Abstract:Fluid homeostasis is required for life. Processes involved in fluid balance are strongly related to exchanges at the microvascular level. Computational models have been presented in the literature to analyze the microvascular-interstitial interactions. As far as we know, none of those models consider a physiological description for the lymphatic drainage-interstitial pressure relation. We develop a computational model that consists of a network of straight cylindrical vessels and an isotropic porous media with… Show more
“…The model described the flow within the gel by means of Darcy's equation, and the flow in the MVNs with the Poiseuille equation for laminar, fully developed flow, taking into account network junctions, and filtration through the capillary membrane, which was described by Equation . The model was solved by means of the finite element method using the GetFem++ software, as previously shown . The MVNs (1/4 of the length of the device, repeated spatially) were reconstructed from confocal images using the FIJI “skeletonize” function to compute the skeleton of the network.…”
In vitro prediction of physiologically relevant transport of therapeutic molecules across the microcirculation represents an intriguing opportunity to predict efficacy in human populations. On‐chip microvascular networks (MVNs) show physiologically relevant values of molecular permeability, yet like most systems, they lack an important contribution to transport: the ever‐present fluid convection through the endothelium. Quantification of transport through the MVNs by current methods also requires confocal imaging and advanced analytical techniques, which can be a bottleneck in industry and academic laboratories. Here, it is shown that by recapitulating physiological transmural flow across the MVNs, the concentration of small and large molecule therapeutics can be directly sampled in the interstitial fluid and analyzed using standard analytical techniques. The magnitudes of transport measured in MVNs reveal trends with molecular size and type (protein versus nonprotein) that are expected in vivo, supporting the use of the MVNs platform as an in vitro tool to predict distribution of therapeutics in vivo.
“…The model described the flow within the gel by means of Darcy's equation, and the flow in the MVNs with the Poiseuille equation for laminar, fully developed flow, taking into account network junctions, and filtration through the capillary membrane, which was described by Equation . The model was solved by means of the finite element method using the GetFem++ software, as previously shown . The MVNs (1/4 of the length of the device, repeated spatially) were reconstructed from confocal images using the FIJI “skeletonize” function to compute the skeleton of the network.…”
In vitro prediction of physiologically relevant transport of therapeutic molecules across the microcirculation represents an intriguing opportunity to predict efficacy in human populations. On‐chip microvascular networks (MVNs) show physiologically relevant values of molecular permeability, yet like most systems, they lack an important contribution to transport: the ever‐present fluid convection through the endothelium. Quantification of transport through the MVNs by current methods also requires confocal imaging and advanced analytical techniques, which can be a bottleneck in industry and academic laboratories. Here, it is shown that by recapitulating physiological transmural flow across the MVNs, the concentration of small and large molecule therapeutics can be directly sampled in the interstitial fluid and analyzed using standard analytical techniques. The magnitudes of transport measured in MVNs reveal trends with molecular size and type (protein versus nonprotein) that are expected in vivo, supporting the use of the MVNs platform as an in vitro tool to predict distribution of therapeutics in vivo.
“…Although the focus of the present work is mostly on the analysis and approximation of the proposed approach, we stress that it aims to build the mathematical foundations for tackling various applications involving 3D-1D mixed-dimensional PDEs, such as fluid-structure interaction of slender bodies [26], microcirculation and lymphatics [29,33], subsurface flow models with wells [8], and the electrical activity of neurons.…”
Coupled partial differential equations (PDEs) defined on domains with different dimensionality are usually called mixed-dimensional PDEs. We address mixed-dimensional PDEs on three-dimensional (3D) and one-dimensional (1D) domains, which gives rise to a 3D-1D coupled problem. Such a problem poses several challenges from the standpoint of existence of solutions and numerical approximation. For the coupling conditions across dimensions, we consider the combination of essential and natural conditions, which are basically the combination of Dirichlet and Neumann conditions. To ensure a meaningful formulation of such conditions, we use the Lagrange multiplier method suitably adapted to the mixed-dimensional case. The well-posedness of the resulting saddle-point problem is analyzed. Then, we address the numerical approximation of the problem in the framework of the finite element method. The discretization of the Lagrange multiplier space is the main challenge. Several options are proposed, analyzed, and compared, with the purpose of determining a good balance between the mathematical properties of the discrete problem and flexibility of implementation of the numerical scheme. The results are supported by evidence based on numerical experiments.
“…Shape functions are also used in algorithms to discharge the vascular flux into the homogenized domain [46]. Other groups developed sophisticated methods that coregistered the circumference of the tangential disc representing each cylinder with the surrounding tetrahedral mesh [33,34,[40][41][42][43] to avoid singularity issues. High quality dense meshing is required to effectively resolve gradients surrounding the smallest capillaries, so that adaptive meshing becomes necessary [33].…”
Section: Discussionmentioning
confidence: 99%
“…Blood flow was computed separately using a simplified Hagen-Poiseuille network model; then oxygen extraction to tissue was solved by projecting the segment oxygen tension to the refined triangular surface mesh of each vessel segment. Other groups avoided the body-fitted meshing by registering a vascular network to a tetrahedral mesh and distributing the transmembrane flux for each segment across many tetrahedral mesh elements [33,34,[40][41][42][43]. Unfortunately, reported problem sizes merely encompass up to a few thousand vascular segments, because the required mesh sizes for fitting a contiguous extravascular mesh with the vascular network graph increase dramatically especially at the microscale.…”
Departures of normal blood flow and metabolite distribution from the cerebral microvasculature into neuronal tissue have been implicated with age-related neurodegeneration. Mathematical models informed by spatially and temporally distributed neuroimage data are becoming instrumental for reconstructing a coherent picture of normal and pathological oxygen delivery throughout the brain. Unfortunately, current mathematical models of cerebral blood flow and oxygen exchange become excessively large in size. They further suffer from boundary effects due to incomplete or physiologically inaccurate computational domains, numerical instabilities due to enormous length scale differences, and convergence problems associated with condition number deterioration at fine mesh resolutions. Our proposed simple finite volume discretization scheme for blood and oxygen microperfusion simulations does not require expensive mesh generation leading to the critical benefit that it drastically reduces matrix size and bandwidth of the coupled oxygen transfer problem. The compact problem formulation yields rapid and stable convergence. Moreover, boundary effects can effectively be suppressed by generating very large replica of the cortical microcirculation in silico using an image-based cerebrovascular network synthesis algorithm, so that boundaries of the perfusion simulations are far removed from the regions of interest. Massive simulations over sizeable portions of the cortex with feature resolution down to the micron scale become tractable with even modest computer resources. The feasibility and accuracy of the novel method is demonstrated and validated with in vivo oxygen perfusion data in cohorts of young and aged mice. Our oxygen exchange simulations quantify steep gradients near penetrating blood vessels and point towards pathological changes that might cause neurodegeneration in aged brains. This research aims to explain mechanistic interactions between anatomical structures and how they might change in diseases or with age. Rigorous quantification of age-related changes is of significant interest because it might aide in the search for imaging biomarkers for dementia and Alzheimer’s disease.
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