Phages are one of the key components in the structure, dynamics, and interactions of microbial communities in different bins. It has a clear impact on human health and the food industry. Bacteriophage characterization using in vitro approaches are time/cost consuming and laborious tasks. On the other hand, with the advent of new high-throughput sequencing technology, the development of a powerful computational framework to characterize the newly identified bacteriophages is inevitable for future research. Machine learning includes powerful techniques that enable the analysis of complex datasets for knowledge discovery and pattern recognition. In this study, we have conducted a comprehensive review of machine learning methods application using different types of features were applied in various aspects of bacteriophage research including, automated curation, identification, classification, host species recognition, virion protein identification, and life cycle prediction. Moreover, potential limitations and advantages of the developed frameworks were discussed.
Background and Objective: Due to the emergence and development of antimicrobial resistance in coagulase-negative Staphylococci (CoNS), which is mainly a normal flora of the skin surface and mucous membrane of humans, and the limitation of therapeutic options, this study was aimed to investigate the antibiotic resistance pattern in CoNS strains isolated from clinical specimens. Materials and Methods: In this cross-sectional descriptive study, a total of 44 isolates of coagulase-negative staphylococci were examined from clinical specimens of the patients using standard biochemical methods. Disc diffusion test was utilized to detect resistance to common antibiotics. Multiplex polymerase chain reaction (PCR) was employed to determine the frequency of resistance genes, namely mecA and vanA. Results: The results of disc diffusion test showed that the isolates had the highest resistance rate to erythromycin as 88.64%; while the lowest resistance rate to meropenem was observed 4.55%. A molecular analysis indicated the presence of 18.18% of the mecA gene in the isolates; however no isolates containing vanA gene were observed. Conclusion: Considering the frequency of mecA gene, results of antibiotic resistance pattern among coagulase-negative Staphylococci strains, and lack of any resistance observations to vancomycin by PCR, it is necessary to conduct more precise laboratory methods for the detection of antibiotic resistance to prevent resistance spread in this bacterium.
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