The work includes synthesis of 1,2,3-triazoles via click conditions and using the microwave irradiation starting from two synthesized azides: 2,3,4,6-tetra-O-acetyl-β-D-glucopyranosyl azide (5) and perfluorobutylethyl azide (10) and different terminal alkynes. It also includes microwave enhanced synthesis of tetrazoles via the reaction of two synthesized azides i.e., perfluorobutylethyl azide (10) and 1,5-diazidopentane (13) with benzoyl cyanide. Most of the prepared compounds have been characterized by: TLC, FT-IR, 1H NMR, 13C NMR, LC-MS and microelemental analysis

Abstract

The experiment has been carried out in the Syrian National Commission of Biotechnology, during the growing season 2018/2019, to study the effect of abiotic stresses (salinity and osmotic stresses) on the activity of some antioxidant enzymes and biochemical traits in Catharanthus roseus. The experiment has been laid according to (CRD) with three replications. The seeds have been sterilized by NaOCl solution (0.5% v/v), then planted on MS medium. Plantlets have been moved to MS medium enriched with NAA (1 mg.L^-1) and BA (2 mg.L^-1). The callus has been initiated from leaves using MS medium containing NAA (1 mg L^-1) and KIN (2 mg.L^-1). After 60 days, callus

A total of 200 samples (180 fecal materials and 20 organ samples) were collected from (5 different poultry farms, 10 local poultry shops, 5 houses poultry, 5 Eggs stores shops and 5hand slaughters centers) in Ibb city, Yemen, 2014. According to morphological, cultural, as well as biochemical characterization and serological tests, 59(29.5%) isolates were identified as Salmonella spp. and all Salmonella isolates were categorized by serotype, which comprised of, 37(62.71%) Salmonella Typhimurium serovar, 21(35.59%). Salmonella Enteritidis serovar and 1(1.69%) Salmonella Heidlberg serovar. Antibiotic sensitivity test was done for bacterial isolates and the results showed there were clear differences in antibiotic resistant. Antimicrobial

Healthcare professionals routinely use audio signals, generated by the human body, to help diagnose disease or assess its progression. With new technologies, it is now possible to collect human-generated sounds, such as coughing. Audio-based machine learning technologies can be adopted for automatic analysis of collected data. Valuable and rich information can be obtained from the cough signal and extracting effective characteristics from a finite duration time interval that changes as a function of time. This article presents a proposed approach to the detection and diagnosis of COVID-19 through the processing of cough collected from patients suffering from the most common symptoms of this pandemic. The proposed method is based on adopt