The Effects of Micro-vessel Curvature Induced Elongational Flows on Platelet Adhesion

Multiscale simulations that incorporate patient-specific neural network models of platelet calcium signaling predict diverse thrombotic outcomes under flow

During thrombosis, platelets rapidly deposit and activate on the vessel wall, driving conditions such as myocardial infarction and stroke. The complexity of thrombus formation in pathological flow geometries, along with patient-specific pharmacological responses, presents an opportunity for computational modeling to help deliver novel diagnostic and therapeutic insights. In the present study, we employed a multiscale 3D computational model that incorporates unique donor-derived neural networks (NNs) trained with platelet calcium mobilization traces under combinatorial exposure to 6 agonists (n = 10 donors). The 3D model comprises four modules: a donor-specific NN model for platelet calcium mobilization, a lattice kinetic Monte Carlo solver for tracking platelet motion and bonding, a finite volume method solver for modeling soluble agonist release and convective-diffusive transport, and a lattice Boltzmann method solver for predicting the blood velocity field. Simulations were conducted for platelets from individual blood donors under venous and arterial flow conditions on a defined collagen surface, examining the effects of inhibiting ADP and TXA2, as well as the influence of nitric oxide and prostacyclin. The results reveal significant individual variability in platelet responses, influencing simulated thrombus growth dynamics and emphasizing the importance of personalized models for predicting thrombotic behavior. This approach enables consideration of patient-specific platelet signaling, drug responses, and vascular geometry for predicting thrombotic episodes, essential for advancing precision medicine and improving patient outcomes in thrombotic conditions.

Carotid single- and dual-layer stents reduce the wall adhesion of platelets by influencing flow and cellular transport

An ongoing thrombosis on a ruptured atherosclerotic plaque in the carotid may cause stroke. The primary treatment for patients with tandem lesion is stenting. Dual-layer stents have been introduced as an alternative to single-layer stents for elective and emergent carotid artery stenting. While the dual-layer structure shows promise in reducing plaque prolapse through the stent struts and with it the occurrence of post-procedural embolism, there are early signs that this newer generation of stents is more thrombogenic. We investigate a single- and a dual-layer stent design to assess their influence on a set of thrombosis-related flow factors in a novel setup of combined experiments and simulations. The in vitro results reveal that both stents reduce thrombus formation by approximately 50% when human anticoagulated whole blood was perfused through macrofluidic flow chambers coated with either collagen or human atherosclerotic plaque homogenates. Simulations predict that the primary cause is reduced platelet presence in the vicinity of the wall, due to the influence of stents on flow and cellular transport. Both stents significantly alter the near-wall flow conditions, modifying shear rate, shear gradient, cell-free zones, and platelet availability. Additionally, the dual-layer stent has further increased local shear rates on the inner struts. It also displays increased stagnation zones and reduced recirculation between the outer-layer struts. Finally, the dual-layer stent shows further reduced adhesion over an atherosclerotic plaque coating. The novel approach presented here can be used to improve the design optimization process of cardiovascular stents in the future by allowing an in-depth study of the emerging flow characteristics and agonist transport.

The effect of flow-derived mechanical cues on the growth and morphology of platelet aggregates under low, medium, and high shear rates

Platelet aggregation is a dynamic process that can obstruct blood flow, leading to cardiovascular diseases. While many studies have demonstrated clear connections between shear rate and platelet aggregation, the impact of flow-derived mechanical signals on this process is not fully understood. The objective of this work is to investigate the role of flow conditions on platelet aggregation dynamics, including effects on growth, shape, density composition, and their potential correlation with binding processes that are characterised by longer (e.g., via αIIbβ3 integrin) and shorter (e.g., via VWF) initial binding times. In vitro blood perfusion experiments were conducted at wall shear rates of 800, 1600 and 4000 s-1. Detailed analysis of two modalities of experimental images was performed to offer insights into the morphology of platelet aggregates. A consistent structural pattern was observed across all samples: a high-density core enveloped by a low-density outer shell. An image-based 3D computational blood flow model was subsequently employed to study the local flow conditions, including binding availability time and flow-derived mechanical signals via shear rate and rate of elongation. The results show substantial dependence of the aggregation dynamics on these flow parameters. We found that the different binding mechanisms that prefer different flow regimes do not have a monotonic cross-over in efficiency as the flow increases. There is a significant dip in the cumulative aggregation potential in-between the preferred regimes. The results suggest that treatments targeting the biomechanical pathways could benefit from creating conditions that exploit these low-efficiency zones of aggregation.

The volume of healthy red blood cells is optimal for advective oxygen transport in arterioles

Red blood cells (RBCs) are vital for transporting oxygen from the lungs to the body's tissues through the intricate circulatory system. They achieve this by binding and releasing oxygen molecules to the abundant hemoglobin within their cytosol. The volume of RBCs affects the amount of oxygen they can carry, yet whether this volume is optimal for transporting oxygen through the circulatory system remains an open question. This study explores, through high-fidelity numerical simulations, the impact of RBC volume on advective oxygen transport efficiency through arterioles, which form the area of greatest flow resistance in the circulatory system. The results show that, strikingly, RBCs with volumes similar to those found in vivo are most efficient to transport oxygen through arterioles. The flow resistance is related to the cell-free layer thickness, which is influenced by the shape and the motion of the RBCs: at low volumes, RBCs deform and fold, while at high volumes, RBCs collide and follow more diffuse trajectories. In contrast, RBCs with a healthy volume maximize the cell-free layer thickness, resulting in a more efficient advective transport of oxygen.

Numerical simulation of cellular blood flow in curved micro-vessels with saccular aneurysms: Effect of curvature degree and hematocrit level

The dynamics of cellular blood flow in curved vessels considerably differ from those in straight vessels. It is reported that clotting development is significantly affected by vessel shape irregularities. Thus, the current study aims to investigate the effect of curvature degree and hematocrit level on cellular blood flow in a curved micro-vessel with a saccular aneurysm. Accordingly, a three-dimensional numerical simulation is performed using a validated code developed for cellular blood flow problems. The obtained results show that the cell-free layer thickness is highly dependent on the curvature degree and hematocrit level, which may have a remarkable impact on the apparent viscosity of blood as well as the dynamics of other particles such as drug particulates. The near-wall region exhibits the highest degree of cell deformation, whereas the red blood cells within the aneurysm zone remain nearly undeformed. Meanwhile, the velocity of the red blood cells decreases with the increase in curvature degree, which can affect the quality of the oxygenation process. Because of the saccular aneurysm, a considerable decrease in plasma velocity is predicted. Moreover, no secondary flows are detected in the curved vessel except in the aneurysm zone. An increase in the curvature degree is expected to reduce the blood flow rate by about 10%. Furthermore, low wall shear stress values are predicted in the straight case compared to the values at the apex of the curved vessel, which may affect the structure and function of the endothelial cells of the vessel wall and, hence, increase the aneurysm rupture possibility.

Cellular Blood Flow Modeling with HemoCell

Many of the intriguing properties of blood originate from its cellular nature. Bulk effects, such as viscosity, depend on the local shear rates and on the size of the vessels. While empirical descriptions of bulk rheology are available for decades, their validity is limited to the experimental conditions they were observed under. These are typically artificial scenarios (e.g., perfectly straight glass tube or in pure shear with no gradients). Such conditions make experimental measurements simpler; however, they do not exist in real systems (i.e., in a real human circulatory system). Therefore, as we strive to increase our understanding on the cardiovascular system and improve the accuracy of our computational predictions, we need to incorporate a more comprehensive description of the cellular nature of blood. This, however, presents several computational challenges that can only be addressed by high performance computing. In this chapter, we describe HemoCell ( https://www.hemocell.eu ), an open-source high-performance cellular blood flow simulation, which implements validated mechanical models for red blood cells and is capable of reproducing the emergent transport characteristics of such a complex cellular system. We discuss the accuracy and the range of validity, and demonstrate applications on a series of human diseases.

Image-based flow simulation of platelet aggregates under different shear rates

Hemodynamics is crucial for the activation and aggregation of platelets in response to flow-induced shear. In this paper, a novel image-based computational model simulating blood flow through and around platelet aggregates is presented. The microstructure of aggregates was captured by two different modalities of microscopy images of in vitro whole blood perfusion experiments in microfluidic chambers coated with collagen. One set of images captured the geometry of the aggregate outline, while the other employed platelet labelling to infer the internal density. The platelet aggregates were modelled as a porous medium, the permeability of which was calculated with the Kozeny-Carman equation. The computational model was subsequently applied to study hemodynamics inside and around the platelet aggregates. The blood flow velocity, shear stress and kinetic force exerted on the aggregates were investigated and compared under 800 s-1, 1600 s-1 and 4000 s-1 wall shear rates. The advection-diffusion balance of agonist transport inside the platelet aggregates was also evaluated by local Péclet number. The findings show that the transport of agonists is not only affected by the shear rate but also significantly influenced by the microstructure of the aggregates. Moreover, large kinetic forces were found at the transition zone from shell to core of the aggregates, which could contribute to identifying the boundary between the shell and the core. The shear rate and the rate of elongation flow were investigated as well. The results imply that the emerging shapes of aggregates are highly correlated to the shear rate and the rate of elongation. The framework provides a way to incorporate the internal microstructure of the aggregates into the computational model and yields a better understanding of the hemodynamics and physiology of platelet aggregates, hence laying the foundation for predicting aggregation and deformation under different flow conditions.

Application of machine learning in predicting blood flow and red cell distribution in capillary vessel networks

Capillary blood vessels in the body partake in the exchange of gas and nutrients with tissues. They are interconnected via multiple vascular junctions forming the microvascular network. Distributions of blood flow and red cells (RBCs) in such networks are spatially uneven and vary in time. Since they dictate the pathophysiology of tissues, their knowledge is important. Theoretical models used to obtain flow and RBC distribution in large networks have limitations as they treat each vessel as a one-dimensional segment and do not explicitly consider cell-cell and cell-vessel interactions. High-fidelity computational models that accurately model each individual RBC are computationally too expensive to predict haemodynamics in large vascular networks and over a long time. Here we investigate the applicability of machine learning (ML) techniques to predict blood flow and RBC distributions in physiologically realistic vascular networks. We acquire data from high-fidelity simulations of deformable RBC suspension flowing in the networks. With the flow and haematocrit specified at an inlet of vasculature, the ML models predict the time-averaged flow rate and RBC distributions in the entire network, time-dependent flow rate and haematocrit in each vessel and vascular bifurcation in isolation over a long time, and finally, simultaneous spatially and temporally evolving quantities through the vessel hierarchy in the networks.