Wv in order to model the rate-dependent mechanical behaviour of the aortic heart valve. The experimental data encompasses Cauchy stress-stretch (σ-λ) curves obtained across a 10,000-fold range of stretch rates (λ˙), from quasi-static (λ˙= 0.001 s-1) to upper-range of physiological (λ˙= 12.4 s-1) deformation rates. The analysis of the data elicits two important trends: (i) the mechanical behaviour of the aortic valve across the tested rates is rate-dependent, with specimens becoming stiffer by increasing rate; and (ii) there appears to be a plateau in the rate-effects on the σ-λ curves; i.e. the rate-effects approach an asymptote with increase in the stretch rate λ˙. Guided by these empirical observations, we devise our new Wv function and demonstrate that the well-known form of the dissipation function commonly used in the literature is a special case of our proposed Wv. The ensuing model is then compared against the experimental σ-λ curves and is shown to provide favourable predictions. An important advantage of the employed modelling framework is that it allows the incorporation of the rate of deformation, which is a direct experimental control parameter, as an explicit modelling variable. The application of the proposed model is thereby recommended for heart valves and other soft tissues that exhibit similar rate-dependent features.

Data-driven approach for automatic detection of aortic valve opening: B point detection from impedance cardiogram.

Pre-ejection period (PEP), an indicator of sympathetic nervous system activity, is useful in psychophysiology and cardiovascular studies. Accurate PEP measurement is challenging and relies on robust identification of the timing of aortic valve opening, marked as the B point on impedance cardiogram (ICG) signals. The ICG sensitivity to noise and its waveform's morphological variability makes automated B point detection difficult, requiring inefficient and cumbersome expert visual annotation. In this article, we propose a machine learning-based automated algorithm to detect the aortic valve opening for PEP measurement, which is robust against noise and ICG morphological variations. We analyzed over 60 hr of synchronized ECG and ICG records from 189 subjects. A total of 3657 averaged beats were formed using our recently developed ICG noise removal algorithm. Features such as the averaged ICG waveform, its first and second derivatives, as well as high-level morphological and critical hemodynamic parameters were extracted and fed into the regression algorithms to estimate the B point location. The morphological features were extracted from our proposed "variable" physiologically valid search-window related to diverse B point shapes. A subject-wise nested cross-validation procedure was performed for parameter tuning and model assessment. After examining multiple regression models, Adaboost was selected, which demonstrated superior performance and higher robustness to five state-of-the-art algorithms that were evaluated in terms of low mean absolute error of 3.5 ms, low median absolute error of 0.0 ms, high correlation with experts' estimates (Pearson coefficient = 0.9), and low standard deviation of errors of 9.2 ms. For reproducibility, an open-source toolbox is provided.

A computational analysis of potential aortic dilation induced by the hemodynamic effects of bicuspid aortic valve phenotypes.

BACKGROUND AND OBJECTIVES: The bicuspid aortic valve (BAV) is a major risk factor for the progression of aortic dilation (AD) because of the induced abnormal blood flow environment in aorta. The differences in the development of AD induced by BAV phenotypes remains unclear. Therefore, the objective of this study was to assess the potential locations of AD induced by different phenotypes of BAV. The different effects of opening orifice area and leaflet orientation on ascending aortic hemodynamics in Type-1 BAV was investigated by means of numerical simulation. METHODS: Finite element dynamic analysis was performed on tricuspid aortic valve (TAV) and BAV models to simulate the motion of the leaflets and obtain the geometrical characteristics of AV at peak systole as a reference, which were used for aortic models. Then, four sets of aortic fluid models were designed according to the leaflet fusion types [TAV; BAV (left-right-coronary cusp fusion, LR; right-non-coronary cusp fusion, RN; left-non-coronary cusp fusion, LN)], and the computational fluid dynamics method was applied to compare the hemodynamic differences within the aorta at peak systole. RESULTS: The maximum opening area of BAV was significantly reduced, resulting in alterations in aortic hemodynamics compared with TAV. The velocity streamlines were essentially parallel to the aortic wall in TAV. The average pressure and wall shear stress in aorta tend to be stable. In contrary, the eccentricity of BAV orifice jet resulted in high-velocity flow directed toward the ascending aorta (AA) wall and aortic arch for LR and LN; RN features an asymmetrical velocity distribution toward the outer bend of the middle AA, and eccentric flow tends to impact the distal AA. As the flow angle is associated with distinct flow impingement locations, different degrees of WSS and pressure concentration occur along the aortic wall from the AA to the aortic arch in three BAV types. CONCLUSIONS: The BAV morphotype affects the aortic hemodynamics, and the abnormal blood flow associated with BAV may play a role in AD. The different BAV phenotypes determine the direction of blood flow jet and change the expression of dilation. LR is likely to cause dilation of the tubular AA; RN results in dilation of the middle AA to proximal aortic arch; and LN causes an increased incidence of the tubular AA and the proximal aortic arch.

Network modeling predicts personalized gene expression and drug responses in valve myofibroblasts cultured with patient sera.

Aortic valve stenosis (AVS) patients experience pathogenic valve leaflet stiffening due to excessive extracellular matrix (ECM) remodeling. Numerous microenvironmental cues influence pathogenic expression of ECM remodeling genes in tissue-resident valvular myofibroblasts, and the regulation of complex myofibroblast signaling networks depends on patient-specific extracellular factors. Here, we combined a manually curated myofibroblast signaling network with a data-driven transcription factor network to predict patient-specific myofibroblast gene expression signatures and drug responses. Using transcriptomic data from myofibroblasts cultured with AVS patient sera, we produced a large-scale, logic-gated differential equation model in which 11 biochemical and biomechanical signals were transduced via a network of 334 signaling and transcription reactions to accurately predict the expression of 27 fibrosis-related genes. Correlations were found between personalized model-predicted gene expression and AVS patient echocardiography data, suggesting links between fibrosis-related signaling and patient-specific AVS severity. Further, global network perturbation analyses revealed signaling molecules with the most influence over network-wide activity, including endothelin 1 (ET1), interleukin 6 (IL6), and transforming growth factor ß (TGFß), along with downstream mediators c-Jun N-terminal kinase (JNK), signal transducer and activator of transcription (STAT), and reactive oxygen species (ROS). Lastly, we performed virtual drug screening to identify patient-specific drug responses, which were experimentally validated via fibrotic gene expression measurements in valvular interstitial cells cultured with AVS patient sera and treated with or without bosentan-a clinically approved ET1 receptor inhibitor. In sum, our work advances the ability of computational approaches to provide a mechanistic basis for clinical decisions including patient stratification and personalized drug screening.