QUERCETIN AND ELLAGIC ACID IN GASTRIC ULCER PREVENTION: AN INTEGRATED SCHEME OF THE POTENTIAL MECHANISMS OF ACTION FROM IN VIVO STUDY

  Objective: The present study was initiated to describe the gastroprotective role of quercetin (Qu) and ellagic acid (EA) on aspirin-induced gastric ulcer (GU) in rats.Methods: Forty adult female albino rats of Wistar strain were distributed into: Control group, GU group, Omeprazole group, Qu group, and EA group. Gross examination, biochemical analyses including serum adrenocorticotropic hormone (ACTH), serotonin (ST), ferritin, heme oxygenase-1 (HO-1), interleukin-2 (IL-2), advanced glycosylation end products (AGEs), and fibronectin (FN) levels were estimated. Moreover, histopathological and histochemical examinations of stomach tissue samples were carried out.Results: Gross examination of gastric mucosa of rats in GU group revealed hyperemia of the stomach mucosa. Furthermore, rats in GU group experienced a significant rise in serum ACTH, ferritin, HO-1, IL-2 and AGEs levels accompanied with significant drop in serum ST and FN levels versus control counterparts. Pre-treatment of GU group with Omeprazole, Qu or EA caused marked improvement in the measured biochemical parameters. Histopathological and histochemical examinations of stomach tissue samples documented the protective action of Omeprazole, Qu and EA with different degrees against GU caused by aspirin.Conclusion: As a conclusion to this study, we can state that both Qu and EA have gastroprotective effect against aspirin-induced GU in rat model. Of note, Qu showed superior impact than EA as an antiulcer agent in this study. The corresponding mechanisms are speculated to be associated with inhibiting stress-induced gastric lesion, attenuating the oxidative stress, iron chelation and blunting ferritin level, modulating inflammatory cascade, and promoting the healing process

Multivariate Analysis for Fault Diagnosis System

Many multivariate techniques have been applied to diagnose faults such as Principal Component Analysis (PCA), Fisher's Discriminant Analysis (FDA), and Discriminant Partial Least Squares (DPLS). However, it has been shown that FDA and DPLS are more proficient than PCA for diagnosing faults. And recently applying kernel on FDA which is called KFDA (Kernel FDA) has showed outperformance than linear FDA based method. We propose in this research work an advanced KFDA for faults classification with Building knowledge base for faults structure using FSN. A case study is done on a chemical G-Plant process, constructed and experimental runs are done in Okayama University, Japan. The results are showing improving performance of fault detection rate for the new model over FDA

A Forecasting Decision Support System

Nowadays forecasting is needed in many fields such as weather forecasting, population estimation, industry demand forecasting, and many others. As complexity and factors increase, it becomes impossible for a human being to do the prediction operation without support of computer system. A Decision support system is needed to model all demand factors and combine with expert opinions to enhance forecasting accuracy. In this research work, we present a decision support system using winters', simple exponential smoothing, and regression statistical analysis with a new proposed genetic algorithm to generate operational forecast. A case study is presented using real industrial demand data from different products types to show the improved demand forecasting accuracy for the proposed system over individual statistical techniques for all time series types

Development of Tea Tree Oil Based Nanoemulgel Loaded with Azithromycin for Enhancing the Antibacterial Activity

Azithromycin (AZ) is an azalide macrolide antibiotic that is frequently employed for treating bacterial skin infections. It suffers from limited oral bioavailability, which results from incomplete absorption or extensive first-pass metabolism. Therefore, preparing azithromycin formulations for topical administration is highly recommended to avoid first-pass metabolism and to boost the concentration of the drug on the skin. The objective of our investigation was to formulate and evaluate the efficacy of AZ-loaded nanoemulgel as an antimicrobial drug. The physical appearance, spreadability, viscosity, particle size, in vitro drug release, ex vivo permeation investigations, and antimicrobial efficiency of the prepared formulations were evaluated. The prepared formulation loaded with AZ exhibited good physical quality. AZ-loaded nanoemulgel had a greater ex vivo drug permeation across rabbit skin than other formulations (AZ-loaded gel and AZ-loaded emulgel), revealing improved drug permeation and greater transdermal flux in addition to enhanced antibacterial efficacy (p < 0.05). Overall, our findings imply that tea-tree-oil-based nanoemulgel would be a promising delivery system for enhancing the antimicrobial efficiency of azithromycin