ABSTRACT Background: resin cement type and intraoral temperature fluctuations may affect the fracture performance of successful zirconia restorations. To fill this gap, the purpose of this study is to evaluate and compare the influence of thermocycling on fracture resistance and mode of failure of monolithic zirconia crowns luted with Rely X™ U200 and BreezeTMself-adhesive resin cements as well as imply the effect of adding 2 % of polylysine (PLS) to these cements. Materials: 64 maxillary premolars were milled out of zirconia blocks using CAD/CAM milling system. They were divided into four groups (n = 16) according to the cement type. Four different resin cements were used (RelyXTMU200, Breeze™, RelyX™ U200 with 2 % PLS

ABSTRACT

Background : The aim of this work is to assess the role of breast sonography  and ductography in the evaluation of different causes of nipple discharge.

Methods :  The study will be carried out on twenty-five female patients referred to the Radiodiagnosis department at Alexandria Main University Hospital presenting with nipple discharge.

They were divided into two groups:

Group I include 10 patients (40%) with surgically significant nipple discharge who were the patients with unilateral, uniorificial surgically significant colour type nipple discharge .They were investigated by mammography, sonography, and ductography.

Group II include 15 patients

The Fylex extract exert a high inhibition effect against A . flavus growth on PDA medium, as the fungus growth was completely inhibited by 100% at a concentration of 0.2 and 0.3% of studied extract, while the lowest inhibition percentage (71%) was found at a concentration of 0.1%. Whereas magnesium oxide nanoparticles showed the highest inhibition ratio of A. flavus (100%) was detected at 0.2% and the lowest inhibition ratio (81.66%) was at concentration 0.5%. Moreover, the addition of G. lucidum powder to PDA medium with a concentration of 2.5 mg increased the inhibition rate of A. flavus growth which was 54.4%, while the lowest inhibition ration (18.22%) was found at a concentration of 1000 mg. The milky liquid (brocade milk) of Calotropi

Back ground: Oral lichen planus is a chronic inflammatory disease that affects the mucous membrane of the mouth. Several researchers suggest that oxidative stress is implicated in the pathogenesis of this disorder. It has been hypothesized that melatonin is powerful anti-oxidants and can easily cross the cell membrane and is considered a free radical scavenger of Hydroxid, Oxygen and Nitrogen dioxide, cortisol as a stress hormone and the immunoglobulin A as first line of defense and protection to the mucous membrane of the mouth are interrelated factors for the emergence of oral lichen planus. Aim of this study was to evaluate the level of melatonin, Cortisol and IgA in saliva of patients with oral lichen planus lesions in comparison with p

Aerial manipulation of objects has a number of advantages as it is not limited by the morphology of the terrain. One of the main problems of the aerial payload process is the lack of real-time prediction of the interaction between the gripper of the aerial robot and the payload. This paper introduces a digital twin (DT) approach based on impedance control of the aerial payload transmission process. The impedance control technique is implemented to develop the target impedance based on emerging the mass of the payload and the model of the gripper fingers. Tracking the position of the interactional point between the fingers of gripper and payload, inside the impedance control, is achieved using model predictive control (MPD) approach.

       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial