Antimicrobial Susceptibility Behavior of Bacterial Isolates from Different Clinical Samples at Nishtar Hospital Multan

The pathogenic bacteria are getting resistant to antibiotics is significantly growing in the developing countries of the world including Pakistan. The present study was designed to find the basic study on resistance among the patients coming to the Nishtar Hospital, Multan. The study was carried out in the Department of Pathology, Nishtar Hospital, Multan. Total 387 clinical samples of urine, pus, high vaginal swab (HVS) and wound were surveyed for the existence of Gram-positive and Gram-negative pathogens. For these bacterial isolates, antimicrobial susceptibility tests were performed. E. Coli was the most prevalent isolates followed by Staphylococcus aureusand Pseudomonas. E. Coli was predominated in urine, pus, HVS and wound specimens. Occurance of Staphylococcus aureus, MRSA, Candida and Pseudomonas were 7.9 %, 3.9 %, 14.7 % and 1.4 % respectively among the clinical specimens. E. Coli shows highest resistance to Linezolid (98.3%) followed by Ceftrizone (90.8%), Sulfamethoxazole + Trimethoprim (85%), Moxifloxacin (82.5%). High frequency of resistance specifies that there is an unremitting requirement of surveillance of resistance behaviour of antimicrobial agents in our study is to investigate the trend of this problem.

A Methodology for Brand Name Hierarchical Clustering Based on Social Media Data

In the recent years, on the social media, the consumer perceptions are very necessary components of brand equity and marketing strategy. Normally, cluster analysis of different brand names is done in supermarkets and in huge- scale retail sector organizations. Clustering of brand names is a common technique for identification of different groups of brand names with maximum effectiveness and similarities. For example, items of different brand name regularly purchased together are located in one place on the shelf of a retail store. There are different techniques used for clustering. Among these techniques, the agglomerative hierarchical clustering technique builds a cluster hierarchy that is commonly displayed as a tree diagram called a dendrogram. In this study, I proposed a methodology for brand name clustering after collecting data from social media like Twitters. Data of garment products from Twitter is collected for experiments.In order to cluster the brand names, I calculated the distance of paired brand names based on the total number of occurrences of paired brands names mentioned together. Hierarchical clustering is used to find similarities among brand names. Finally, clusters of brand names are visualized through dendrogram. The dendrogram clearly shows the maximum similarities among brand names.