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      Numerical study of ultra-large von Willebrand factor multimers in coagulopathy

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          A multiscale red blood cell model with accurate mechanics, rheology, and dynamics.

          Red blood cells (RBCs) have highly deformable viscoelastic membranes exhibiting complex rheological response and rich hydrodynamic behavior governed by special elastic and bending properties and by the external/internal fluid and membrane viscosities. We present a multiscale RBC model that is able to predict RBC mechanics, rheology, and dynamics in agreement with experiments. Based on an analytic theory, the modeled membrane properties can be uniquely related to the experimentally established RBC macroscopic properties without any adjustment of parameters. The RBC linear and nonlinear elastic deformations match those obtained in optical-tweezers experiments. The rheological properties of the membrane are compared with those obtained in optical magnetic twisting cytometry, membrane thermal fluctuations, and creep followed by cell recovery. The dynamics of RBCs in shear and Poiseuille flows is tested against experiments and theoretical predictions, and the applicability of the latter is discussed. Our findings clearly indicate that a purely elastic model for the membrane cannot accurately represent the RBC's rheological properties and its dynamics, and therefore accurate modeling of a viscoelastic membrane is necessary. Copyright 2010 Biophysical Society. Published by Elsevier Inc. All rights reserved.
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            LATTICE BOLTZMANN METHOD FOR FLUID FLOWS

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              A Model for Collision Processes in Gases. I. Small Amplitude Processes in Charged and Neutral One-Component Systems

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                Author and article information

                Journal
                Biomechanics and Modeling in Mechanobiology
                Biomech Model Mechanobiol
                Springer Science and Business Media LLC
                1617-7959
                1617-7940
                June 2024
                January 13 2024
                June 2024
                : 23
                : 3
                : 737-756
                Article
                10.1007/s10237-023-01803-5
                626c94ae-9494-4d57-9ca6-947ef7116f5a
                © 2024

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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