Renal blood flow and oxygenation

Our kidneys receive about one-fifth of the cardiac output at rest and have a low oxygen extraction ratio, but may sustain, under some conditions, hypoxic injuries that might lead to chronic kidney disease. This is due to large regional variations in renal blood flow and oxygenation, which are the prerequisite for some and the consequence of other kidney functions. The concurrent operation of these functions is reliant on a multitude of neuro-hormonal signaling cascades and feedback loops that also include the regulation of renal blood flow and tissue oxygenation. Starting with open questions on regulatory processes and disease mechanisms, we review herein the literature on renal blood flow and oxygenation. We assess the current understanding of renal blood flow regulation, reasons for disparities in oxygen delivery and consumption, and the consequences of disbalance between O2 delivery, consumption, and removal. We further consider methods for measuring and computing blood velocity, flow rate, oxygen partial pressure, and related parameters and point out how limitations of these methods constitute important hurdles in this area of research. We conclude that to obtain an integrated understanding of the relation between renal function and renal blood flow and oxygenation, combined experimental and computational modeling studies will be needed. Keywords: Autoregulation; Kidney; Oxygenation; Perfusio

Propagation of Plasma L-Phenylalanine Concentration Fluctuations to the Neurovascular Unit in Phenylketonuria: An in silico Study

Phenylketonuria (PKU) is an inherited metabolic disease characterized by abnormally high concentrations of the essential amino acid L-phenylalanine (Phe) in blood plasma caused by reduced activity of phenylalanine hydroxylase (PAH). While numerous studies have shown association between high plasma Phe concentration and intellectual impairment, it is not clear whether increased Phe fluctuations also observed in PKU affect the brain as well. To investigate this, time-resolved in vivo data on Phe and competing large neutral amino acid (LNAA) concentrations in neurons are needed, but cannot be acquired readily with current methods. We have used in silico modeling as an alternative approach to characterize the interactive dynamics of Phe and competing LNAAs (CL) in the neurovascular unit (NVU). Our results suggest that plasma Phe fluctuations can propagate into the NVU cells and change there the concentration of LNAAs, with the highest magnitude of this effect observed at low frequency and high amplitude-to-mean ratio of the plasma Phe concentration fluctuations. Our model further elucidates the effect of therapeutic LNAA supplementation in PKU, showing how abnormal concentrations of Phe and CL in the NVU move thereby toward normal physiologic levels

Stabilized reduced-order models for unsteady incompressible flows in three-dimensional parametrized domains

In this work we derive a parametric reduced-order model (ROM) for the unsteady three-dimensional incompressible Navier–Stokes equations without additional pre-processing on the reduced-order subspaces. Concerning the high-fidelity, full-order model, we start from a streamline-upwind Petrov–Galerkin stabilized finite element discretization of the equations using elements for velocity and pressure, respectively. We rely on Galerkin projection of the discretized equations onto reduced basis subspaces for the velocity and the pressure, respectively, obtained through Proper Orthogonal Decomposition on a dataset of snapshots of the full-order model. Both nonlinear and nonaffinely parametrized algebraic operators of the reduced-order system of nonlinear equations, including the projection of the stabilization terms, are efficiently assembled exploiting the Discrete Empirical Interpolation Method (DEIM), and its matrix version (MDEIM), thus obtaining an efficient offline–online computational splitting. We apply the proposed method to (i) a two-dimensional lid-driven cavity flow problem, considering the Reynolds number as parameter, and (ii) a three-dimensional pulsatile flow in stenotic vessels characterized by geometric and physiological parameter variations. We numerically show that the projection of the stabilization terms on the reduced basis subspace and their reconstruction using (M)DEIM allows to obtain a stable ROM with coupled velocity and pressure solutions, without any need for enriching the reduced velocity space, or further stabilizing the ROM. Additionally, we demonstrate that our implementation allows to compute the ROM solution about 20 times faster than the full order model

The breakup of intravascular microbubbles and its impact on the endothelium

Encapsulated microbubbles (MBs) serve as endovascular agents in a wide range of medical ultrasound applications. The oscillatory response of these agents to ultrasonic excitation is determined by MB size, gas content, viscoelastic shell properties and geometrical constraints. The viscoelastic parameters of the MB capsule vary during an oscillation cycle and change irreversibly upon shell rupture. The latter results in marked stress changes on the endothelium of capillary blood vessels due to altered MB dynamics. Mechanical effects on microvessels are crucial for safety and efficacy in applications such as focused ultrasound-mediated blood-brain barrier (BBB) opening. Since direct in vivo quantification of vascular stresses is currently not achievable, computational modelling has established itself as an alternative. We have developed a novel computational framework combining fluid-structure coupling and interface tracking to model the nonlinear dynamics of an encapsulated MB in constrained environments. This framework is used to investigate the mechanical stresses at the endothelium resulting from MB shell rupture in three microvessel setups of increasing levels of geometric detail. All configurations predict substantial elevation of up to 150 % for peak wall shear stress upon MB breakup, whereas global peak transmural pressure levels remain unaltered. The presence of red blood cells causes confinement of pressure and shear gradients to the proximity of the MB, and the introduction of endothelial texture creates local modulations of shear stress levels. With regard to safety assessments, the mechanical impact of MB breakup is shown to be more important than taking into account individual red blood cells and endothelial texture. The latter two may prove to be relevant to the actual, complex process of BBB opening induced by MB oscillations

Age-Specific Characteristics and Coupling of Cerebral Arterial Inflow and Cerebrospinal Fluid Dynamics

The objective of this work is to quantify age-related differences in the characteristics and coupling of cerebral arterial inflow and cerebrospinal fluid (CSF) dynamics. To this end, 3T phase-contrast magnetic resonance imaging blood and CSF flow data of eleven young ( years) and eleven elderly subjects ( years) with a comparable sex-ratio were acquired. Flow waveforms and their frequency composition, transfer functions from blood to CSF flows and cross-correlations were analyzed. The magnitudes of the frequency components of CSF flow in the aqueduct differ significantly between the two age groups, as do the frequency components of the cervical spinal CSF and the arterial flows. The males' aqueductal CSF stroke volumes and average flow rates are significantly higher than those of the females. Transfer functions and cross-correlations between arterial blood and CSF flow reveal significant age-dependence of phase-shift between these, as do the waveforms of arterial blood, as well as cervical-spinal and aqueductal CSF flows. These findings accentuate the need for age- and sex-matched control groups for the evaluation of cerebral pathologies such as hydrocephalus

Terabyte-scale supervised 3D training and benchmarking dataset of the mouse kidney

The performance of machine learning algorithms, when used for segmenting 3D biomedical images, does not reach the level expected based on results achieved with 2D photos. This may be explained by the comparative lack of high-volume, high-quality training datasets, which require state-of-the-art imaging facilities, domain experts for annotation and large computational and personal resources. The HR-Kidney dataset presented in this work bridges this gap by providing 1.7 TB of artefact-corrected synchrotron radiation-based X-ray phase-contrast microtomography images of whole mouse kidneys and validated segmentations of 33 729 glomeruli, which corresponds to a one to two orders of magnitude increase over currently available biomedical datasets. The image sets also contain the underlying raw data, threshold- and morphology-based semi-automatic segmentations of renal vasculature and uriniferous tubules, as well as true 3D manual annotations. We therewith provide a broad basis for the scientific community to build upon and expand in the fields of image processing, data augmentation and machine learning, in particular unsupervised and semi-supervised learning investigations, as well as transfer learning and generative adversarial networks

Frequently asked questions in hypoxia research

“What is the O2 concentration in a normoxic cell culture incubator?” This and other frequently asked questions in hypoxia research will be answered in this review. Our intention is to give a simple introduction to the physics of gases that would be helpful for newcomers to the field of hypoxia research. We will provide background knowledge about questions often asked, but without straightforward answers. What is O2 concentration, and what is O2 partial pressure? What is normoxia, and what is hypoxia? How much O2 is experienced by a cell residing in a culture dish in vitro vs in a tissue in vivo? By the way, the O2 concentration in a normoxic incubator is 18.6%, rather than 20.9% or 20%, as commonly stated in research publications. And this is strictly only valid for incubators at sea level

Terabyte-scale supervised 3D training and benchmarking dataset of the mouse kidney

The performance of machine learning algorithms, when used for segmenting 3D biomedical images, does not reach the level expected based on results achieved with 2D photos. This may be explained by the comparative lack of high-volume, high-quality training datasets, which require state-of-the-art imaging facilities, domain experts for annotation and large computational and personal resources. The HR-Kidney dataset presented in this work bridges this gap by providing 1.7 TB of artefact-corrected synchrotron radiation-based X-ray phase-contrast microtomography images of whole mouse kidneys and validated segmentations of 33 729 glomeruli, which corresponds to a one to two orders of magnitude increase over currently available biomedical datasets. The image sets also contain the underlying raw data, threshold- and morphology-based semi-automatic segmentations of renal vasculature and uriniferous tubules, as well as true 3D manual annotations. We therewith provide a broad basis for the scientific community to build upon and expand in the fields of image processing, data augmentation and machine learning, in particular unsupervised and semi-supervised learning investigations, as well as transfer learning and generative adversarial networks