Antimicrobial resistance (AMR) is predicted to be the leading cause of death around the world by 2050. It is imperative to explore new strategies to slow down the evolution of resistance and preserve our ability to fight infectious diseases. A critical area for intervention includes the correct and rapid diagnosis of microbes causing disease in patients. For urinary tract infections (UTI), the second most common infection in humans, the standard diagnosis method (microbial culturing and mass spectrometry) takes 1 to 2 days. During this time, patients receive broad-spectrum antibiotics increasing the selection of new resistances in the population.
Moreover, this method cannot detect antimicrobial resistance. Our team has developed MICROB-AI, a technology based on protein signatures and artificial intelligence, to identify pathogens causing UTI in 4h. In this proposal, we move beyond state-of-the-art by extending our work to the detection of resistance (MICROB-AI-R+) that can be used to inform clinicians on effective treatment strategies. This will limit the evolution of resistance and will reduce costs associated with UTI in Canada. Overall, the proposed project is a proof-of-concept for a technology that could make Quebec and Canada a leader in the appropriate use of antibiotics in healthcare.