3 results match your criteria: "China National Center for Food Safety Risk Assessmentgrid.464207.3[Affiliation]"
Microbiol Spectr
October 2022
Key Laboratory of Food Safety Risk Assessment, National Health Commission, China National Center for Food Safety Risk Assessmentgrid.464207.3, Beijing, People's Republic of China.
is a foodborne pathogen associated with severe infections in restricted populations and particularly with high mortality in neonates and infants. The prevalence and antimicrobial resistance (AMR) phenotype of cultured from powdered infant formula and supplementary food were studied. The virulence factors, AMR genes, and genomic environments of the multidrug-resistant isolates were further studied.
View Article and Find Full Text PDFMicrobiol Spectr
August 2022
Division IV of Food Safety Standards, China National Center for Food Safety Risk Assessmentgrid.464207.3, Beijing, China.
Sequence type 88 (ST88) methicillin-resistant Staphylococcus aureus (MRSA) has been recognized as an important pathogen that causes infections in humans, especially when it has strong biofilm production and multidrug resistance (MDR). However, knowledge of the determinants of resistance or virulence and genomic characteristics of ST88 MRSA from China is still limited. In this study, we employed the antimicrobial resistance (AMR), biofilm formation, and genomic characteristics of ST88 MRSA collected from various foods in China and estimated the worldwide divergence of ST88 MRSA with publicly available ST88 genomes.
View Article and Find Full Text PDFmSystems
August 2021
School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, Leicestershire, United Kingdom.
Antimicrobial resistance (AMR) is becoming one of the largest threats to public health worldwide, with the opportunistic pathogen Escherichia coli playing a major role in the AMR global health crisis. Unravelling the complex interplay between drug resistance and metabolic rewiring is key to understand the ability of bacteria to adapt to new treatments and to the development of new effective solutions to combat resistant infections. We developed a computational pipeline that combines machine learning with genome-scale metabolic models (GSMs) to elucidate the systemic relationships between genetic determinants of resistance and metabolism beyond annotated drug resistance genes.
View Article and Find Full Text PDF