Gastrointestinal illnesses afflict more than 100 million people in the U.S. alone and are often indicated by gut microbiota motility. Typically, swarming bacteria are indicators of infection while swimming bacteria are more innocuous. Current diagnostic methods for intestinal diseases are lengthy, expensive, non-specific, or risky. This study proposes a new way to diagnose Inflammatory Bowel Disease (IBD) through quantitatively distinguishing bacterial motion. No quantitative models to differentiate bacterial motility currently exist. In this study, a novel diagnostic tool was developed that distinguishes swarming and swimming bacteria quantitatively for the first time. PDMS sheets, which are polymer sheets with wells permeable to oxygen, and microgears were used to study both motilities. Images on agar and intestinal tissue underwent Particle Image Velocimetry analysis (a type of imaging software that plots vectors for physical motion) for the calculation of Vortex, Nematic, and Polar Order Parameters (three kinds of physical metrics describing the organization of motion on both a group and individual level). These parameters were fed into a developed machine learning algorithm; accuracy was analyzed to ascertain the importance of each variable in motility distinction. VOPs were used in a Vicsek model, which describes the position and motion of particles with respect to their neighbors, for differentiating motion, demonstrating the importance of cell-cell alignment force in motility distinction — the model yielded high and low VOP values for swarming and swimming respectively. This novel tool can be refined for intestinal disease diagnosis, operating more economically, efficiently, specifically, and safely than conventional procedure.
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