Scrophularia. striata from Scrophulariacea family has been used in Iranian folk medicine for the treatment of infectious diseases. In this study we evaluated the synergistic effect of S. striata   hydroalcoholic extract (SSE) and commercially available antibiotics against P. aeroginosa and Methicillin- resistant Staphylococcus aureus (MRSA). The resazurin-based microdilution method was used to determine the minimum inhibitory concentration (MIC) values of plan extract and standard antibiotics. The interaction between standard antibiotics and SSE was evaluated by using checkerboard method. The results of this study revealed that SSE enhance the antibacterial activity of antibiotics. The combin

Background: Extracorporeal Shock wave lithotripsy (ESWL) is widely used in treating patients with ureteralstones because it is effective, safe, and noninvasive. Based on factors such as size and the location of stones,there is a significant variation in the overall stone-free rate (SFR).Aim of the study: To evaluate the effect of ureteral wall thickness (UWT), stone attenuation, the time fromfirst attack of pain till first session of ESWL and stone/ rib density on the outcome of SWL in the treatmentof upper ureteral stones (UUS).Patient and methods: A prospective study when 127 patients with radio-opaque UUS ranging from 7 to 20mm and treated by ESWL were included in this study. The effect of (stone/ 12th rib) density by KUB, ureter

Various theories have been proposed since in last century to predict the first sighting of a new crescent moon. None of them uses the concept of machine and deep learning to process, interpret and simulate patterns hidden in databases. Many of these theories use interpolation and extrapolation techniques to identify sighting regions through such data. In this study, a pattern recognizer artificial neural network was trained to distinguish between visibility regions. Essential parameters of crescent moon sighting were collected from moon sight datasets and used to build an intelligent system of pattern recognition to predict the crescent sight conditions. The proposed ANN learned the datasets with an accuracy of more than 72% in comp

       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

A novel analytical method is developed for the determination of azithromycin. The method utilizes continuous flow injection analysis to enhance the chemiluminescence system of luminol, H2O2, and Cr(III). The method demonstrated a linear dynamic range of 0.001–100 mmol L-1 with a high correlation coefficient (r) of 0.9978, and 0.001–150 mmol L-1 with a correlation coefficient (r) of 0.9769 for the chemiluminescence emission versus azithromycin concentration. The limit of detection (L.O.D.) of the method was found to be 18.725 ng.50 µL−1 based on the stepwise dilution method for the lowest concentration within the linear dynamic range of the calibration graph. The relative standard deviation (R.S.D. %) for n = 6 was less than 1.2%