Xibin Zhang

TL;DR: A microfluidic biosensor based on immunomagnetic separation, fluorescence labeling and smartphone video processing that was able to quantitatively detect Salmonella typhimurium and could be extended for detection of multiple foodborne pathogens using different fluorescent materials.

TL;DR: Isolates from different districts had similar pulsed-field gel electrophoresis (PFGE) patterns indicating that circulating foodborne Salmonella constitutes a potential public health issue across different districts.

TL;DR: An original data processing pipeline which combines omics, machine learning, gene sharing network and mobile genetic elements analysis, uncovered an extensive network of genes, correlated to AMR phenotypes, shared among livestock, humans, farm and slaughterhouse environments.

TL;DR: The developed predictive models could provide parts of the input data for microbial risk assessment of the poultry supply chain in China and showed the best fit as indicated by the pseudo-R2, root mean square error (RMSE) and standard error of prediction (SEP) values.

TL;DR: In this article , a poultry farm and connected slaughterhouse in China was investigated, where the authors combined shotgun metagenome sequencing, machine learning, and culture-based methods to investigate the gut microbiome of livestock, workers and their households, and microbial communities in carcasses and soil.