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Tetralogy of Fallot is a congenital heart disease affecting newborns and involves stenosis of the right ventricular outflow tract (RVOT). Surgical correction often widens the RVOT with a transannular enlargement patch, but this causes issues including pulmonary valve insufficiency and progressive right ventricle failure. A monocusp valve can prevent pulmonary regurgitation; however, valve failure resulting from factors including leaflet design, morphology, and immune response can occur, ultimately resulting in pulmonary insufficiency. A multimodal platform to quantitatively evaluate the effect of shape, size, and material on clinical outcomes could optimize monocusp design. This study introduces a benchtop soft biorobotic heart model, a computational fluid model of the RVOT, and a monocusp valve made from an entirely biological cell-assembled extracellular matrix (CAM) to tackle the multifaceted issue of monocusp failure. The hydrodynamic and mechanical performance of RVOT repair strategies was assessed in biorobotic and computational platforms. The monocusp valve design was validated in vivo in ovine models through echocardiography, cardiac magnetic resonance, and catheterization. These models supported assessment of surgical feasibility, handling, suturability, and hemodynamic and mechanical monocusp capabilities. The CAM-based monocusp offered a competent pulmonary valve with regurgitation of 4.6 ± 0.9% and a transvalvular pressure gradient of 4.3 ± 1.4 millimeters of mercury after 7 days of implantation in sheep. The biorobotic heart model, in silico analysis, and in vivo RVOT modeling allowed iteration in monocusp design not now feasible in a clinical environment and will support future surgical testing of biomaterials for complex congenital heart malformations.

 

Heart failure with preserved ejection fraction (HFpEF) is a major challenge in cardiovascular medicine, accounting for ≈50% of all cases of heart failure. Despite the ongoing efforts, no medical device has yet received FDA approval. This is largely due to the lack of an in vivo model of the HFpEF hemodynamics, resulting in the inability to evaluate device effectiveness in vivo prior to clinical trials. Here, the development of a highly tunable porcine model of HFpEF hemodynamics is described using implantable soft robotic sleeves, where controlled actuation of a left ventricular and an aortic sleeve can recapitulate changes in ventricular compliance and afterload associated with a broad spectrum of HFpEF hemodynamic phenotypes. The feasibility of the proposed model in preclinical testing is demonstrated by evaluating the hemodynamic response of the model post‐implantation of an interatrial shunt device, which is found to be consistent with findings from in silico studies and clinical trials. This work overcomes limitations of prior HFpEF models, such as low hemodynamic accuracy, high costs, and long development phases. The versatile and adjustable platform introduced can transform HFpEF device development, aiming to enhance the lives of the 32 million people affected globally.

 

The increasing recognition of the right ventricle (RV) necessitates the development of RV-focused interventions, devices and testbeds. In this study, we developed a soft robotic model of the right heart that accurately mimics RV biomechanics and hemodynamics, including free wall, septal and valve motion. This model uses a biohybrid approach, combining a chemically treated endocardial scaffold with a soft robotic synthetic myocardium. When connected to a circulatory flow loop, the robotic right ventricle (RRV) replicates real-time hemodynamic changes in healthy and pathological conditions, including volume overload, RV systolic failure and pressure overload. The RRV also mimics clinical markers of RV dysfunction and is validated using an in vivo porcine model. Additionally, the RRV recreates chordae tension, simulating papillary muscle motion, and shows the potential for tricuspid valve repair and replacement in vitro. This work aims to provide a platform for developing tools for research and treatment for RV pathophysiology.

 

Granular hydrogel matrices have emerged as promising candidates for cell encapsulation, bioprinting, and tissue engineering. How- ever, it remains challenging to design and optimize these materials given their broad compositional and processing parameter space. Here, we combine experimentation and computation to create granular matrices composed of alginate-based bioblocks with controlled structure, rheological properties, and injectability pro- files. A custom machine learning pipeline is applied after each phase of experimentation to automatically map the multidimensional input-output patterns into condensed data-driven models. These models are used to assess generalizable predictability and define high-level design rules to guide subsequent phases of development and characterization. Our integrated, modular approach opens new avenues to understanding and controlling the behavior of complex soft materials. 

A soft robotics-driven model recreates patient-specific biomechanics and hemodynamics of cardiovascular disease. 

Severe diaphragm dysfunction can lead to respiratory failure and to the need for permanent mechanical ventilation. Yet permanent tethering to a mechanical ventilator through the mouth or via tracheostomy can hinder a patient’s speech, swallowing ability and mobility. Here we show, in a porcine model of varied respiratory insufficiency, that a contractile soft robotic actuator implanted above the diaphragm augments its motion during inspiration. Synchronized actuation of the diaphragm-assist implant with the native respiratory effort increased tidal volumes and maintained ventilation flow rates within the normal range. Robotic implants that intervene at the diaphragm rather than at the upper airway and that augment physiological metrics of ventilation may restore respiratory performance without sacrificing quality of life.

 

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. 

Preclinical models of aortic stenosis can induce left ventricular pressure overload and coarsely control the severity of aortic constriction. However, they do not recapitulate the haemodynamics and flow patterns associated with the disease. Here we report the development of a customizable soft robotic aortic sleeve that can mimic the haemodynamics and biomechanics of aortic stenosis. By allowing for the adjustment of actuation patterns and blood-flow dynamics, the robotic sleeve recapitulates clinically relevant haemodynamics in a porcine model of aortic stenosis, as we show via in vivo echocardiography and catheterization studies, and a combination of in vitro and computational analyses. Using in vivo and in vitro magnetic resonance imaging, we also quantified the four-dimensional blood-flow velocity profiles associated with the disease and with bicommissural and unicommissural defects re-created by the robotic sleeve. The design of the sleeve, which can be adjusted on the basis of computed tomography data, allows for the design of patient-specific devices that may guide clinical decisions and improve the management and treatment of patients with aortic stenosis.