Abstract

β-thalassemia major is a genetic disease that causes sever defect in normal hemoglobin synthesis. The patients with β-thalassemia major need periodic blood transfusions that can result in accumulation of body iron, so treatment with iron chelating agent is required. Complications of this iron overload affecting many vital organs, including the liver. The aim of this work was to evaluate liver enzymes in β -thalassemia major patients with deferasirox versus without it. Two groups of β-thalassemia major patients were involved in this study named group A; 40 β-thalassemia patients of blood transfusion dependent without deferasirox, group B; 40 β-thalassemia patients of blood transfusion dependent on de

It is not often  easy to identify a certain group of words as a lexical bundle, since the same set of words can be, in different situations, recognized as idiom,  a collocation, a lexical phrase or a lexical bundle. That is, there are many cases where the overlap among the four types is plausible. Thus, it is important to extract the most identifiable and distinguishable characteristics with which a certain group of words, under certain conditions, can be recognized as a lexical bundle, and this is the task of this paper.

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Background: Ankylosing spondylitis (AS) is inflammation of the sacroiliac joints and spine, associated with clinical symptoms such as pain and stiffness in the vertebral column, after which, in a considerable number of individuals, new bone growth occurs. Objective: The current research study attempted to find out whether the presence of SNPs in TNF receptor [TNFRSF1A (rs767455), TNFRSF1B (rs1061622)] encoding genes could influence patients' outcomes to etanercept in a specimen of Iraqi AS patients. Patients and methods: Sixty patients with established AS receiving only etanercept were selected to be enrolled in this research with a mean age of 40.75 ± 8.67 years, 51 patients of them were males and only 9 patients were females. Patients we

While hepatitis viruses A–E are established, emerging evidence points to additional, novel viral hepatitis agents. The torqueteno virus (TTV) has garnered interest due to its prevalence among patients with hepatitis, suggesting potential hepatotropism.

Abstract:                                                                                                                Fluoroquinolones drugs are an important class of wide

Many studies of the relationship between COVID-19 and different factors have been conducted since the beginning of the corona pandemic. The relationship between COVID-19 and different biomarkers including ABO blood groups, D-dimer, Ferritin and CRP, was examined. Six hundred (600) patients, were included in this trial among them, 324 (56%) females and the rest 276 (46%) were males. The frequencies of blood types A, B, AB, and O were 25.33, 38.00, 31.33, and 5.33%, respectively, in the case group. Association analysis between the ABO blood group and D-dimer, Ferritin and CRP of COVID-19 patients indicated that there was a statistically significant difference for Ferritin (P≤0.01), but no-significant differences for both D-dimer and CRP.

Background: Coronavirus disease 2019 (COVID-19) is an emerging zoonotic disease caused by the new respiratory virus SARS-CoV2. It has a tropism in the lung tissues where excess target receptors exist. Periostin plays a role in subepithelial fibrosis associated with bronchial asthma. Since the Coronavirus's target is the human respiratory system, Periostin has been recently described as a valuable new biomarker in the diagnosis and evaluation of disease in patients with COVID-19 lung involvement. Objectives: To assess the level of Periostin in the serum of COVID-19 patients and to correlate its role in disease severity and prognosis. Subjects and Methods: Periostin serum levels were measured for 63 patients attending three main COVID

Support vector machines (SVMs) are supervised learning models that analyze data for classification or regression. For classification, SVM is widely used by selecting an optimal hyperplane that separates two classes. SVM has very good accuracy and extremally robust comparing with some other classification methods such as logistics linear regression, random forest, k-nearest neighbor and naïve model. However, working with large datasets can cause many problems such as time-consuming and inefficient results. In this paper, the SVM has been modified by using a stochastic Gradient descent process. The modified method, stochastic gradient descent SVM (SGD-SVM), checked by using two simulation datasets. Since the classification of different ca