Two simple, rapid, and useful spectrophotometric methods were suggest or the determination of sulphadimidine sodium (SDMS) with and without using cloud point extraction technique in pure form and pharmaceutical preparation. The first method was based on diazotization of the Sulphdimidine Sodium drug by sodium nitrite at 5 ºC, followed by coupling with α –Naphthol in basic medium to form an orange colored product . The product was stabilized and its absorption was measured at 473 nm. Beer’s law was obeyed in the concentration range of (1-12) μg∙ml-1. Sandell’s sensitivity was 0.03012 μg∙cm-1, the detection limit was 0.0277 μg∙ml-1, and the limit of Quantitation was 0.03605μg∙ml-1.The second method was the cloud point extraction (CPE) using Trtion X-114 as surfactant. Beer’s law was obeyed in the concentration range of (1-12) μg∙ml-1. Sandell’s sensitivity was 0.02958 μg∙cm-1, the detection limit was 0.01745 μg∙ml-1, and the limit of quantitation was 0.028303 μg∙ml-1. All variables including the reagent concentration, reaction time, color stability period, and mole ratio were studied in order to optimize the reaction conditions. The mole ratio for the composition of product is (1:1). Both methods were effectively useful to the determination of sulphdimidine sodium in pharmaceutical dose form. The attained results were in a good agreement with the official and other methods in the literature .No interference were observed from the commonly encountered additives and excipients.
Autorías: Hadeer Idan Ghanim, Ishraq Mahmood. Localización: Revista iberoamericana de psicología del ejercicio y el deporte. Nº. 3, 2021. Artículo de Revista en Dialnet.
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Flavonoids were extracted from Zizyphus spina-christi leaves by Ethyl acetate after acid digested and used as antioxidant. The dried extract was added separately to each sample of fat extracted from hallow cow and sheep bones as follows: T1 cow fat, T2 control for cow fat, T3 sheep fat and T4 control for sheep fat (the control T2 and T4 reffered to samples without added antioxidant).
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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
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