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 were grouped into 32 participants with PUD (case group) and 18 healthy participants (control group). A full-mouth examination was performed for every patient; saliva and gastric samples from both groups were obtained. Helicobacter pylori were detected in gastric biopsies by histological examination by using H & E stain, and Polymerase Chain Reaction (PCR) was carried out on the oral samples. Results: Helicobacter pylori DNAwas determined by PCR in oral samples in 88% patients and in gastric biopsies by histology in 86% patients, and in both samples in 84% patients.It was highly significant to find simultaneous presence for those have H. pylori in stomach also have such microorganism in the mouth P < 0.05 and there was an excellent correlation between detecting H. pylori simultaneously in both stomach and mouth. If we screen for stomach H. pylori through detecting this microorganism in the mouth; saliva samples is highly sensitive (98%) but not very specific. Conclusion: Helicobacter pylori saliva test has high sensitivity, specificity, and accuracy for the diagnosis of H. pylori infection in Iraqi population. The test can be clinically applied as a routine diagnostic tool for H. pylori infection this could permit not only a target for therapeutic procedures but also a monitoring tool for the efficacy of therapy. It seems to overcome some limitations of the conventional invasive techniques.
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Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to
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Electronic Health Record (EHR) systems are used as an efficient and effective method of exchanging patients’ health information with doctors and other key stakeholders in the health sector to obtain improved patient treatment decisions and diagnoses. As a result, questions regarding the security of sensitive user data are highlighted. To encourage people to move their sensitive health records to cloud networks, a secure authentication and access control mechanism that protects users’ data should be established. Furthermore, authentication and access control schemes are essential in the protection of health data, as numerous responsibilities exist to ensure security and privacy in a network. So, the main goal of our s
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This study investigates the feasibility of a mobile robot navigating and discovering its location in unknown environments, followed by the creation of maps of these navigated environments for future use. First, a real mobile robot named TurtleBot3 Burger was used to achieve the simultaneous localization and mapping (SLAM) technique for a complex environment with 12 obstacles of different sizes based on the Rviz library, which is built on the robot operating system (ROS) booted in Linux. It is possible to control the robot and perform this process remotely by using an Amazon Elastic Compute Cloud (Amazon EC2) instance service. Then, the map to the Amazon Simple Storage Service (Amazon S3) cloud was uploaded. This provides a database
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Recent population studies have shown that placenta accreta spectrum (PAS) disorders remain undiagnosed before delivery in half to two-thirds of cases. In a series from specialist diagnostic units in the USA, around one-third of cases of PAS disorders were not diagnosed during pregnancy. Maternal
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