BME Seminar: Katie Sosnowski
Monday, November 28th, 2022 - 12:00 p.m.
Katie Sosnowski
Ph.D. Candidate
Biomedical Engineering
University of Arizona
"Signals From Out of the Blue (Light Wavelengths): Portable, Low-cost Camera-based Optical Chemical/Bio-Sensors for Detecting Chemical and Biological Targets Utilizing Fluorescence and Machine Learning Techniques"
Keating 103 | Live Zoom Link: Passcode: BearDown
(Instructor permission required for enrolled students to attend via Zoom)
Hosts: Dr. Beth Hutchinson and Dr. Shang Song
Persons with a disability may request a reasonable accommodation by contacting the Disability Resource Center at 621-3268 (V/TTY).
Abstract: Optical chemical/bio-sensors have the capacity to rapidly respond to challenges in our society such as oil spills, viral diseases, and medical conditions affected by bacterial imbalance. These powerful sensors often have many advantages including ease of use, low limits of detection, inexpensive implementation, and fast results. Such features are made possible using widely available cameras (such as the Raspberry Pi camera module and smartphone cameras), fluorescent particles or inherent molecular autofluorescence, and machine learning algorithms such as support vector machine (SVM) and neural networks. Four examples of optical chemical/bio-sensor methods will be demonstrated in this work. First, Sosnowski created a field-ready, Raspberry Pi-powered autofluorescence sensor for analyzing ocean oil samples using SVM, to assist cleanup efforts after a spill. Then, when COVID-19 arrived in the US, Sosnowski developed two different smartphone biosensor methods for SARS-CoV-2 antigen detection. The first uses a custom-built device for quantifying fluorescent particle immunoagglutination from smartphone images to determine if a saline gargle sample is positive or negative for COVID-19. The second simply requires a smartphone video to analyze the flow rate profile of particles moving along a paper microfluidic channel that is pre-loaded with a saline gargle sample, relying on changes in surface tension during flow to determine if the sample contains SARS-CoV-2. Finally, Sosnowski designed a custom-built autofluorescence device that uses smartphone images and a convolutional neural network (CNN) to determine whether or not a bacterial sample is dominated by Staphylococcus aureus, as is common in patients with atopic dermatitis or eczema. These projects highlight the adaptability and usefulness of optical chemical/bio-sensors for shedding blue or UV light on the microscopic elements affecting our daily lives.