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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15 January 2025Exploring frameworks for quantitative risk assessment of antimicrobial resistance along the food chain
Campylobacter, a major cause of bacterial gastroenteritis, is transmitted through birds, and the silent pandemic of antimicrobial resistance (AMR) is a significant public health challenge. This project aimed to describe the global knowledge on AMR Campylobacter, estimating the prevalence of fluoroquinolone-resistant Campylobacter isolated from broiler meat at retail level. A systematic literature review and meta-analysis […]
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23 December 2024Antimicrobial resistance pattern of Escherichia coli isolated from imported frozen shrimp in Saudi Arabia
This study examines the antimicrobial susceptibility patterns of Escherichia coli in imported frozen shrimp in retail markets in Saudi Arabia. The study found that 30 out of 40 samples tested positive for E. coli, resulting in an overall isolation rate of 75%. The largest number of positive samples were found in samples from China and […]
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16 December 2024Artificial intelligence-driven quantification of antibiotic-resistant Bacteria in food by color-encoded multiplex hydrogel digital LAMP
The study developed an artificial intelligence-driven system for quantifying antibiotic-resistant bacteria in food using a color-encoded multiplex hydrogel digital loop-mediated isothermal amplification (LAMP) system. The system uses fluorophores to generate color-specific fluorescent spots, which are then identified and quantified using a deep learning model. This technology has potential for digital quantification in the food industry.
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