Determination of Antibiotic Drug Cefdinir in Human Plasma Using Liquid Chromatography Tandem Mass Spectroscopy

The following article has been retracted due to the investigation of complaints received against it. The Editorial Board found that there are conflicts of interests between the authors and their organization. The scientific community takes a very strong view on this matter, and the American Journal of Analytical Chemistry treats all unethical behavior seriously. This paper published in Vol. 6 No. 3 239-254, 2015 has been removed from this site. Title: Determination of Antibiotic Drug Cefdinir in Human Plasma Using Liquid Chromatography Tandem Mass Spectroscopy Author: Mohammad A. Al Bayyari, Raed S. Abu

Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System

With the increasing and rapid growth rate of COVID-19 cases,the healthcare scheme of several developed countries have reached the point of collapse.An important and critical steps in fighting against COVID-19 is powerful screening of diseased patients,in such a way that positive patient can be treated and isolated.A chest radiology image-based diagnosis scheme might have several benefits over traditional approach.The accomplishment of artificial intelligence(AI)based techniques in automated diagnoses in the healthcare sector and rapid increase in COVID-19 cases have demanded the requirement of AI based automated diagnosis and recognition systems.This study develops an Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19Monitoring System(IFFA-DTLMS).The proposed IFFADTLMSmodelmajorly aims at identifying and categorizing the occurrence of COVID19 on chest radiographs.To attain this,the presented IFFA-DTLMS model primarily applies densely connected networks(DenseNet121)model to generate a collection of feature vectors.In addition,the firefly algorithm(FFA)is applied for the hyper parameter optimization of DenseNet121 model.Moreover,autoencoder-long short term memory(AE-LSTM)model is exploited for the classification and identification of COVID19.For ensuring the enhanced performance of the IFFA-DTLMS model,a wide-ranging experiments were performed and the results are reviewed under distinctive aspects.The experimental value reports the betterment of IFFA-DTLMS model over recent approaches.

Evaluation of hypoglycemic and hypolipidemic activity of methanol extract of Brassica oleracea

AIM: The hypoglycemic and hypolipidemic effects of the methanol extract of Brassica oleracea var. capitata(MEB) was evaluated in alloxan-induced diabetic rabbits. METHOD: The study was conducted on twenty-eight healthy white rabbits of either sex. All animals were equally divided into four groups. After confirmation of hyperglycemia, the animals of the treated and standard groups were administered MEB(500 mg·kg-1) and glibenclamide(10 mg·kg-1), respectively for 15 and 30 days. The animals of the normal and diabetic controls received normal saline 1 mL/day equivalent to the volume of doses given to the test and standard animals. Biochemical tests were performed at the end of dosing, i.e. the 16 th and 31 st days. RESULTS: The MEB revealed a decrease of 106.6 mg·dL-1 in fasting blood glucose as compared to diabetic control, which was almost comparable to glibenclamide; both of these changes were highly significant. The decrease in total cholesterol and low density lipoprotein was 94.3 and 96.5 mg·dL-1, respectively, whereas the high-density lipoprotein was increased by 26.7 mg·dL-1, as compared to diabetic control. All of the changes in lipid profile were statistically significant. CONCLUSION: These results suggest the potential of MEB as a hypoglycemic and hypolipidemic agent.