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https://www.selleckchem.com/products/tvb-2640.html Posteroanterior and lateral cephalogram have been widely used for evaluating the necessity of orthognathic surgery. The purpose of this study was to develop a deep learning network to automatically predict the need for orthodontic surgery using cephalogram. The cephalograms of 840 patients (Class ll 244, Class lll 447, Facial asymmetry 149) complaining about dentofacial dysmorphosis and/or a malocclusion were included. Patients who did not require orthognathic surgery were classified as Group I (622 patients-Class ll 221, Class lll 312, Facial asymmetry 89). Group II (218 patients-Class ll 23, Class lll 135, Facial asymmetry 60) was set for cases requiring surgery. A dataset was extracted using random sampling and was composed of training, validation, and test sets. The ratio of the sets was 415. PyTorch was used as the framework for the experiment. Subsequently, 394 out of a total of 413 test data were properly classified. The accuracy, sensitivity, and specificity were 0.954, 0.844, and 0.993, respectively. It was found that a convolutional neural network can determine the need for orthognathic surgery with relative accuracy when using cephalogram. It was found that a convolutional neural network can determine the need for orthognathic surgery with relative accuracy when using cephalogram. Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide. Adoption of sedentary life style and westernized diet are shown to be associated with development of NAFLD. Since previous studies suggested that calcium (Ca) to magnesium (Mg) ratio intake is associated with some chronic diseases including dyslipidemia and insulin resistance, we designed this study to find any possible association between this ratio and NAFLD development. The NAFLD was diagnosed using Fibroscan according to a CAP cut-off value of 263 dB/m. Dietary intakes of one hundred and ninety-six patients with incident NAFLD diagn

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