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Current Uses and Future Perspectives of Genomic Technologies in Clinical Microbiology

Journal

ANTIBIOTICS-BASEL
Volume 12, Issue 11, Pages -

Publisher

MDPI
DOI: 10.3390/antibiotics12111580

Keywords

antimicrobial resistance; microbial genomics; genome sequencing; metagenomics

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Recent advancements in sequencing technology and data analytics have revolutionized pathogen detection and typing, making the process faster and more cost-effective. Genomic analysis is becoming the standard for pathogen analysis and control, providing insights into pathogen epidemiology and antimicrobial resistance. Integrated genomic data can accurately predict resistance phenotypes and aid in the investigation and control of hospital outbreaks. When combined with epidemiological data, genomic data can provide profound insights into the evolution and genetic relationships of antimicrobial resistance in pathogens, hosts, and the environment.
Recent advancements in sequencing technology and data analytics have led to a transformative era in pathogen detection and typing. These developments not only expedite the process, but also render it more cost-effective. Genomic analyses of infectious diseases are swiftly becoming the standard for pathogen analysis and control. Additionally, national surveillance systems can derive substantial benefits from genomic data, as they offer profound insights into pathogen epidemiology and the emergence of antimicrobial-resistant strains. Antimicrobial resistance (AMR) is a pressing global public health issue. While clinical laboratories have traditionally relied on culture-based antimicrobial susceptibility testing, the integration of genomic data into AMR analysis holds immense promise. Genomic-based AMR data can furnish swift, consistent, and highly accurate predictions of resistance phenotypes for specific strains or populations, all while contributing invaluable insights for surveillance. Moreover, genome sequencing assumes a pivotal role in the investigation of hospital outbreaks. It aids in the identification of infection sources, unveils genetic connections among isolates, and informs strategies for infection control. The One Health initiative, with its focus on the intricate interconnectedness of humans, animals, and the environment, seeks to develop comprehensive approaches for disease surveillance, control, and prevention. When integrated with epidemiological data from surveillance systems, genomic data can forecast the expansion of bacterial populations and species transmissions. Consequently, this provides profound insights into the evolution and genetic relationships of AMR in pathogens, hosts, and the environment.

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