Amyloid-beta detection with saccharide immobilized gold nanoparticle on carbon electrode

The electrochemical sensing of saccharide-protein interactions using a couple of sialic acid derivatives and Alzheimer's amyloid-beta (Abeta) is described. The densely-packed saccharide area for recognition of protein was fabricated onto a carbon electrode by three steps, which were electrochemical deposition of Au nanoparticles on a screen printed strip, self-assembled monolayer (SAM) formation of the acetylenyl group on Au nanoparticles, and the cycloaddition reaction of an azide-terminated sialic acid to the acetylenyl group. The attachment of Abeta peptides to the sialic acid layer was confirmed by electrochemistry and atomic force microscopy imaging. The intrinsic oxidation signal of the captured Abeta(1-40) and (1-42) peptides, containing a single tyrosine (Tyr) residues, was monitored at a peak potential of 0.6 V (vs Ag/AgCl within this sensor) in connection with differential pulse voltammetry. The peak current intensities were concentration dependent. The proposed process provides new routes for analysis of saccharide-protein interactions and electrochemical biosensor development.     

All-d-Enantiomer of β-Amyloid Peptide Forms Ion Channels in Lipid Bilayers

Alzheimer's disease (AD) is the most common type of senile dementia in aging populations. Amyloid β (Aβ)-mediated dysregulation of ionic homeostasis is the prevailing underlying mechanism leading to synaptic degeneration and neuronal death. Aβ-dependent ionic dysregulation most likely occurs either directly via unregulated ionic transport through the membrane or indirectly via Aβ binding to cell membrane receptors and subsequent opening of existing ion channels or transporters. Receptor binding is expected to involve a high degree of stereospecificity. Here, we investigated whether an Aβ peptide enantiomer, whose entire sequence consists of d-amino acids, can form ion-conducting channels; these channels can directly mediate Aβ effects even in the absence of receptor-peptide interactions. Using complementary approaches of planar lipid bilayer (PLB) electrophysiological recordings and molecular dynamics (MD) simulations, we show that the d-Aβ isomer exhibits ion conductance behavior in the bilayer indistinguishable from that described earlier for the l-Aβ isomer. The d isomer forms channel-like pores with heterogeneous ionic conductance similar to the l-Aβ isomer channels, and the d-isomer channel conductance is blocked by Zn(2+), a known blocker of l-Aβ isomer channels. MD simulations further verify formation of β-barrel-like Aβ channels with d- and l-isomers, illustrating that both d- and l-Aβ barrels can conduct cations. The calculated values of the single-channel conductance are approximately in the range of the experimental values. These findings are in agreement with amyloids forming Ca(2+) leaking, unregulated channels in AD, and suggest that Aβ toxicity is mediated through a receptor-independent, nonstereoselective mechanism.     

Voltammetric determination of isoproterenol using a 5-amino-2’,4’-dimethoxybiphenyl-2-ol modified carbon nanotube paste electrode

A new electrochemical sensor for determination of isoproterenol(IP) is described.The sensor is based on carbon paste electrode (CPE) modified with 5-amino-2’,4’-dimethoxybiphenyl-2-ol(5ADMB) and takes the advantages of carbon nanotubes(CNTs). Under the optimum pH of 7.0,the oxidation of IP occurs at a potential about 210 mV less positive than that of the unmodified CPE. The oxidation currents increased linearly with two concentration intervals of IP,one is 0.09 to 20.0 |xmol/L and,the other is 20.0 to 400.0μmol/L.The detection limit(3σ) obtained by square wave voltammetry(SWV) was 39.0 nmol/L.The practical application of the modified electrode was demonstrated by determining IP in IP ampoule,urine and human blood serum samples.     

Über die Transformation der Polyeder

Ohne Zusammenfassung     

Detection of Static and Mobile Targets by an Autonomous Agent with Deep Q-Learning Abilities

This paper addresses the problem of detecting multiple static and mobile targets by an autonomous mobile agent acting under uncertainty. It is assumed that the agent is able to detect targets at different distances and that the detection includes errors of the first and second types. The goal of the agent is to plan and follow a trajectory that results in the detection of the targets in a minimal time. The suggested solution implements the approach of deep Q-learning applied to maximize the cumulative information gain regarding the targets’ locations and minimize the trajectory length on the map with a predefined detection probability. The Q-learning process is based on a neural network that receives the agent location and current probability map and results in the preferred move of the agent. The presented procedure is compared with the previously developed techniques of sequential decision making, and it is demonstrated that the suggested novel algorithm strongly outperforms the existing methods.