Cairo University

About: Cairo University is a education organization based out in Giza, Egypt. It is known for research contribution in the topics: Population & Medicine. The organization has 33532 authors who have published 55581 publications receiving 792654 citations. The organization is also known as: Fuad I University & King Fuad I University.

Addition of dexmedetomidine to bupivacaine for greater palatine nerve block prolongs postoperative analgesia after cleft palate repair.

Abstract: Background and objectiveThe effect of dexmedetomidine on the duration of sensory blockade has not been studied in humans. We evaluated the effect of adding dexmedetomidine to bupivacaine on the duration of postoperative analgesia in children who underwent repair of a cleft palate.MethodsThirty child

Atherosclerosis in Ancient Egyptian Mummies: The Horus Study

Abstract: Objectives The purpose of this study was to determine whether ancient Egyptians had atherosclerosis. Background The worldwide burden of atherosclerotic disease continues to rise and parallels the spread of diet, lifestyles, and environmental risk factors associated with the developed world. It is tempting to conclude that atherosclerotic cardiovascular disease is exclusively a disease of modern society and did not affect our ancient ancestors. Methods We performed whole body, multislice computed tomography scanning on 52 ancient Egyptian mummies from the Middle Kingdom to the Greco-Roman period to identify cardiovascular structures and arterial calcifications. We interpreted images by consensus reading of 7 imaging physicians, and collected demographic data from historical and museum records. We estimated age at the time of death from the computed tomography skeletal evaluation. Results Forty-four of 52 mummies had identifiable cardiovascular (CV) structures, and 20 of these had either definite atherosclerosis (defined as calcification within the wall of an identifiable artery, n = 12) or probable atherosclerosis (defined as calcifications along the expected course of an artery, n = 8). Calcifications were found in the aorta as well as the coronary, carotid, iliac, femoral, and peripheral leg arteries. The 20 mummies with definite or probable atherosclerosis were older at time of death (mean age 45.1 ± 9.2 years) than the mummies with CV tissue but no atherosclerosis (mean age 34.5 ± 11.8 years, p 2,000 years. Conclusions Atherosclerosis is commonplace in mummified ancient Egyptians.

An optimized model based on convolutional neural networks and orthogonal learning particle swarm optimization algorithm for plant diseases diagnosis

Abstract: The plant disease classification based on using digital images is very challenging. In the last decade, machine learning techniques and plant images classification tools such as deep learning can be used for recognizing, detecting and diagnosing plant diseases. Currently, deep learning technology has been used for plant disease detection and classification. In this paper, an ensemble model of two pre-trained convolutional neural networks (CNNs) namely VGG16 and VGG19 have been developed for the task plant disease diagnosis by classifying the leaves images of healthy and unhealthy. In this context, CNNs are used due to its capability of overcoming the technical problems which are associated with the classification problem of plant diseases. However, CNNs suffer from a great variety of hyperparameters with specific architectures which is considered as a challenge to identify manually the optimal hyperparameters. Therefore, orthogonal learning particle swarm optimization (OLPSO) algorithm is utilized in this paper to optimize a number of these hyperparameters by finding optimal values for these hyperparameters rather than using traditional methods such as the manual trial and error method. In this paper, to prevent CNNs from falling into the local minimum and to train efficiently, an exponentially decaying learning rate (EDLR) schema is used. In this paper, the problem of the imbalanced used dataset has been solved by using random minority oversampling and random majority undersampling methods, and some restrictions in terms of both the number and diversity of samples have been overcome. The obtained results of this work show that the accuracy of the proposed model is very competitive. The experimental results are compared with the performance of other pre-trained CNN models namely InceptionV3 and Xception, whose hyperparameters were selected using a non-evolutionary method. The comparison results demonstrated that the proposed diagnostic approach has achieved higher performance than the other models.