Fifty celiac disease (CD) patients (21 males and 29 females) with an age range of 2-35 years and 25 apparently healthy controls were investigated for 10 autoantibodies (anti-tissue transglutaminase IgA antibody; ATA, anti-tissue transglutaminase IgG antibody; ATG, anti-gliadine IgA antibody; AGA, anti-gliadine IgG antibody; AGG, anti-nuclear antibody; ANA, anti-double strand DNA antibody; AdsDNA, anti-thyroid peroxidase antibody; ATP, anti-phospholipid antibody; APP, anti-myeloperoxidase antibody; AMP and anti-proteinase 3 antibody; AP3) in their sera. Six autoantibodies (ATA, ATG, AGA, AGG, AMP and AP3) showed significant variations between CD patients and controls. The first four antibodies were not detected in sera of controls, while

Wireless Body Area Sensor Networks (WBASNs) have garnered significant attention due to the implementation of self-automaton and modern technologies. Within the healthcare WBASN, certain sensed data hold greater significance than others in light of their critical aspect. Such vital data must be given within a specified time frame. Data loss and delay could not be tolerated in such types of systems. Intelligent algorithms are distinguished by their superior ability to interact with various data systems. Machine learning methods can analyze the gathered data and uncover previously unknown patterns and information. These approaches can also diagnose and notify critical conditions in patients under monitoring. This study implements two s

       Uromodulin is the most abundant protein ordinary excreted in urine which could be used as a biomarker to diagnose kidney diseases.  However, evidence suggests that it regulates salt transport, protects against urinary tract infection and kidney stones, and has a role in kidney damage and innate immunity. This study aimed to understand the association of uromodulin gene rs13332878 with  chronic kidney disease. More than 100 people were selected for the study and the samples collected from the under study subjects were divided into two groups. 70 chosen subjects were under the dialysis with kidney failure, and  aged between 18-88 years. The second group included 30 samples from healthy individuals, used as control. One of t

Heart failure (HF) is characterized by family history and clinical examination combined with diagnostic tools such as electrocardiogram, chest x-ray and an assessment of left ventricular function by echocardiography. An early diagnosis of heart failure is still based on symptoms of dyspnea, fatigue and signs of fluid overload. Serum N-terminal pro-B-type natriuretic peptide (NT-pro BNP) is cardiac biomarker has emerged as potential predictor of heart failure. It is used as a sensitive biomarker in diagnosis and assessment severity of heart failure. This study assed the diagnostic value of (NT-pro BNP), in Iraqi children patients with heart failure and its correlation with LVEF% especially in emergency rooms of hospitals.Ninety (90) consecut

Nearly, in the middle of 1970s the split-brain theory became the only theory that explains human creativity used in all fine art and art education schools. In fact, this theory- which appeared for first time in the middle of 1940s – faced many radical changes including its concepts and structures, and these changes affected both teaching art and art criticism. To update people awareness within art field of study, this paper reviews the split-brain theory and its relationship with teaching art from its appearance to its decay in 2013 and after.

The object of the presented study was to monitor the changes that had happened
in the main features (water, vegetation, and soil) of Al-Hammar Marsh region. To
fulfill this goal, different satellite images had been used in different times, MSS
1973, TM 1990, ETM+ 2000 and MODIS 2010. K-Means which is unsupervised
classification and Neural Net which is supervised classification was used to classify
the satellite images 0Tand finally by use 0Tadaptive classification 0Twhich is0T3T 0T3Tapply
s0Tupervised classification on the unsupervised classification. ENVI soft where used
in this study.

In this paper, we build a fuzzy classification system for classifying the nutritional status of children under 5 years old in Iraq using the Mamdani method based on input variables such as weight and height to determine the nutritional status of the child. Also, Classifying the nutritional status faces a difficult challenge in the medical field due to uncertainty and ambiguity in the variables and attributes that determine the categories of nutritional status for children, which are relied upon in medical diagnosis to determine the types of malnutrition problems and identify the categories or groups suffering from malnutrition to determine the risks faced by each group or category of children. Malnutrition in children is one of the most

Astronomy image is regarded main source of information to discover outer space, therefore to know the basic contain for galaxy (Milky way), it was classified using Variable Precision Rough Sets technique to determine the different region within galaxy according different color in the image. From classified image we can determined the percentage for each class and then what is the percentage mean. In this technique a good classified image result and faster time required to done the classification process.

One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services th