Optimal cell transport in straight channels and networks

Farutin, Alexander; Shen, Zaiyi; Prado, Gaël; Audemar, Vassanti; Ez‐Zahraouy, H.; Benyoussef, A.; Polack, Benoı̂t; Harting, Jens; Vlahovska, Petia M.; Podgorski, Thomas; Coupier, Gwennou; Misbah, Chaouqi

doi:10.1103/physrevfluids.3.103603

Cited by 10 publications

(15 citation statements)

References 96 publications

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“…Several other detailed theoretical modelling studies (3,4) yield the optimal hematocrit value of about 0.4 as well. This shows that even a highly simplified model (1,2) can lead to relevant results.…”

Section: Introductionmentioning

confidence: 75%

“…However, according to the calculations (1)(2)(3)(4), the optimal value should be independent of the level of exertion. This is in contradiction to the optimal hematocrit value of 0.5-0.6 found during a blood perfusion of isolated working muscle at a constant perfusion pressure (6).…”

Section: Introductionmentioning

confidence: 99%

“…Even placebo effects have been mentioned to solve this "hematocrit paradox" and the topic remains controversial (8). A drawback of the idea about a positive effect of increased hematocrit is the absence of a clear theoretical explanation: why should the increased hematocrit promote performance, when the theoretical models (1,3,4) predict the optimum to be at normal values even at exertion.…”

Section: Introductionmentioning

confidence: 99%

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Calculating the optimal hematocrit under the constraint of constant cardiac power

Šitina

Stark

Schuster

2020

Preprint

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In humans and higher animals, a trade-off between sufficiently high erythrocyte concentrations to bind oxygen and sufficiently low blood viscosity to allow rapid blood flow has been achieved during evolution. Optimal hematocrit theory has been successful in predicting hematocrit values of about 0.3 -0.5, in very good agreement with the normal values observed for humans and many animal species. However, according to those calculations, the optimal value should be independent of the mechanical load of the body. This is in contradiction to the exertional increase in hematocrit observed in some animals called natural blood dopers and to the illegal practice of blood boosting in high-performance sports. In this study, we calculate the optimal hematocrit under two different constraints -under a constant driving pressure and under constant cardiac power -and show that the optimal hematocrit under constant cardiac power is higher than the normal value, ranging from 0.5 to 0.7. We use this result to explain the tendency to better exertional performance at an increased hematocrit. Statement of SignificanceIn humans and higher animals, erythrocytes comprise a volume fraction (hematocrit) of 30-50 % of the blood. Mathematical calculations based on the assumption of constant blood pressure show that the optimal hematocrit value is indeed in that range. However, this optimum should apply to both rest and physical exertion, which is in contradiction to the increase in hematocrit observed in some animals called natural blood dopers and to the illegal practice of blood boosting in sports. Here, we calculate the optimal value based on the alternative constraint of constant cardiac power. We show that this results in a higher optimal value, ranging from 50 to 70 %. In this way, we explain the better exertional performance at an increased hematocrit.3

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Section: Introductionmentioning

confidence: 75%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Calculating the optimal hematocrit under the constraint of constant cardiac power

Šitina

Stark

Schuster

2020

Preprint

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“…Past work in physiology has argued that this loading of blood with red blood cells maximizes the flux of oxygen delivered to the body and have analyzed empirical models of blood viscosity to justify these arguments. [3][4][5][6] Likewise, the idea that nature optimizes fluxes of a single solute in a Newtonian fluid under different mechanical constraints has been explored in other contexts including the delivery of nutrients in plants and the sipping of nectar from flowers by hummingbirds. [7][8][9] While there is some overlap between the present work and these past efforts, we will abstract away from the natural context to the artificially engineered one.…”

Section: Introductionmentioning

confidence: 99%

Optimal loading for injection

2020

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The injection of fluids loaded with a precise number of particles, polymers, and other solutes is common in many areas of chemical engineering. By definition, injection of these fluids is meant to occur over the shortest possible duration. This raises the question that is answered in this note: At what concentration should a fluid be loaded in order to inject that fluid fastest? A similar question has been addressed for flows of Newtonian fluids in biophysical and physiological studies. We generalize that analysis. We show for Newtonian fluids containing a single suspended component that the optimal loading is determined from a common tangent construction for the viscosity as a function of concentration. We extend this formulation to describe optimal injection of a multicomponent Newtonian fluid. Additionally, we study the injection problem for a simple, model non-Newtonian fluid carrying a single suspended component. Finally, we discuss applications for optimally loaded injections.

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“…Simulations of soft particles in a single-component fluid are well established in the LBM literature. Typical examples are fluid-filled capsules and cells [9,11,19,20], vesicles [21][22][23], and particles with three-dimensional elasticity [24,25]. Comparably, there are various approaches to model rigid particles in multi-component fluids.…”

Section: Introductionmentioning

confidence: 99%

Mesoscale simulation of soft particles with tunable contact angle in multicomponent fluids

2019

Self Cite

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Soft particles at fluid interfaces play an important role in many aspects of our daily life, such as the food industry, paints and coatings, and medical applications. Analytical methods are not capable of describing the emergent effects of the complex dynamics of suspensions of many soft particles, whereas experiments typically either only capture bulk properties or require invasive methods. Computational methods are therefore a great tool to complement experimental work. However, an efficient and versatile numerical method is needed to model dense suspensions of many soft particles. In this article we propose a method to simulate soft particles in a multi-component fluid, both at and near fluid-fluid interfaces, based on the lattice Boltzmann method, and characterize the error stemming from the fluid-structure coupling for the particle equilibrium shape when adsorbed onto a fluid-fluid interface. Furthermore, we characterize the influence of the preferential contact angle of the particle surface and the particle softness on the vertical displacement of the center of mass relative to the fluid interface. Finally, we demonstrate the capability of our model by simulating a soft capsule adsorbing onto a fluid-fluid interface with a shear flow parallel to the interface, and the covering of a droplet suspended in another fluid by soft particles with different wettability.

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Optimal cell transport in straight channels and networks

Cited by 10 publications

References 96 publications

Calculating the optimal hematocrit under the constraint of constant cardiac power

Calculating the optimal hematocrit under the constraint of constant cardiac power

Optimal loading for injection

Mesoscale simulation of soft particles with tunable contact angle in multicomponent fluids

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