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Mobile Interfaces for Real-Time Surveillance of Antimicrobial Resistance

Antimicrobial resistance (AMR) refers to the ability of an organism to stop an antimicrobial (e.g., antibiotic) from working against it and has become a serious threat to public health since it causes antibiotics to be ineffective, resulting in 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. One manner to control these outbreaks is with real-time identification of AMR. Currently, the most effective method for identification of AMR is to apply high-throughput sequencing to a biological sample (e.g., nose swab or blood sample). Advancements in sequencing technology have shrunken the size of the devices so that they can fit into one hand, however the bioinformatics analysis ? requires comparing millions or billions of DNA sequences — has been limited to high performance computers that have significant memory and disk space. This, in turn, makes AMR identification limited in low-resource settings, such as rural areas of the U.S. In collaboration with Dr. Christina Boucher, KwangCheol Jeong, and Dr. Mattei Prosperi, this project will overcome the challenge of detection of AMR in rural areas by developing bioinformatics analysis methods for on-site, real-time detection of AMR using portable computing devices (such as phones and tablets).

SCH: INT: Enabling real-time surveillance of antimicrobial resistance