A monolithic fluid-structure interaction framework applied to red blood cells

Numerical investigation on red blood cell flow based on unstructured grid

Prediction of blood cell flow is known as the difficult research by reason of the complexity of blood vessel. In this study, considering the complex structure of blood vessels, a mechanical model for red blood cell (RBC) based on unstructured grid has been established to study the flow characteristics of RBCs in complex blood vessels. In the model, the strain-energy function by Skalak is employed to model the shear elasticity and surface-area conservation of the membrane, and the hinge spring is used to describe the forces originating from local bending of the membrane. The immersed boundary method is utilized to couple the interphase force. Using the model, the stretching test of RBC is compared with the experiment data, and the good agreement verified the validation of the present model. The morphology of red blood cell and the blood viscosity in micro-vessel are studied. RBCs move with a symmetric shape (parachute shape) in small blood vessels, and the buckling instability is observed when the RBC flow slowly through a micro-vessel or a converging-diverging capillary. When the vessel diameter is around 10 μm, the reverse Fahraeus-Lindqvist effect is presented. The blood apparent viscosity shows linear increase with the blood hematocrit. In addition, Malaria infection can make the RBC deformability decreased and the blood apparent viscosity increased.

Modeling Dynamics of the Cardiovascular System Using Fluid-Structure Interaction Methods

Using fluid-structure interaction algorithms to simulate the human circulatory system is an innovative approach that can provide valuable insights into cardiovascular dynamics. Fluid-structure interaction algorithms enable us to couple simulations of blood flow and mechanical responses of the blood vessels while taking into account interactions between fluid dynamics and structural behaviors of vessel walls, heart walls, or valves. In the context of the human circulatory system, these algorithms offer a more comprehensive representation by considering the complex interplay between blood flow and the elasticity of blood vessels. Algorithms that simulate fluid flow dynamics and the resulting forces exerted on vessel walls can capture phenomena such as wall deformation, arterial compliance, and the propagation of pressure waves throughout the cardiovascular system. These models enhance the understanding of vasculature properties in human anatomy. The utilization of fluid-structure interaction methods in combination with medical imaging can generate patient-specific models for individual patients to facilitate the process of devising treatment plans. This review evaluates current applications and implications of fluid-structure interaction algorithms with respect to the vasculature, while considering their potential role as a guidance tool for intervention procedures.

Recent developments in modeling, imaging, and monitoring of cardiovascular diseases using machine learning

Cardiovascular diseases are the leading cause of mortality, morbidity, and hospitalization around the world. Recent technological advances have facilitated analyzing, visualizing, and monitoring cardiovascular diseases using emerging computational fluid dynamics, blood flow imaging, and wearable sensing technologies. Yet, computational cost, limited spatiotemporal resolution, and obstacles for thorough data analysis have hindered the utility of such techniques to curb cardiovascular diseases. We herein discuss how leveraging machine learning techniques, and in particular deep learning methods, could overcome these limitations and offer promise for translation. We discuss the remarkable capacity of recently developed machine learning techniques to accelerate flow modeling, enhance the resolution while reduce the noise and scanning time of current blood flow imaging techniques, and accurate detection of cardiovascular diseases using a plethora of data collected by wearable sensors.

Valid Presumption of Shiga Toxin-Mediated Damage of Developing Erythrocytes in EHEC-Associated Hemolytic Uremic Syndrome

The global emergence of clinical diseases caused by enterohemorrhagic Escherichia coli (EHEC) is an issue of great concern. EHEC release Shiga toxins (Stxs) as their key virulence factors, and investigations on the cell-damaging mechanisms toward target cells are inevitable for the development of novel mitigation strategies. Stx-mediated hemolytic uremic syndrome (HUS), characterized by the triad of microangiopathic hemolytic anemia, thrombocytopenia, and acute renal injury, is the most severe outcome of an EHEC infection. Hemolytic anemia during HUS is defined as the loss of erythrocytes by mechanical disruption when passing through narrowed microvessels. The formation of thrombi in the microvasculature is considered an indirect effect of Stx-mediated injury mainly of the renal microvascular endothelial cells, resulting in obstructions of vessels. In this review, we summarize and discuss recent data providing evidence that HUS-associated hemolytic anemia may arise not only from intravascular rupture of erythrocytes, but also from the extravascular impairment of erythropoiesis, the development of red blood cells in the bone marrow, via direct Stx-mediated damage of maturing erythrocytes, leading to “non-hemolytic” anemia.