Antibiotic resistance has become a significant public health threat. To combat the problem, a rapid pathogen identification (ID) and antimicrobial susceptibility testing (AST) technology is needed to provide timely diagno- sis of resistant infections and delivery of accurate antibiotic treatment at primary health-care settings, includ- ing hospitals and point-of-care (POC). The present project aims to develop a point-of-care AST (POCASTTM) technology based on a large-image-volume microscopy technique that enables direct detection of individual bacterial cells in clinical samples without culturing or pathogen isolation, and a machine-learning model that allows fast determination of pathogen and susceptibility. To establish the technology, the project will focus on urinary tract infections (UTIs). UTIs affect millions of people annually, and the pathogens that usually cause UTIs are the organisms that pose the highest threat of antimicrobial resistance, including carbapenem- resistant Enterobacteriaceae (CRE) and extended spectrum ?-lactamase (ESBL)-producing Enterobacteri- aceae. This project will focus on: 1) developing the large-image-volume microscopy and machine learning model for simultaneous tracking of multi-phenotypic features of single bacterial cells directly in patient urine sample, and performing rapid automatic pathogen ID and AST for UTIs; 2) building prototype instrument, and 3) validating the instrument for UTIs using large scale clinical samples. Successful development and validation of the tech- nology will enable precise antibiotic prescription on the same day of patient visit. The project will be carried out by a multidisciplinary team with expertise in biosensors (Biodesign Center for Bioelectronics and Biosensors, ASU), microbiology and infectious diseases (Biodesign Center for Immuno- therapy, Vaccines and Virotherapy, ASU), biomedical instrument development and production (Biosensing Instrument Inc.), and clinical testing (Clinical Microbiology Laboratory, Mayo Clinic). !
This project will develop a culture-independent technology for point-of-care diagnosis of antimicrobial-resistant bacteria in urinary tract infections within 3 hours, by imaging urine samples directly with an innovative large- image-volume imaging technique and analyzing the data with a machine-learning model. Successful devel- opment of the technology will enable precise antibiotic prescriptions and accurate treatment of the patient on the same day of visit. !
