Skip to main content
. Author manuscript; available in PMC: 2023 Aug 26.
Published in final edited form as: ACS Sens. 2022 Aug 5;7(8):2262–2272. doi: 10.1021/acssensors.2c00788

Figure 4. Differentiation of bacterial cells from urine particles by phenotypic features tracking.

Figure 4.

(A) Single cell motion and intensity mapping for cultured E. coli cells and urine particles. (B) Comparison of the corresponding micro motion (top panel) and intensity fluctuation (lower panel) of a single E. coli cell and a single urine particle. (C) The corresponding training results of E. coli (n = 185) and urine particles (n = 155) with machine learning classification (Support Vector Machine, SVM) based on mean squared displacement (MSD) of single cell motion and normalized intensity standard deviation (NISD) of single cell intensity. (D) The corresponding testing results of E. coli (n = 80) and urine particles (n = 66) with the trained SVM model. Scale bar: 20 μm.