Introduction: COVID-19 vaccine have been indicated to successfully decrease the hazard for symptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection furthermore associated hospitalisations. Objective: To study the immune response among different types of SARS-CoV-2 vaccines. Methods: This study includes 100 vaccinated individuals (43 Sinopharm, 30 AstraZeneca and 27 Pfizer) with one or two doses from different health centres in Baghdad. During the period from April 2021 to the end of May 2021, SARS-CoV-2 IgG and SARS-CoV-2 IgM levels were detected using AFIAS-6 device depending on FIA (Fluorescence Immunoassay) technique. Results: 93% of the cases were positive for IgG levels, and negative in 7% case

The Jeribe Formation, the Jambour oil field, is the major carbonate reservoir from the tertiary reservoirs of the Jambour field in northern Iraq, including faults. Engineers have difficulty organizing carbonate reserves since they are commonly tight and heterogeneous. This research presents a geological model of the Jeribe reservoir based on its facies and reservoir characterization data (Permeability, Porosity, Water Saturation, and Net to Gross). This research studied four wells. The geological model was constructed with the Petrel 2020.3 software. The structural maps were developed using a structural contour map of the top of the Jeribe Formation. A pillar grid model with horizons and layering was designed for each zone. Followin

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with

In recent years, there has been a rise in interest in the study of antibiotic occurrence in the aquatic environment due to the negative consequences of prolonged exposure and the potential for bacterial antibiotic resistance. Most antibiotic residues from treated wastewater end up in the aquatic environment as they are not eliminated in facilities that treat wastewater. Antibiotics must be identified in influent and effluent wastewater using reliable analytical techniques for several reasons. Firstly, monitoring antibiotic presence in aquatic environments. Secondly, assessing environmental risks, computing wastewater treatment plant removal efficiencies, and estimating antibiotic consumption. Therefore, this work aims to provide an overview

Flexible joint robot (FJR) manipulators can offer many attractive features over rigid manipulators, including light weight, safe operation, and high power efficiency. However, the tracking control of the FJR is challenging due to its inherent problems, such as underactuation, coupling, nonlinearities, uncertainties, and unknown external disturbances. In this article, a terminal sliding mode control (TSMC) is proposed for the FJR system to guarantee the finite-time convergence of the systems output, and to achieve the total robustness against the lumped disturbance and estimation error. By using two coordinate transformations, the FJR dynamics is turned into a canonical form. A cascaded finite-time sliding mode observer (CFTSMO) is construct

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

Collagen triple helix repeat containing-1 (CTHRC1) is an essential marker for Rheumatoid Arthritis (RA), but its relationship with pro-inflammatory, anti-inflammatory, and inflammatory markers has been scantily covered in extant literature. To evaluate the level of CTHRC1 protein in the sera of 100 RA patients and 25 control and compare levels of tumour necrosis factor alpha (TNF-α), interleukin 10 (IL-10), RA disease activity (DAS28), and inflammatory factors. Higher significant serum levels of CTHRC1 (29.367 ng/ml), TNF-α (63.488 pg/ml), and IL-10 (67.1 pg/ml) were found in patient sera as compared to that in control sera (CTHRC1 = 15.732 ng/ml, TNF-α = 33.788 pg/ml, and IL-10 = 25.122 pg/ml). There was no significant correlati