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%

       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

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

Background: A noninvasive method of determining the severity of coronary artery disease (CAD) is the use of cardiac biomarkers. This study aimed to investigate the use of serum levels of high-sensitivity cardiac troponin I (hs-cTnI) and N-terminal pro–B-type natriuretic peptide (NT-proBNP) in determining CAD severity.Methods: This cross-sectional study included 125 participants categorized by coronary angiographic results into 3 groups: group I (no or < 50% stenosis), group II (1 or 2 vessels with ≥ 50% stenosis), and group III (left main stem or ≥ 3 vessels with ≥ 50% obstruction). Serum hs-cTnI and NT-proBNP levels were measured by enzyme-linked immunosorbent assay. Data were analyzed using SPSS version 25.0 and described as pe

In this research, production of ethanol from waste potatoes fermentation was studied using Saccharmyses cerevisiae. Potato Flour was prepared from potato tubers after cooking and drying at 85°C. Homogenous slurry of potato flour was prepared in water at solid liquid ratio 1:10. Liquefaction of potato flour slurry with α-amylase at 80°C for 40 min followed by saccharification with glucoamylase at 65°C for 2 hr .Fermentation of hydrolysate with Saccharomyces cerevisiae at 35°C for two days resulted in production of 33 g/l ethanol.

      The parameters studied were; temperature, time of fermentation and pH. It was found that Saccharification process is affected by enzyme Amylo 300 conc

The extraction process of chlorophyll from dehydrated and pulverized alfalfa plant were studied by percolation method. Two  solvent systems were used for the extraction namely; Ethanol-water and Hexane-Toluene systems . The effect of circulation rate, solvent concentration, and solvent volume to solid weight ratio were studied. In  both ethanol water, and Hexane-Toluene systems it appears that solvent concentration is the most effective variable.