Parkinson's disease (PD) is a neurodegenerative disease that affects motor abilities with increasing severity as the disease progresses. Traditional methods for diagnosing PD require specialists scoring qualitative symptoms using the motor subscale of the Unified Parkinson's Disease Rating Scale (UPDRS-III). Using force-plate data during quiet standing (QS), this study uses machine learning to target the characterization and prediction of PD and UPDRS-III. The purpose of predicting different subscores of the UPDRS-III is to give specialists more tools to help make an informed diagnosis and prognosis. The classification models employed classified PD with a sensitivity of 87.5% and specificity of 83.1%. Stepwise forward regression indicated that features correlated with base of support were most useful in the prediction of head rigidity (r-square = .753). Although there is limited data, this thesis can be used as an exploratory study that evaluates the predictability of UPDRS-III subscores using QS data. Similar prediction models can be implemented to a home setting using low-cost force plates as a novel telemedicine technique to track disease progression.

Electrospun nanofibers have been researched extensively in the culturing of stem cells to understand their behavior since electrospun fibers mimic the native extracellular matrix (ECM) in many types of mammalian tissues. Here, electrospun nanofibers with defined structure (orientation/alignment) and pore size could significantly modulate human mesenchymal stem cell (hMSC) behavior. Controlling the fiber membrane pore size was predominantly influenced by the duration of electrospinning, while the alignment of the fiber membrane was determined by parallel electrode collector design. Electric field simulation data provided information on the electrostatic interactions in this electrospinning apparatus.hMSCs on small-sized pores (~3-10 µm²) tended to promote the cytoplasmic retention of Yes-associated protein (YAP), while larger pores (~30-45 µm²) promoted the nuclear activation of YAP. hMSCs also displayed architecture-mediated behavior, as the cells aligned along with the fiber membranes orientation. Additionally, fiber membranes affected nuclear size and shape, indicating changes in cytoskeletal tension, which coincided with YAP activity. The mechanistic understanding of hMSC behavior on defined nanofiber structures seeks to advance their translation into more clinical settings and increase biomanufacturing efficiencies.