Christina Boucher, Ph.D., associate professor in the UF Department of Computer & Information Science & Engineering (CISE), has received a $1.2 million grant from the National Science Foundation (NSF). The grant will give Dr. Boucher and her team the opportunity to develop a set of algorithms and an electronic interface that will allow public health investigators to test and analyze biological samples for antibiotic resistance in rural areas.
Antimicrobial resistance (AMR) refers to the ability of an organism to stop an antimicrobial (e.g., antibiotic) from working against it. AMR has become a serious threat to public health, as it causes antibiotics to be ineffective, resulting in microbial outbreaks becoming more frequent, widespread, and severe. It is estimated that 2.8 million people per year in the United States are infected with resistant bacteria, and more than 35,000 of these infections are lethal.
Moreover, according to a 2016 report by the National Academies of Medicine, antimicrobials for livestock account for 80% of the antimicrobials purchased in the U.S. Feeding sub-therapeutic concentrations of antibiotics to livestock causes them to grow bigger, faster, and less expensively. The fear is that this practice leads to bacteria in the guts and on the skin of livestock that become resistant to antibiotics, which are then passed on to humans.
One method of controlling AMR outbreaks is real-time identification of AMR. High-throughput sequencing technology has been proven to be effective in identification of AMR, but in the past both the technology and analysis were not portable. Now, advancements in sequencing technology have shrunken the size of the devices used so that they can fit into one hand, making the sequencing technology portable; but the analysis of the resulting data requires comparing millions or billions of DNA sequences. This analysis has been limited to high performance computers that have significant memory and disk space, limiting AMR identification in low-resource settings, such as rural areas.
Dr. Boucher’s research project will overcome the challenge of detection of AMR in rural areas by developing novel algorithms and interfaces for on-site, real-time detection of AMR using consumer portable computing devices such as smartphones and tablets. This will, in turn, lead to a completely portable system for AMR identification, which can be used in areas remote from large data analysis centers.