Secure Foods
Antimicrobial resistance (AMR)
AMR develops when bacteria, fungi or viruses are exposed to antibiotics, antifungals or antivirals. As a result, the antimicrobials become ineffective and infections may persist. In addition, medical interventions including surgery, chemotherapy and stem cell therapy may become impossible.
AMR is considered the biggest global threat of Health and Food Safety.
AMR Insights
For Food professionals who wish to prevent Antimicrobial resistance in raw materials, intermediate and finished dairy, meat and other food products, AMR Insights offers selected, global information and data, specific education and extensive networking and partnering opportunities.
AMR Insights is for:
- Farmers and other agrifood primary producers
- Quality staff in Food, Dairy and Meat processing companies
- Lab technicians in contract research and analysis laboratories
- Regulatory authorities staff
- Quality staff in Retail
Latest Topics
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18 August 2025Interconnections between the food system and antimicrobial resistance: A systems-informed umbrella review from a One Health perspective
This umbrella review examines antimicrobial resistance (AMR) in human food systems through a complex systems lens. Synthesizing 80 systematic reviews, it shows that AMR emerges from interconnected factors spanning human, animal, and environmental reservoirs. Antimicrobial use in livestock is a central driver, shaped by trade-offs between animal health, welfare, and farm economics. Evidence highlights feedback […]
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18 August 2025Antimicrobial resistance reservoirs in salmon and broiler processing environments, sidestreams, and waste discharges
This study investigates the prevalence of antimicrobial resistance (AMR) genes and bacteria in salmon and broiler sidestream materials, waste discharges, and processing environments. A hybrid capture-based sequencing approach revealed a diverse range of AMR genes, including high-risk genes like TolC and mdtE. The highest numbers of AMR genes were found in process wastewater and sludge. […]
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18 August 2025Machine learning-based predictive modeling of foodborne pathogens and antimicrobial resistance in food microbiomes using omics techniques: A systematic review
The globalization of food systems has increased the risk of foodborne pathogens like Salmonella and Listeria, and antimicrobial resistance (AMR). Traditional methods are labor-intensive and low-throughput, and relying on individual machine learning models limits predictive robustness. A systematic review of 13 studies using ML algorithms showed predictive accuracies up to 99% and AUROC scores above […]
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