A new study suggests that using big data and machine learning in antimicrobial resistance (AMR) surveillance in livestock production methods could help inform interventions and offer protections against germs that are becoming resistant to antibiotics.
Over two and a half years, researchers at the University of Nottingham analysed microbiomes from chickens, carcasses and environments. The resulting network of correlations between livestock, environments, microbial communities and antimicrobial resistance suggests multiple routes for improving antimicrobial resistance surveillance in livestock production.
The findings also show that a core subset of the chicken gut microbiome, featuring clinically relevant bacteria and antibiotic resistance genes correlates with AMR profiles ofcolonizing the gut. Notably, this core, which contains clinically high transmissible ARGs shared by chickens and environments, is influenced by environmental temperature and humidity, and correlates with antimicrobial usage.
Around 600 million cases of food-borne disease, resulting in approximately 420,000 deaths, occurs worldwide every year. Within this, nearly 300 million illnesses and 200,000 deaths are caused by diarrheagenic E. coli globally.In many countries, chickens are housed in sheds that do not have an effective climate control system, and therefore experience substantial temperature and humidity variations.
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