Bacterial biofilms on wounds lead to severe infections because of their resistance to antibiotics and host defence mechanisms. These bacterial colonies comprising of bacterial cells embedded in an extracellular matrix can easily develop on wounds, are hard to detect, and significantly delay wound healing leading to chronic infections especially in diabetic foot ulcer patients. Current methods to detect bacterial biofilms are extremely cumbersome and time-consuming (2-3 days) and, therefore, pose a challenge to low-resource implementations. We demonstrate a machine learning aided rapid wound blot detection method (<10 min) that combines the wound blotting technique using nitrocellulose membranes, white light imaging and machine learning-based models to accurately infer the presence of biofilms. We validate our method against the standard test-tube method that utilizes dye staining of the wound-swab culture to infer biofilm presence and demonstrate a detection accuracy in excess of 85%.
Anti-microbial resistance has emerged as a major global threat. Due to lack of rapid AST tests, generic antibiotics are typically prescribed. Pathogens possess various metabolic biomarkers such as NAD(P)H and flavins which exhibit auto fluorescence. A rapid phenotypic point-of-care AST device is developed that leverages the changes in the autofluorescence when the pathogens encounter antibiotic stress. The device is integrated with multiple wavelength sources to excite various biomarkers, and a CMOS camera integrated with optical filters to capture the emitted autofluorescence intensity. The results show that the device is capable of determining antibiotic susceptibility with its significant minimum inhibitory concentration in under 5 hours suitable for point-of-care testing.
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