Background: Acute appendicitis is the most common abdominal surgical emergency. The diagnosis of this condition is still essentially clinical and there is difficulty in the clinical diagnosis, especially among elderly, children and patients with a typical presentation, so early and accurate diagnosis of acute appendicitis is important to avoid its complications.Objectives: To evaluate the degree of accuracy of Alvarado scoring system in the diagnosis of acute appendicitis.Method: Two hundred patients were admitted to the Alkindy Teaching Hospital from January 2011 to april 2014- presented with symptoms and signs suggestive of acute appendicitis. After examination and investigations all patients were given a score according to Alvarado sc

We report here an innovative feature of green nanotechnology-focused work showing that mangiferin—a glucose functionalized xanthonoid, found in abundance in mango peels—serves dual roles of chemical reduction and in situ encapsulation, to produce gold nanoparticles with optimum in vivo stability and tumor specific characteristics. The interaction of mangiferin with a Au-198 gold precursor affords MGF-198AuNPs as the beta emissions of Au-198 provide unique advantages for tumor therapy while gamma rays are used for the quantitative estimation of gold within the tumors and various organs. The laminin receptor specificity of mangiferin affords specific accumulation of therapeutic payloads of this new therapeutic agent within prostate tumors

Recurrent respiratory tract infections are responsible for about 85% of all diseases in childhood, and are associated with significant morbidity and mortality. The aim of this study is to evaluate the main causes underlying recurrent respiratory tract infections in 176 pediatric patients aged 2 month to 4 year and weight from 4 to11 kg referred to the child center hospital and Al-sader hospital prospective study.All parents were given information sheet which then analyzed and the percentage of incidence of causes were recorded, we found that higher % related to many causes; mostly related to the parent like poor family education, mother carelessness, incomplete vaccination, other related to empirical diagnosis, and short course of t

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-c

Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi