Methods of exploring the red blood cells rotation during the simulations in devices with periodic topology

Bachratý, Hynek; Kovalčíková, Kristína; Bachratá, Katarína; Slavík, Martin

doi:10.1109/dt.2017.8024269

Cited by 9 publications

(4 citation statements)

References 5 publications

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“…The applications mentioned so far considered a system of bases for a position of the RBC center and its velocity. Using a similar approach described in [14] and [33], it is possible to obtain data about other RBC characteristics, which describe the general cell properties, e.g. rotation and inclination, or more specific properties, e.g.…”

Section: Extension To Other Types Of Datamentioning

confidence: 99%

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Applications of machine learning for simulations of red blood cells in microfluidic devices

et al. 2020

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Background For optimization of microfluidic devices for the analysis of blood samples, it is useful to simulate blood cells as elastic objects in flow of blood plasma. In such numerical models, we primarily need to take into consideration the movement and behavior of the dominant component of the blood, the red blood cells. This can be done quite precisely in small channels and within a short timeframe. However, larger volumes or timescales require different approaches. Instead of simplifying the simulation, we use a neural network to predict the movement of the red blood cells. Results The neural network uses data from the numerical simulation for learning, however, the simulation needs only be run once. Alternatively, the data could come from video processing of a recording of a biological experiment. Afterwards, the network is able to predict the movement of the red blood cells because it is a system of bases that gives an approximate cell velocity at each point of the simulation channel as a linear combination of bases.In a simple box geometry, the neural network gives results comparable to predictions using fluid streamlines, however in a channel with obstacles forming slits, the neural network is about five times more accurate.The network can also be used as a discriminator between different situations. We observe about two-fold increase in mean relative error when a network trained on one geometry is used to predict trajectories in a modified geometry. Even larger increase was observed when it was used to predict trajectories of cells with different elastic properties. Conclusions While for uncomplicated box channels there is no advantage in using a system of bases instead of a simple prediction using fluid streamlines, in a more complicated geometry, the neural network is significantly more accurate. Another application of this system of bases is using it as a comparison tool for different modeled situations. This has a significant future potential when applied to processing data from videos of microfluidic flows.

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Section: Extension To Other Types Of Datamentioning

confidence: 99%

“…The behavior is characterized primarily by cell positions, i.e. trajectories, velocities and more generally by cell rotations, inclinations and areas of occurrence [14].…”

Section: Introductionmentioning

confidence: 99%

Applications of machine learning for simulations of red blood cells in microfluidic devices

et al. 2020

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“…The first experiments with these geometries are described in articles [11], [12], [10]. For our experiment, we used geometries with the same number of obstacles that were the same size, the only difference between these geometries was the distance of the obstacles.…”

Section: Rbc's Trajectories Data Processing For Various Geometriesmentioning

confidence: 99%

Processing of Cells’ Trajectories Data for Blood Flow Simulation Model*

et al. 2018

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Abstract. Simulations of the red blood cells (RBCs) flow as a movement of elastic objects in a fluid, are developed to optimize microfluidic devices used for a blood sample analysis for diagnostic purposes in the medicine. Tracking cell behaviour during simulation helps to improve the model and adjust its parameters. For the optimization of the microfluidic devices, it is also necessary to analyse cell trajectories as well as likelihood and frequency of their occurrence in a particular device area, especially in the parts, where they can affect circulating tumour cells capture. In this article, we propose and verify several ways of processing and analysing the typology and trajectory stability in simulations with single or with a large number of red blood cells (RBCs) in devices with different topologies containing cylindrical obstacles.

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“…式中，a 为换算参数(J)，d、d cut 分别为细胞之间的距离和相互作用势为零时的距离(m)， n 为比例系数(m -1 )，表征细胞之间相互作用的强弱。本文取 a=5×10 -18 J， d cut =0.5 µm，n=2.0 µm -1 [30] 。纳米颗粒在毛细血管中的输运过程中，颗粒与颗粒、红细胞之间的相互作用不可忽略。采用 L-J 势能函数来表示纳米颗粒间的相互作用力 [31] ，由公式(8) 表示： 12 6 ( ) 4 Vr rr -15 J，ζ=0.2 µm [32] 。采用 Morse 势能函数来表示纳米颗粒与红细胞之间的相互作用力 [31] ，由公式(9)表示：…”

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Numerical simulation of transport of deformable drug nanoparticles in capillaries

Yan¹,

Le²,

Zhang³

et al. 2022

Sci. Sin.-Vitae

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Methods of exploring the red blood cells rotation during the simulations in devices with periodic topology

Cited by 9 publications

References 5 publications

Applications of machine learning for simulations of red blood cells in microfluidic devices

Applications of machine learning for simulations of red blood cells in microfluidic devices

Processing of Cells’ Trajectories Data for Blood Flow Simulation Model*

Numerical simulation of transport of deformable drug nanoparticles in capillaries

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