Multiple sclerosis (MS) is a chronic, inflammatory demyelinating disease of central nervous system with complex etiopathogenesis that impacts young adults (Lee et al., 2015), and MS impacts younger and middle aged character and leads to a range of disabilities that can alter their daily routines (Yara et al, 2010). Although, the exact cause of MS is still undetermined, the disease is mediated by adaptive immunity through the infiltration of T cells into the central nervous system (Bjelobaba et al, 2017). MS causes the Focal neurological symptomsand biochemical changes in the molecular level and the variation of neural cells such as loss or alteration of sensation, motor function, visible signs such as blurred vision or transient blindness,

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%

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

Everybody is connected with social media like (Facebook, Twitter, LinkedIn, Instagram…etc.) that generate a large quantity of data and which traditional applications are inadequate to process. Social media are regarded as an important platform for sharing information, opinion, and knowledge of many subscribers. These basic media attribute Big data also to many issues, such as data collection, storage, moving, updating, reviewing, posting, scanning, visualization, Data protection, etc. To deal with all these problems, this is a need for an adequate system that not just prepares the details, but also provides meaningful analysis to take advantage of the difficult situations, relevant to business, proper decision, Health, social media, sc

4-((2-hydroxy-3,5-dinitrophenyl)diazenyl)-1,5-dimethyl-2-phenyl-1H-pyrazol-3(2H)-one was produced through the reaction of diazonium salt from 4-amino antipyrine with 2,4-dinitrophenol. This ligand is examined by (UV-Vis, FTIR,1H,13CNMR, and LC-Mass) spectral techniques and micro elemental analysis (C.H.N.O). Co(II), Ni(II), Cu(II), and Zn(II) complexes were also performed and depicted. Metal chelates were distinguished by utilizing flame atomic absorption, infrared analysis, and elemental, visible, as well as ultraviolet spectroscopy, in addition to conductivity and magnetic quantification. Methods of mole ratio and continuous contrast have been studied to determine the nature of the compounds. Beer's law was followed throughout a co

The taxonomy of Ficus L., 1753 species is confusing because of the intense morphological variability and the ambiguity of the taxa. This study handled 36 macro-morphological characteristics to clarify the taxonomic identity of the taxa. The study revealed that Ficus is represented in the Egyptian gardens with forty-one taxa; 33 species, 4 subspecies and 4 varieties, and classified into five subgenera: Ficus Corner, 1960; Terega Raf., 1838; Sycomorus Raf., 1838; Synoecia (Miq.) Miq., 1867, and Spherosuke Raf.,1838; out of them seven were misidentified. Amongst, four new Ficus taxa were recently introduced to Egypt namely: F. lingua subsp. lingua Warb. ex De Wild. & T. Durand, 1901; F. pumila L., 1753; F. rumphii Blume, 1825, and F. su