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Emerging nanophotonic platforms for infectious disease diagnostics: Re-imagining the conventional microbiology toolkit

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Wednesday, October 21, 2020
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12:00 pm - 1:00 pm
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Dr. Jennifer A. Dionne
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MatSci Lecture

We present our research controlling light at the nanoscale for infectious disease diagnostics, including detecting bacteria at low concentration, sensing COVID antibodies and antigens, and visualizing in-vivo inter-cellular forces. First, we combine Raman spectroscopy and deep learning to accurately classify bacteria by both species and antibiotic resistance in a single step. We design a convolutional neural network (CNN) for spectral data and train it to identify 30 of the most common bacterial strains from single-cell Raman spectra, achieving antibiotic treatment identification accuracies exceeding 99% and species identification accuracies similar to leading mass spectrometry identification techniques. Our combined Raman-CNN system represents a proof-of-concept for rapid, culture-free identification of bacterial isolates and antibiotic resistance.

Contact: Quiana Tyson