Background:This is a prospective study of three children presented to us in the Orbital clinic in AL ShahidGazi Al Hariri Hospital with painless proptosiswith suspension of Hydatid disease.Objectives: : Orbital hydatid disease is a rare lesion accounting for less than 1% of the total lesions of the body (1, 2). Orbital cysts presented as a primary lesion in our study which is rare to have such lesion without involvement of other organs (3). Humans represent the intermediate host where the commonly affected organ are liver and the lung (10-15%) (4). Methods:This is a prospective study of three Children presented to us in the Orbital clinic in Al Shahid Ghazi Alhariri Hospital with painless proptosis with suspension of Hydatid disease, depending on the cultural background and baseline investigation done elsewhere, during the period from Jan. 2012 to Jan. 2013.Results:Three children presented to us with painless proptosis one of them involving the right eyewhile the other two involve the left eye. One of patients male aged only three years while the other two were females aged nine and thirteen years of age. After radiological investigations two of the Children found to have a cystic extraconal lesion in superolateral angle of the orbit while the other one his lesions found in the superomedial angle of the orbit. The first two surgically approached by lateral orbitotomy while the other one by medial orbitotomy trying to avoid rupture of the cysts. After histopathological investigation of the lesions the diagnosis was confirmedasHydatidcyst.conclusions:Hydatid cyst of the Orbit is uncommon disease account for less than 1% of the total orbital lesions of the body.Haydatid disease of the orbit more common on the left side.The most common sites involved in the Orbit are the superolateral and superomedialangle.Haydatid disease of the orbit can present as a primary lesion without evidence of involvement of other part of the body.Haydatid disease of the orbit can present below 7 years of age.Haydatid cyst of the orbit can be removed intact with meticulous dissection
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Previous studies on the synthesis and characterization of metal chelates with uracil by elemental analysis, conductivity, IR, UV-Vis, NMR spectroscopy, and thermal analysis were covered in this review article. Reviewing these studies, we found that uracil can be coordinated through the electron pair on the N1, N3, O2, or O4 atoms. If the uracil was a mono-dentate ligand, it will be coordinated by one of the following atoms: N1, N3 or O2. But if the uracil was bi-dentate ligand, it will be coordinated by atoms N1 and O2, N3 and O2 or N3 and O4. However, when uracil forms complexes in the form of polymers, coordination occurs through the following atoms: N1 and N3 or N1 and O4.
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A compact microstrip six-port reflectometer (SPR) with extended bandwidth is proposed in this paper. The design is based on using 16-dB multi-section coupled line directional couplers and a multi-section 3-dB Wilkinson power divider operating from 1 to 6 GHz. The proposed SPR employs only two calibration standards: a matched load and an open load. As compared to other dielectric substrates, fabricating the proposed SPR involves using a low-cost (FR4) substrate. A novel algorithm is also proposed to estimate the complex reflection coefficient over the frequency ranges at which the standard performance of the circuit components is not fully satisfied. The new algorithm is based on the circles’ intersection points, which have been de
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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
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