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Introduction: Carbapenem Resistant Enterobacteriaceae (CRE) are major cause of community as well as healthcare associated infections and have limited treatment options. Measuring the magnitude of the problem of CRE, it is important for making strategies to lower its spread. Aim: To assess the incidence and prevalence rate of CRE in a tertiary care hospital of Gujarat, India. Materials and Methods: Retrospective data was collected for a period from 2014 to 2018 using Laboratory Information System (LIS). Prevalence of CRE was determined as number of CRE isolated per 100 Enterobacteriaceae isolated during the study period whereas incidence rate was determined as number of CRE cases per 1000 patient-days. Consumption of Carbapenems was calculated as Defined Daily Dose (DDD) per 1000 patient-days. Demographic data including age, gender, location in the hospital and sample type from which CRE was isolated was also analysed using Microsoft Excel. Results: The incidence of CRE cases per 1000 patient-days in 2014 to 2018 was 1.66, 2.11, 1.90, 2.26 and 1.91, respectively with an overall incidence of 1.99 per 1000 patient-days. The overall prevalence of CRE over a period of five years was found to be 29.07%. Klebsiella sp. was the most common CRE and had the highest percentage of Carbapenem resistance among all Enterobacteriaceae. Conclusion: The rate of CRE in present study was high and worrisome. Screening of the patient for CRE, source isolation and stringent implementation of infection control practices is required to confine the spread of CRE in this institute. [ABSTRACT FROM AUTHOR]

The present work deals with the analysis and monitoring of bacterial resistance in using Python for the state of Gujarat, India, where occurrences of drug-resistant bacteria are prevalent. This will provide an insight into the portfolio of drug-resistant bacteria reported, which can be used to track resistance behavior and to suggest a treatment regime for the particular bacteria. The present analysis has been done using Python on Jupyter Notebook as the integrated development environment and its data analysis libraries such as Pandas, Seaborn, and Matplotlib. The data have been loaded from excel file using Pandas and cleaned to transform features into required format. Seaborn and Matplotlib have been used to create data visualizations and represent the data inexplicable manner using graphs, plots, and tables. This program can be used to study disaster epidemiology, tracking, analyzing, and surveillance of antimicrobial resistance with a proper system integration approach. [ABSTRACT FROM AUTHOR]

Dirofilariasis is a worldwide zoonotic filariasis with over 782 cases reported so far from different parts of the world. Human dirofilariasis, caused by Dirofilaria repens, have been reported to occur widely throughout Asia, Europe, and Africa. It has not been widely recognized in India, however; several cases have been reported in last few years. There is probably a focus of human infection with D. repens in Kerala. Herein, we present a review of human infections by D. repens, along with a case report of subconjunctival dirofilariasis from rural part of Gujarat.