Electrodiffusion Phenomena in Neuroscience and the Nernst–Planck–Poisson Equations

This work is aimed to give an electrochemical insight into the ionic transport phenomena in the cellular environment of organized brain tissue. The Nernst–Planck–Poisson (NPP) model is presented, and its applications in the description of electrodiffusion phenomena relevant in nanoscale neurophysiology are reviewed. These phenomena include: the signal propagation in neurons, the liquid junction potential in extracellular space, electrochemical transport in ion channels, the electrical potential distortions invisible to patch-clamp technique, and calcium transport through mitochondrial membrane. The limitations, as well as the extensions of the NPP model that allow us to overcome these limitations, are also discussed. View Full-TextKeywords: electrodiffusion; Nernst–Planck–Poisson; neuroscience; neurons; liquid junction potential; ionic channels; patch-clamp</p

Computer simulations of electrodiffusion problems based on Nernst-Planck and Poisson equations

A numerical procedure based on the method of lines for time-dependent electrodiffusion transport has been developed. Two types of boundary conditions (Neumann and Dirichlet) are considered. Finite difference space discretization with suitably selected weights based on a non-uniform grid is applied. Consistency of this method and the method put forward by Brumleve and Buck are analysed and compared. The resulting stiff system of ordinary differential equations is effectively solved using the RADAU5, RODAS and SEULEX integrators. The applications to selected electrochemical systems: liquid junction, bi-ionic case, ion selective electrodes and electrochemical impedance spectroscopy have been demonstrated. In the paper we promote the use of the full form of the Nernst-Planck and Poisson (NPP) equations, that is including explicitly the electric field as an unknown variable with no simplifications like electroneutrality or constant field assumptions. An effective method of the numerical solution of the NPP problem for arbitrary number of ionic species and valence numbers either for a steady state or a transient state is shown. The presented formulae - numerical solutions to the NPP problem - are ready to be implemented by anyone. Moreover, we make the resulting software freely available to anybody interested in using it. (C) 2012 Elsevier B.V. All rights reserved

Modeling Non Equilibrium Potentiometry to Understand and Control Selectivity and Detection Limit

The majority of present theoretical interpretations of ion-sensor response focus on phase boundary potentials. They assume electroneutrality and equilibrium or steady-state, thus ignoring electrochemical migration and time-dependent effects, respectively. These theoretical approaches, owing to their idealizations, make theorizing on ion distributions and electrical potentials in space and time domains impossible. Moreover, they are in conflict with recent experimental reports on ion-sensors, in which both kinetic (time-dependent) discrimination of ions to improve selectivity, and non-equilibrium transmembrane ion-transport for lowering detection limits, are deliberately used.For the above reasons, the Nernst-Planck-Poisson (NPP) equations are employed here to model the non-equilibrium response in a mathematically congruent manner. In the NPP model, electroneutrality and steady-state/equilibrium assumptions are abandoned. Consequently, directly predicting and visualizing the selectivity and the low detection limit variability over time, as well as the influence of other parameters, i.e. ion diffusibility, membrane thickness and permittivity, and primary to interfering ion concentration ratios on ion-sensor responses, are possible. Additionally, the NPP allows for solving the inverse problem i.e. searching for optimal sensor properties and measurement conditions via target functions and hierarchical modeling. The conditions under which experimentally measured selectivity coefficients are true (unbiased) and detection limits are optimized are demonstrated, and practical conclusions relevant to clinical measurements and bioassays are derived

Breakthrough in Modeling of Electrodiffusion Processes; Continuation and Extensions of the Classical Work of Richard Buck

In 1978 Brumleve and Buck published an important paper [1] pertaining to numerical modeling of electrodiffusion. At the time their approach was not immediately recognized and followed. However, it has changed since the beginning of 21st century. The approach of Brumleve and Buck based on Nernst-Planck-Poisson (NPP) equations is utilized to model transient behavior of various electrochemical processes. Multi-layers and reactions allow extending applications to selectivity and low detection limit with time variability, influence of parameters (ion diffusivities, membrane thickness, permittivity, rate constants), and ion interference on ion-sensor responses. Solution of NPP inverse problem allows for optimizing sensor properties and measurement environment. Conditions under which experimentally measured selectivity coefficients are true (unbiased) and detection limit is optimized are demonstrated. Impedance spectra obtained directly from NPPs are presented. Modeling durability and diagnosis of reinforced concrete is presented. Chlorides transport in concrete is modeled using NPPs and compared to other solutions

Phase 1 study of selinexor plus carfilzomib and dexamethasone for the treatment of relapsed/refractory multiple myeloma

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/150566/1/bjh15969.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150566/2/bjh15969_am.pd

Utility of perfusion PET measures to assess neuronal injury in Alzheimer's disease

Introduction: 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is commonly used to estimate neuronal injury in Alzheimer's disease (AD). Here, we evaluate the utility of dynamic PET measures of perfusion using 11C-Pittsburgh compound B (PiB) to estimate neuronal injury in comparison to FDG PET. Methods: FDG, early frames of PiB images, and relative PiB delivery rate constants (PiB-R1) were obtained from 110 participants from the Dominantly Inherited Alzheimer Network. Voxelwise, regional cross-sectional, and longitudinal analyses were done to evaluate the correlation between images and estimate the relationship of the imaging biomarkers with estimated time to disease progression based on family history. Results: Metabolism and perfusion images were spatially correlated. Regional PiB-R1 values and FDG, but not early frames of PiB images, significantly decreased in the mutation carriers with estimated year to onset and with increasing dementia severity. Discussion: Hypometabolism estimated by PiB-R1 may provide a measure of brain perfusion without increasing radiation exposure

Modelowanie czujników potencjometrycznych. Cz. 1

Potencjometryczne czujniki jonowe, tj. elektrody jonoselektywne oraz sensory jonoselektywne są bardzo ważną podgrupą czujników elektrochemicznych. Są bardzo atrakcyjne dla zastosowań praktycznych ze względu na ich cechy, takie jak niewielki rozmiar, przenośność, niskie zużycie energii, oraz stosunkowo niskie koszta. Żywy rozwój tej dziedziny stwarza konieczność szczegółowego opisu odpowiedzi czujników, w tym jej zależności od różnych parametrów. Celem niniejszej pracy jest przedstawienie istniejących modeli dostępnych do opisu czujników potencjometrycznych. Pierwsza część artykułu stanowi wstęp do modelowania oraz opis najbardziej wyidealizowanego Modelu Granicy Faz. Druga część stanowi szczegółowy opis bardziej ogólnych modeli tj. Modelu Warstwy Dyfuzyjnej oraz modelu Nernsta-Plancka-Poissona

Modelowanie czujników potencjometrycznych. Cz. 2

Potencjometryczne czujniki jonowe, tj. elektrody jonoselektywne oraz sensory jonoselektywne są bardzo ważną podgrupą czujników elektrochemicznych. Są one bardzo atrakcyjne dla zastosowań praktycznych ze względu na ich cechy, takie jak niewielki rozmiar, mobilność, niskie zużycie energii, oraz stosunkowo niskie koszty. Dynamiczny rozwój tej dziedziny stwarza konieczność szczegółowego opisu odpowiedzi czujników, w tym jej zależności od różnych parametrów. Celem niniejszej pracy jest przedstawienie istniejących modeli dostępnych do opisu czujników potencjometrycznych. Pierwsza część artykułu stanowi wstęp do modelowania oraz opis najbardziej wyidealizowanego Modelu Granicy Faz. Druga część stanowi szczegółowy opis bardziej ogólnych modeli, tj. Modelu Warstwy Dyfuzyjnej oraz modelu Nernsta-Plancka-Poissona