Researchers at the University of British Columbia-Okanagan have developed a new microwave sensor that can quickly gather detailed data about how bacteria grow, allowing them to measure with a greater degree of specificity whether bacteria will be susceptible to antibiotics.
According to a study published in Scientific Reports, researchers developed a contactless planar microwave resonator sensor designed as an antibiotic susceptibility test (AST). The sensor uses microwaves to detect very precise changes in bacterial growth when in contact with different concentrations of antibiotics, changes that often go unnoticed to the naked eye. The microwave uses a split ring microwave resonator to detect charged particles released by bacteria when exposed to antibiotics, allowing for a more precise understanding of how bacteria respond to antibiotics. The specificity of the sensor may help doctors make more effective, accurate, and responsible decisions when it comes to treating patients with antibiotics.
Researchers noted that existing AST methods stand to be improved because of “the inadequacy of current standards in early detection of bacterial response to antibiotics and affordability of contemporarily used methods.” Previous research also indicates that the threat of antibiotic resistance necessitates new AST technologies that are minimally invasive and rapid (able to provide susceptibility information within the hour).
The rise in antibiotic resistance, specifically, is one of the most pressing concerns that the microwave sensor may help address. The Centers for Disease Control estimate that nearly 3 million people globally get an antibiotic-resistant infection each year. The research team at British Columbia note that the incorrect or inappropriate use of an antibiotic may be partly to blame for the rise in antibiotic resistance. And despite the need for new tools to combat the incorrect use of antibiotics, technology has not kept pace. As a result, the antibiotics prescribed for an infection either come too late or are not the correct kind for the bacteria causing infection.
The research team is considering ways to integrate artificial intelligence into their program, with the goal of improving the speed and effectiveness of their sensor. Either way, the new sensor may offer a more efficient and accurate way of testing for antibiotic susceptibility, improving patient outcomes and reducing the rate of bacterial infections that become treatment-resistant.