BME PhD Preliminary Exam Announcement for Zachary R. Davis (Deva Chan, advisor) Everyone is invited to attend the public presentation beginning at 1:00 pm. Dissertation title: Understanding spatial and volumetric content changes in cartilage and tissue engineered repair scaffolds using non-invasive imaging Date: April 4, 2024 Time: 1:00 pm Location: MJIS 2001 Committee: Deva D. Chan, Chair Craig J. Goergen Julie C. Liu Chad C. Carroll Abstract: Articular cartilage is a soft tissue that lines the end of bones. It contains a complex extracellular matrix with depth dependent properties. It primarily consists of cells called chondrocytes, collagen, proteoglycans, hyaluronic acid and water. Cartilage has poor self-healing properties due to having no blood flow. Thus, when damaged, cartilage begins a degradation process that can progress to severe diseases such as osteoarthritis. Understanding both the roles of cartilage components during both repair and degradation is pivotal to understanding cartilage biology. Repair methods in research include using collagen-based hydrogel constructs that incorporate natural constituents like hyaluronic acid and chondrocytes. However, there are limited non-invasive methods to determine if the repair is successful. Quantitative magnetic resonance imaging (qMRI) has been shown to correlate with biochemical content and ECM fibril organization in cartilage and other soft tissues. Preliminary results show that qMRI outputs could be sensitive to collagen scaffold components, inferring that it may be a useful tool to determine repair progress. Part of this study is to determine how and to what extent each hydrogel component is affecting qMRI outputs. Another part is to determine how chondrocytes remodel the ECM over a 21-day culture period, which would provide insight into what a successful repair looks like through MRI. Finally, we will be knocking out hyaluronan synthase genes to determine the roles of hyaluronic acid in cartilage and bone development in micro computed tomography. These data will be used to create a spatial image processing pipeline that detects where morphological changes occur.