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Background and Objective Multidrug resistance is emerging worldwide at an alarming rate among a variety of bacterial species, causing both community-acquired and nosocomial infections, including Enterobacteriaceae. The clinical utility of carbapenems, the agents of last resort against multi-drug resistant Enterobacteriaceae, is under threat with the growing incidence of pan resistant isolates. This study aims to determine the prevalence of carbapenemase production among Enterobacteriaceae, as such data is not available from this area. The study, prospective in design, was conducted during the period from April 2016 -Materials and Methods August 2018. Any isolate reistant to IMP and/or MRP were subjected to phenotypic detection (Modied Hodge Test, MHT and Double Disk Synergy Test, DDST) and genotypic (blaIMP, blaKPC, blaVIM, blaOXA-48, blaOXA-23, blaOXA-51, blaOXA-58 and blaNDM) basis of carbapenamase production. A total of 218 consecutive isolates of Enterobacteriaceae, which were resistant to IMP and/or MRP, wereResult: considered for the study. The most common isolates of Enterobacteriaceae were Klebsiella pneumoniae (n=122, 55.5%) followed by Escherichia coli (n=80, 36.4%). Modied Hodge Test (MHT) were positive for 12, 02 and Double Disc Synergy Test (DDST) were 11, 05 for Klebsiella and E.coli respectively. Multiplex PCR I and II, was performed for 36 isolates. The carbapenemase producing organisms were positive for 60% for single gene and 36% for multiple genes. Delay in detection of carbapenemase producing organisms result in longer hospitalizationConclusion: and increased health-care cost and this further affects the morbidity and mortality of patients in hospital set up.

Today, wherever we go we will find one thing or the other that is running in cloud. It has become an essential component in our everyday life. Many cloud service providers have been extensively working on improving the performance of the service provided. Highavailability in cloud is a place where various cloud service providers are extensively researching. High availability in cloud can vary depending on the scenario it is considered under. In our case we are going to see this in the form of response time for the request to get processed and how we can improve it. We are proposing an algorithm which will ensure the above and also provide an efficient model for analysing the same. We will also be configuring Reinforcement machine learning algorithm for training the load balancer and the server to process the request faster in a short duration of time.