In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesi

Background: Helicobacter pylori are important gastrointestinal pathogen associated with gastritis, peptic ulcers, and an increased risk of gastric carcinoma. There are several popular methods for detection of H. pylori (invasive and non-invasive methods) each having its own advantages, disadvantages, and limitations, and by using PCR technique the ability to detect H. pylori in saliva samples offers a potential for an alternative test for detection of this microorganism. Materials and methods: The study sample consists of fifty participants of both genders, who undergo Oesophageo-gastrodudenoscopy at the Gastroenterology Department of Al-Kindy Teaching Hospital Baghdad/ Iraq, during five months period from January 2014 to May 2014. They we

There is currently a pressing need to create an electro-analytical approach capable of detecting and monitoring genosensors in a highly sensitive, specific, and selective way. In this work, Functionalized Multiwall Carbon Nanotubes, Graphene, Polypyrrole, and gold nanoparticles nanocomposite (f-MWCNTs-GR-PPy-AuNP) were effectively deposited on the surface of the ITO electrode using a drop-casting process to modify it. The structural, morphological, and optical analysis of the modified ITO electrodes was carried out at room temperature using X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM) images, atomic force microscopy (AFM) and Fourier transform infrared (FTIR) spectra. Cyclic voltammetry (CV) and electrochemi

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