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Introduction The trends of β-lactamases producing Enterobacteriaceae is ever increasing, and limited studies have reported investigating coexistence of β lactamases in Enterobacteriaceae. A cross-sectional study after approval from the Institutional Ethical committee was conducted between June 2014 and May 2016 in community-acquired infections due to multidrug-resistant organisms in our tertiary care. Nonrepetitive clinical samples from the out-patient department (OPD) were processed for bacteriological culture and identification of Enterobacteriaceae. An antibiotic susceptibility test, screening, and phenotypic confirmation for ESBLs and carbapenemases and AmpC producers were performed to check for coexistence of these enzymes. Results Nonrepetitive clinical specimens processed for culture and identification in our hospital revealed 417 positive isolates in community acquired infections which were multidrug-resistant organisms, and on screening for β-lactamases, 293 isolates were positive for one of the three beta lactamases, ESBL, AmpC, or carbapnemases. Coproduction of ESBL and MBL was seen in 5 isolates, 35 isolates showed coproduction of ESBL and AmpC enzymes, and AmpC and MBL coproduction was exhibited in only in 5 isolates. Conclusions Coexistence of ESBLs, AmpC producers, and carbapenemases has been described. Continuous monitoring and surveillance and proper infection control and prevention practices will limit the further spread of these superbugs within the hospital and beyond.

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The prescription of antibiotics empirically without confirmation of an infective etiology is on the rise. Administration of appropriate antibiotics can be guided by real-time fluorescence imaging using a point-of-care device. These composite images show the presence, type and the burden of infection. The time saved by this method over microbiological testing, especially in resource-poor settings, can lead to a paradigm shift in treatment by facilitating prompt and adequate antimicrobial therapy, surgical debridement as well as follow-up. Thumbnail sketches of a series of four cases highlighting different scenarios in which a fluorescent imaging device utilizing artificial intelligence and machine learning was found useful is presented in this report.

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