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1. In silico analyses of blood flow and oxygen transport in human micro-veins and valves

   https://doi.org/10.3233/CH-211345  

   Rajeeva Pandian, Navaneeth Krishna ; Jain, Abhishek ( April 2022 , Clinical Hemorheology and Microcirculation)

   BACKGROUND: Almost 95% of the venous valves are micron scale found in veins smaller than 300μm diameter. The fluid dynamics of blood flow and transport through these micro venous valves and their contribution to thrombosis is not yet well understood or characterized due to difficulty in making direct measurements in murine models. OBJECTIVE: The unique flow patterns that may arise in physiological and pathological non-actuating micro venous valves are predicted. METHODS: Computational fluid and transport simulations are used to model blood flow and oxygen gradients in a microfluidic vein. RESULTS: The model successfully recreates the typical non-Newtonian vortical flow within the valve cusps seen in preclinical experimental models and in clinic. The analysis further reveals variation in the vortex strengths due to temporal changes in blood flow. The cusp oxygen is typically low from the main lumen, and it is regulated by systemic venous flow. CONCLUSIONS: The analysis leads to a clinically-relevant hypothesis that micro venous valves may not create a hypoxic environment needed for endothelial inflammation, which is one of the main causes of thrombosis. However, incompetent micro venous valves are still locations for complex fluid dynamics of blood leading to low shear regions that may contribute to thrombosismore »through other pathways.« less
2. A Deep Learning Framework for Sickle Cell Disease Microfluidic Biomarker Assays

   https://doi.org/10.1182/blood-2020-141693  

   Praljak, Niksa ; Shipley, Brandon ; Gonzales, Ayesha ; Goreke, Utku ; Iram, Shamreen ; Singh, Gundeep ; Hill, Ailis ; Gurkan, Umut A. ; Hinczewski, Michael ( November 2020 , Blood)

   Introduction: Vaso-occlusive crises (VOCs) are a leading cause of morbidity and early mortality in individuals with sickle cell disease (SCD). These crises are triggered by sickle red blood cell (sRBC) aggregation in blood vessels and are influenced by factors such as enhanced sRBC and white blood cell (WBC) adhesion to inflamed endothelium. Advances in microfluidic biomarker assays (i.e., SCD Biochip systems) have led to clinical studies of blood cell adhesion onto endothelial proteins, including, fibronectin, laminin, P-selectin, ICAM-1, functionalized in microchannels. These microfluidic assays allow mimicking the physiological aspects of human microvasculature and help characterize biomechanical properties of adhered sRBCs under flow. However, analysis of the microfluidic biomarker assay data has so far relied on manual cell counting and exhaustive visual morphological characterization of cells by trained personnel. Integrating deep learning algorithms with microscopic imaging of adhesion protein functionalized microfluidic channels can accelerate and standardize accurate classification of blood cells in microfluidic biomarker assays. Here we present a deep learning approach into a general-purpose analytical tool covering a wide range of conditions: channels functionalized with different proteins (laminin or P-selectin), with varying degrees of adhesion by both sRBCs and WBCs, and in both normoxic and hypoxic environments. Methods: Our neuralmore »networks were trained on a repository of manually labeled SCD Biochip microfluidic biomarker assay whole channel images. Each channel contained adhered cells pertaining to clinical whole blood under constant shear stress of 0.1 Pa, mimicking physiological levels in post-capillary venules. The machine learning (ML) framework consists of two phases: Phase I segments pixels belonging to blood cells adhered to the microfluidic channel surface, while Phase II associates pixel clusters with specific cell types (sRBCs or WBCs). Phase I is implemented through an ensemble of seven generative fully convolutional neural networks, and Phase II is an ensemble of five neural networks based on a Resnet50 backbone. Each pixel cluster is given a probability of belonging to one of three classes: adhered sRBC, adhered WBC, or non-adhered / other. Results and Discussion: We applied our trained ML framework to 107 novel whole channel images not used during training and compared the results against counts from human experts. As seen in Fig. 1A, there was excellent agreement in counts across all protein and cell types investigated: sRBCs adhered to laminin, sRBCs adhered to P-selectin, and WBCs adhered to P-selectin. Not only was the approach able to handle surfaces functionalized with different proteins, but it also performed well for high cell density images (up to 5000 cells per image) in both normoxic and hypoxic conditions (Fig. 1B). The average uncertainty for the ML counts, obtained from accuracy metrics on the test dataset, was 3%. This uncertainty is a significant improvement on the 20% average uncertainty of the human counts, estimated from the variance in repeated manual analyses of the images. Moreover, manual classification of each image may take up to 2 hours, versus about 6 minutes per image for the ML analysis. Thus, ML provides greater consistency in the classification at a fraction of the processing time. To assess which features the network used to distinguish adhered cells, we generated class activation maps (Fig. 1C-E). These heat maps indicate the regions of focus for the algorithm in making each classification decision. Intriguingly, the highlighted features were similar to those used by human experts: the dimple in partially sickled RBCs, the sharp endpoints for highly sickled RBCs, and the uniform curvature of the WBCs. Overall the robust performance of the ML approach in our study sets the stage for generalizing it to other endothelial proteins and experimental conditions, a first step toward a universal microfluidic ML framework targeting blood disorders. Such a framework would not only be able to integrate advanced biophysical characterization into fast, point-of-care diagnostic devices, but also provide a standardized and reliable way of monitoring patients undergoing targeted therapies and curative interventions, including, stem cell and gene-based therapies for SCD. Disclosures Gurkan: Dx Now Inc.: Patents & Royalties; Xatek Inc.: Patents & Royalties; BioChip Labs: Patents & Royalties; Hemex Health, Inc.: Consultancy, Current Employment, Patents & Royalties, Research Funding.« less
3. Multilayer microfluidic platform for the study of luminal, transmural, and interstitial flow

   https://doi.org/10.1088/1758-5090/ac48e5  

   Lee, Gi-hun ; Huang, Stephanie A. ; Aw, Wen Y. ; Rathod, Mitesh L. ; Cho, Crescentia ; Ligler, Frances S. ; Polacheck, William J. ( January 2022 , Biofabrication)

   Efficient delivery of oxygen and nutrients to tissues requires an intricate balance of blood, lymphatic, and interstitial fluid pressures (IFPs), and gradients in fluid pressure drive the flow of blood, lymph, and interstitial fluid through tissues. While specific fluid mechanical stimuli, such as wall shear stress, have been shown to modulate cellular signaling pathways along with gene and protein expression patterns, an understanding of the key signals imparted by flowing fluid and how these signals are integrated across multiple cells and cell types in native tissues is incomplete due to limitations with current assays. Here, we introduce a multi-layer microfluidic platform (MμLTI-Flow) that enables the culture of engineered blood and lymphatic microvessels and independent control of blood, lymphatic, and IFPs. Using optical microscopy methods to measure fluid velocity for applied input pressures, we demonstrate varying rates of interstitial fluid flow as a function of blood, lymphatic, and interstitial pressure, consistent with computational fluid dynamics (CFD) models. The resulting microfluidic and computational platforms will provide for analysis of key fluid mechanical parameters and cellular mechanisms that contribute to diseases in which fluid imbalances play a role in progression, including lymphedema and solid cancer.
4. Shear rate dependent margination of sphere-like, oblate-like and prolate-like micro-particles within blood flow

   https://doi.org/10.1039/C8SM01304G  

   Ye, Huilin ; Shen, Zhiqiang ; Li, Ying ( January 2018 , Soft Matter)

   This study investigates the shear rate dependent margination of micro-particles (MPs) with different shapes in blood flow through numerical simulations. We develop a multiscale computational model to handle the fluid–structure interactions involved in the blood flow simulations. The lattice Boltzmann method (LBM) is used to solve the plasma dynamics and a coarse-grained model is employed to capture the dynamics of red blood cells (RBCs) and MPs. These two solvers are coupled together by the immersed boundary method (IBM). The shear rate dependent margination of sphere MPs is firstly investigated. We find that margination of sphere MPs dramatically increases with the increment of wall shear rate  ω under 800 s −1 , induced by the breaking of rouleaux in blood flow. However, the margination probability only slowly grows when  ω > 800 s −1 . Furthermore, the shape effect of MPs is examined by comparing the margination behaviors of sphere-like, oblate-like and prolate-like MPs under different wall shear rates. We find that the margination of MPs is governed by the interplay of two factors: hydrodynamic collisions with RBCs including the collision frequency and collision displacement of MPs, and near wall dynamics. MPs that demonstrate poor performance in one processmore »such as collision frequency may stand out in the other process like near wall dynamics. Specifically, the ellipsoidal MPs (oblate and prolate) with small aspect ratio (AR) outperform those with large AR regardless of the wall shear rate, due to their better performance in both the collision with RBCs and near wall dynamics. Additionally, we find there exists a transition shear rate region 700 s −1 <  ω < 900 s −1 for all of these MPs: the margination probability dramatically increases with the shear rate below this region and slowly grows above this region, similar to sphere MPs. We further use the surface area to volume ratio (SVR) to distinguish different shaped MPs and illustrate their shear rate dependent margination in a contour in the shear rate–SVR plane. It is of significance that we can approximately predict the margination of MPs with a specific SVR. All these simulation results can be potentially applied to guide the design of micro-drug carriers for biomedical applications.« less
5. Shear-induced gradient diffusivity of a red blood cell suspension: effects of cell dynamics from tumbling to tank-treading

   https://doi.org/10.1039/d1sm00938a  

   Malipeddi, Abhilash Reddy ; Sarkar, Kausik ( September 2021 , Soft Matter)

   Hydrodynamic interactions generate a diffusive motion in particulates in a shear flow, which plays seminal roles in overall particulate rheology and its microstructure. Here we investigate the shear induced diffusion in a red-blood cell (RBC) suspension using a numerical simulation resolving individual motion and deformation of RBCs. The non-spherical resting shape of RBCs gives rise to qualitatively different regimes of cell dynamics in a shear flow such as tank-treading, breathing, tumbling and swinging, depending on the cell flexibility determined by the elastic capillary number. We show that the transition from tumbling to tank-treading causes a reduction in the gradient diffusivity. The diffusivity is computed using a continuum approach from the evolution of a randomly packed cell-layer width with time as well as by the dynamic structure factor of the suspension. Both approaches, although operationally different, match and show that for intermediate capillary numbers RBCs cease tumbling accompanied by a drop in the coefficient of gradient diffusivity. A further increase of capillary number increases the diffusivity due to increased deformation. The effects of bending modulus and viscosity ratio variations are also briefly investigated. The computed shear induced diffusivity was compared with values in the literature. Apart from its effects in marginationmore »of cells in blood flow and use in medical diagnostics, the phenomenon broadly offers important insights into suspensions of deformable particles with non-spherical equilibrium shapes, which also could play a critical role in using particle flexibility for applications such as label free separation or material processing.« less