ABSTRACT
The excessive use of broad-spectrum | antibiotics will lead to drug resistance of microorganism and i specially nosocomial organisms. Because of high incidence of 1 antibiotic resistance in hospitals, we aimed to study antibiotic resistance to gram negative bacteria. This cross-sectional study was conducted on the data of biological samples [2006-2008], with positive culture result. Using antibiogram, microbial resistance to isolated microorganism was determined, and after culturing the samples, bacteria were identified by using differential media and antiserum. Then, antibiotic resistance was performed by disk diffusion. The most common gram-negative microorganism obtained from all cultures was E.coli with the lowest drug resistance to Nitrofurantoin. Based on the results, antimicrobial resistance pattern is not the same in different places and furthermore it is ever changing. Therefore, further research is needed to be done to have an accurate pattern of antibiotic resistance to provide effective treatment regimens
ABSTRACT
Introduction: In the last 10 years the incidence of diabetes has doubled worldwide with annual increasing rate of about 6%. More than 2 million people in Iran are now affected by this disease. The present research deals with the relation between the observed complications of type 2 diabetic patients and some related features like Blood Glucose Level, Blood Pressure, Age, and Family History. The main purpose was to predict the patients' complications based on the observed signs
Methods: The research data were gathered from 856 patient records related to the 2009's cases in the Diabetes Center of Golestan province. A new model based on the standard methodology CRISP was developed. In the modeling section, two well-known data mining techniques called C5.0 decision tree and Neural Network were used. Celementine 12.0 software was implemented for data analysis
Results: The results of data mining showed that the variables of high blood pressure, age, and family history had the most impact on the observed complications. Based on the created decision tree, some rules have been extracted which can be used as a pattern to predict the probability of occurring these complications in the patients. The accuracy of the C5.0 model on the data was shown to be 89.74% and on the Artificial Neural Network was 51.28%
Conclusion: As the highest accuracy was shown to be achieved using C5.0 algorithm, according to the created rules, it can be predicted which complication[s] any diabetic patient with new specified features may probably suffer from
ABSTRACT
The Reactive oxygen species [ROS] is induced in the cells following various stresses but the effect of recombinant protein expression on ROS generation has not been studied yet. In this study, H2O2 concentration and catalase activity variations and their correlation with cell growth following cytoplasmic expression of human interleukin-2 [hIL-2] and mouse interleukin-4 [mIL-4] in Escherichia coli were investigated. Additionally, the effect of recombinant protein expression under different conditions was compared to the effect of foreign DNA introduction on these factors. Plasmids pEThIL-2 and pETmIL-4 were used for expression of human interleukin-2 [hIL-2] and mouse interleukin-4 [mIL-4] inside the cytoplasm of the cells. Having confirmed protein expression using SDS-PAGE analysis and ELISA assay, H2O2 concentration and catalase activity were measured at various ODs. Empty vector introduction increased significantly H2O2 concentration of the cells. However, H2O2 concentration in hIL-2 and mIL-4 expressing cells was significantly higher than its concentration in empty vector transformed cells. Catalase activity was reduced in foreign DNA introduced cells. It was more lowered following expression of recombinant proteins. Results of this study revealed the relationship between foreign DNA introduction and protein expression with H2O2 elevation and catalase activity reduction. There was also correlation between H2O2 elevation and reduction in catalase activity with the cell growth depression