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1. Decellularization Following Fixation of Explanted Aortic Valves as a Strategy for Preserving Native Mechanical Properties and Function

   https://doi.org/10.3389/fbioe.2021.803183  

   Singh, Manisha; Park, Clara; Roche, Ellen T. (January 2022, Frontiers in Bioengineering and Biotechnology)

   Mechanical or biological aortic valves are incorporated in physical cardiac simulators for surgical training, educational purposes, and device testing. They suffer from limitations including either a lack of anatomical and biomechanical accuracy or a short lifespan, hence limiting the authentic hands-on learning experience. Medical schools utilize hearts from human cadavers for teaching and research, but these formaldehyde-fixed aortic valves contort and stiffen relative to native valves. Here, we compare a panel of different chemical treatment methods on explanted porcine aortic valves and evaluate the microscopic and macroscopic features of each treatment with a primary focus on mechanical function. A surfactant-based decellularization method after formaldehyde fixation is shown to have mechanical properties close to those of the native aortic valve. Valves treated in this method were integrated into a custom-built left heart cardiac simulator to test their hemodynamic performance. This decellularization, post-fixation technique produced aortic valves which have ultimate stress and elastic modulus in the range of the native leaflets. Decellularization of fixed valves reduced the valvular regurgitation by 60% compared to formaldehyde-fixed valves. This fixation method has implications for scenarios where the dynamic function of preserved valves is required, such as in surgical trainers or device test rigs. 

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2. ParaValve: An open source framework for parametric design and fluid–structure interaction simulation of bioprosthetic heart valves in patient-specific aortic geometries

   https://doi.org/10.1016/j.cagd.2025.102455  

   Saraeian, Mehdi; Corpuz, Ashton M; Hsu, Ming-Chen; Krishnamurthy, Adarsh (July 2025, Computer Aided Geometric Design)

   Heart valve disease (HVD), a significant cardiovascular complication, is one of the leading global causes of morbidity and mortality. Treatment for HVD often involves medical devices such as bioprosthetic valves. However, the design and optimization of these devices require a thorough understanding of their biomechanical and hemodynamic interactions with patient-specific anatomical structures. Parametric procedural geometry has become a powerful tool in enhancing the efficiency and accuracy of design optimization for such devices, allowing researchers to systematically explore a wide range of possible configurations. In this work, we present a robust framework for parametric and procedural modeling of stented bioprosthetic heart valves and patient-specific aortic geometries. The framework employs non-uniform rational B-splines (NURBS)-based geometric parameterization, enabling precise control over key anatomical and design variables. By enabling a modular and expandable workflow, the framework supports iterative optimization of valve designs to achieve improved hemodynamic performance and durability. We demonstrate its applicability through simulations on bioprosthetic aortic valves, highlighting the impact of geometric parameters on valve function and their potential for personalized device design. By coupling parametric geometry with computational tools, this framework offers researchers and engineers a streamlined pathway toward innovative and patient-specific cardiovascular solutions. 

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3. Development and external validation of a dynamic risk score for early prediction of cardiogenic shock in cardiac intensive care units using machine learning

   https://doi.org/10.1093/ehjacc/zuae037  

   Hu, Yuxuan; Lui, Albert; Goldstein, Mark; Sudarshan, Mukund; Tinsay, Andrea; Tsui, Cindy; Maidman, Samuel D; Medamana, John; Jethani, Neil; Puli, Aahlad; et al (March 2024, European Heart Journal: Acute Cardiovascular Care)

   Abstract AimsMyocardial infarction and heart failure are major cardiovascular diseases that affect millions of people in the USA with morbidity and mortality being highest among patients who develop cardiogenic shock. Early recognition of cardiogenic shock allows prompt implementation of treatment measures. Our objective is to develop a new dynamic risk score, called CShock, to improve early detection of cardiogenic shock in the cardiac intensive care unit (ICU). Methods and resultsWe developed and externally validated a deep learning-based risk stratification tool, called CShock, for patients admitted into the cardiac ICU with acute decompensated heart failure and/or myocardial infarction to predict the onset of cardiogenic shock. We prepared a cardiac ICU dataset using the Medical Information Mart for Intensive Care-III database by annotating with physician-adjudicated outcomes. This dataset which consisted of 1500 patients with 204 having cardiogenic/mixed shock was then used to train CShock. The features used to train the model for CShock included patient demographics, cardiac ICU admission diagnoses, routinely measured laboratory values and vital signs, and relevant features manually extracted from echocardiogram and left heart catheterization reports. We externally validated the risk model on the New York University (NYU) Langone Health cardiac ICU database which was also annotated with physician-adjudicated outcomes. The external validation cohort consisted of 131 patients with 25 patients experiencing cardiogenic/mixed shock. CShock achieved an area under the receiver operator characteristic curve (AUROC) of 0.821 (95% CI 0.792–0.850). CShock was externally validated in the more contemporary NYU cohort and achieved an AUROC of 0.800 (95% CI 0.717–0.884), demonstrating its generalizability in other cardiac ICUs. Having an elevated heart rate is most predictive of cardiogenic shock development based on Shapley values. The other top 10 predictors are having an admission diagnosis of myocardial infarction with ST-segment elevation, having an admission diagnosis of acute decompensated heart failure, Braden Scale, Glasgow Coma Scale, blood urea nitrogen, systolic blood pressure, serum chloride, serum sodium, and arterial blood pH. ConclusionThe novel CShock score has the potential to provide automated detection and early warning for cardiogenic shock and improve the outcomes for millions of patients who suffer from myocardial infarction and heart failure. 

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4. Investigating Seismocardiogram Patterns: A Computational Modeling of Cardiac Wall Motion Propagation to the Chest Surface

   https://doi.org/10.1115/IMECE2024-145441  

   Monfared, Mohammadali; Thibbotuwawa_Gamage, Peshala; Taebi, Amirtahà (November 2024, American Society of Mechanical Engineers)

   Abstract Cardiovascular diseases, the leading cause of global mortality, demand refined diagnostic methods. Seismocardiography (SCG), a noninvasive method of measuring cardiovascular-induced vibrations on the chest surface, offers promise in assessing cardiac function. The cardiac wall movements are transmitted to the organs around the heart and eventually damped onto the chest surface, where they manifest as visible vibrations. These chest surface vibrations can be measured using an accelerometer via SCG. Although SCG signals are widely used in literature, further investigations are needed to understand the genesis of their patterns under different pathophysiological conditions. The goal of this study is to improve our understanding of the origin of SCG signals by simulating the transmission of cardiac motion reaching the chest surface using finite element method, and linking back the patterns of the simulated SCG signals to specific cardiac events. The computational domain, extracted from 4D computed tomography (CT) images of a healthy subject, comprised the lungs, ribcage, and chest muscles and fat. Using the Lukas-Kanade algorithm, the cardiac wall motion was extracted from the 4D CT scan images and was used as a displacement boundary condition. The elastic material properties were assigned to the lungs, muscles, fat, and rib cage. The dorsoventral SCG component from the finite element modeling was compared with two actual SCG signals obtained from the literature. The left ventricular volume was also calculated from the CT scans and was used to interpret the SCG waveforms. Important cardiac phases were labeled on the SCG signal extracted from the computationally modeled acceleration map near the xiphoid. This type of analysis can provide insights into various cardiac parameters and SCG patterns corresponding to the mitral valve closing, mitral valve opening, aortic valve opening, and aortic valve closure. These findings suggested the effectiveness of this modeling approach in understanding the underlying sources of the SCG waveforms. 

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5. Cardiovascular hemodynamics in mice with tumor necrosis factor receptor—associated factor 2 mediated cytoprotection in the heart

   https://doi.org/10.3389/fcvm.2023.1064640  

   Marshall, Andrea G.; Neikirk, Kit; Vue, Zer; Beasley, Heather K.; Garza-Lopez, Edgar; Vang, Larry; Barongan, Taylor; Evans, Zoe; Crabtree, Amber; Spencer, Elsie; et al (May 2023, Frontiers in Cardiovascular Medicine)

   Introduction: Many studies in mice have demonstrated that cardiac-specific innate immune signaling pathways can be reprogrammed to modulate inflammation in response to myocardial injury and improve outcomes. While the echocardiography standard parameters of left ventricular (LV) ejection fraction, fractional shortening, end-diastolic diameter, and others are used to assess cardiac function, their dependency on loading conditions somewhat limits their utility in completely reflecting the contractile function and global cardiovascular efficiency of the heart. A true measure of global cardiovascular efficiency should include the interaction between the ventricle and the aorta (ventricular-vascular coupling, VVC) as well as measures of aortic impedance and pulse wave velocity. Methods: We measured cardiac Doppler velocities, blood pressures, along with VVC, aortic impedance, and pulse wave velocity to evaluate global cardiac function in a mouse model of cardiac-restricted low levels of TRAF2 overexpression that conferred cytoprotection in the heart. Results: While previous studies reported that response to myocardial infarction and reperfusion was improved in the TRAF2 overexpressed mice, we found that TRAF2 mice had significantly lower cardiac systolic velocities and accelerations, diastolic atrial velocity, aortic pressures, rate-pressure product, LV contractility and relaxation, and stroke work when compared to littermate control mice. Also, we found significantly longer aortic ejection time, isovolumic contraction and relaxation times, and significantly higher mitral early/atrial ratio, myocardial performance index, and ventricular vascular coupling in the TRAF2 overexpression mice compared to their littermate controls. We found no significant differences in the aortic impedance and pulse wave velocity. Discussion: While the reported tolerance to ischemic insults in TRAF2 overexpression mice may suggest enhanced cardiac reserve, our results indicate diminished cardiac function in these mice. 

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