Three graduate students will present their research at the second Beckman Institute Graduate Student Seminar of the spring 2022 semester: Yongdeok Kim, materials science and engineering; Neha Goswami, bioengineering; and Yudu Li, electrical and computer engineering.
The event takes place Wednesday, March 2 at noon. Register to attend on Zoom.
3D engineered bio-hybrid skeletal muscle tissue integrated with electronics system
Yongdeok Kim, materials science and engineering
Integrating electronics systems with 3D tissue models has great potential for diverse biomedical applications, including the 3D-organ-on-a-chip platform, implantable bioelectronics, and bio-hybrid soft robotics. In this work, in vitro 3D-engineered skeletal muscle tissues were integrated with diverse electronics systems — such as graphene electrodes, buckled electronics, and wireless optoelectronics — for 3D-organ-on-a-chip and soft robotics applications. Compliant 3D frameworks incorporated microscale strain sensors for high-sensitivity measurements of contractile forces of engineered optogenetic muscle tissues, applicable for drug screening and disease modeling. In addition, integration of wireless optoelectronics systems with optogenetics skeletal muscle-based bio-hybrid machines enabled the remote controllable actuation of a bio-hybrid machine and its advanced functions.
Yongdeok Kim is a Ph.D. candidate in materials science and engineering under the guidance of Rashid Bashir. His research focuses on developing hybrid 3D skeletal muscle based bioelectronics systems for biomedical applications. He received his bachelor’s degree and master’s degree in materials science and engineering from Hanyang University. Yongdeok was a NIH T32 Tissue Microenvironment (TiME) Fellow from the Cancer Center at Illinois in 2018-2020, and was also a 2020 Beckman Institute Graduate Fellow.
Rapid and label-free detection and classification of SARS-CoV-2 through phase imaging with computational specificity
Neha Goswami, bioengineering
Rapid detection of the SARS-CoV-2 virus and potentially other pathogens is the need of the hour. Current testing methods involve the use of external agents like stains, chemicals, or plasmonic sensors, which increase the cost and testing time. In this talk, I will discuss a rapid, label-free optical detection method for SARS-CoV-2 that is aimed at detecting the virus in the patient’s breath condensates. Using phase imaging with computational specificity (PICS), we can detect and classify SARS-CoV-2 versus other viruses (H1N1, HAdV and ZIKV) with 96% accuracy, within a minute after sample collection. Currently, we are working on the clinical validation of our proposed method. We have received breath samples of patients collected by our collaborators at UIC, and we are in the process of training our deep neural network for SARS-CoV-2 detection. Cross-validation of test results will be done through the gold-standard PCR test on the saliva sample collected from same patient. We aim to bring our method of combining quantitative phase imaging with machine learning to clinics for SARS-CoV-2 detection. This method can potentially be applied for detection of other pathogens as well.
Neha Goswami is a fourth-year Ph.D. student in the Department of Bioengineering. She works in the Quantitative Light Imaging Lab at the Beckman Institute under the supervision of Gabriel Popescu. She obtained her M.S. in photonics science and engineering from the Indian Institute of Technology, Kanpur in 2015, and her B.S. in electrical and computer engineering from Graphic Era University, India in 2012. She was a research assistant in electrical engineering at the Indian Institute of Technology, Bombay from 2016 to 2018. Neha's research interests revolve around developing optical techniques for biomedical imaging. Since the beginning of the pandemic, she has been working to develop a rapid, label-free SARS-CoV-2 detection technique through phase imaging with computational specificity. Pre-clinical results of the technique are published, and the clinical validation is underway. She received a 2021 Nadine Barrie Smith Memorial Fellowship for this work.
Simultaneous metabolic and multi-parametric mapping of the brain
Yudu Li, electrical and computer engineering
Magnetic resonance imaging obtains structural and functional information from the brain using water MR signals. MR spectroscopic signals, or MRSI, enables us to obtain MR signals from water and metabolites simultaneously, providing useful information about brain structure and metabolism. However, conventional MRSI methods suppress the dominant water signals for metabolic imaging, losing the structural imaging capability. This talk reports an emerging MRSI method developed by our group, called SPICE, which provides an unprecedented capability for simultaneous metabolic and multi-parametric brain mapping. I will provide a brief overview of our imaging technology and present some experimental results to demonstrate its unique capability.
Yudu Li is a Ph.D. candidate in electrical and computer engineering under the direction of Zhi-Pei Liang. He received his undergraduate degree in electronic engineering from Tsinghua University and his master’s degree in electrical and computer engineering at the University of Illinois Urbana-Champaign. Li has received a number of awards for his research accomplishments, including the Mavis Future Faculty Fellowship, the Rambus Computer Engineering Fellowship, and the Yee Fellowship Award.
Editor's note: See our Graduate Student Seminar webpage for upcoming speakers and topics.