Barrier dysfunction or drainage reduction: differentiating causes of CSF protein increase

BACKGROUND Cerebrospinal fluid (CSF) protein analysis is an important element in the diagnostic chain for various central nervous system (CNS) pathologies. Among multiple existing approaches to interpreting measured protein levels, the Reiber diagram is particularly robust with respect to physiologic inter-individual variability, as it uses multiple subject-specific anchoring values. Beyond reliable identification of abnormal protein levels, the Reiber diagram has the potential to elucidate their pathophysiologic origin. In particular, both reduction of CSF drainage from the cranio-spinal space as well as blood-CNS barrier dysfunction have been suggested ρas possible causes of increased concentration of blood-derived proteins. However, there is disagreement on which of the two is the true cause. METHODS We designed two computational models to investigate the mechanisms governing protein distribution in the spinal CSF. With a one-dimensional model, we evaluated the distribution of albumin and immunoglobulin G (IgG), accounting for protein transport rates across blood-CNS barriers, CSF dynamics (including both dispersion induced by CSF pulsations and advection by mean CSF flow) and CSF drainage. Dispersion coefficients were determined a priori by computing the axisymmetric three-dimensional CSF dynamics and solute transport in a representative segment of the spinal canal. RESULTS Our models reproduce the empirically determined hyperbolic relation between albumin and IgG quotients. They indicate that variation in CSF drainage would yield a linear rather than the expected hyperbolic profile. In contrast, modelled barrier dysfunction reproduces the experimentally observed relation. CONCLUSIONS High levels of albumin identified in the Reiber diagram are more likely to originate from a barrier dysfunction than from a reduction in CSF drainage. Our in silico experiments further support the hypothesis of decreasing spinal CSF drainage in rostro-caudal direction and emphasize the physiological importance of pulsation-driven dispersion for the transport of large molecules in the CSF

Multiphysics Modelling of Powder Coating of U-Profiles: Towards Simulation-based Optimization of Key-Performance Attributes by Variation of Powder-Parameters

Multiphysics simulation software has been developed to predict the key performance attributes of industrial powder coating applications based on applied process-parameter settings. The software is a Eulerian-Lagrangian finite-volume Multiphysics solver based on OpenFOAM, capable of modelling mass transfer effects between powder-coating pistols and electrically grounded metallic substrates. It considers various factors such as fluid dynamics of process airflow, coating-particle dynamics, particle-substrate interactions, and particle charging mechanisms within the corona. The software is fully compatible with Massive Simultaneous Cloud Computing technology, allowing hundreds of simulated coating scenarios to be computed simultaneously. Experimental validation efforts have been conducted, indicating a high degree of practical relevance of the technology. The current simulation study aims to demonstrate the potential of the simulation software for adjusting coating lines and optimizing powder coating of U-profiles. Specifically, the study focuses on optimizing the key-performance-attributes of the powder coating application with respect to varying material parameters of the applied powder, namely mean particle diameter, standard deviation of Gaussian particle size distribution, and powder particle density. The software predicts and visualizes coating patterns, coating efficiencies, and the batch-based standard deviation of coating thickness on a U-shaped metallic substrate, resulting in concrete and optimized powder settings. The presented results and the applied software are highly relevant for powder material suppliers