Considering that both cardiovascular disease (CVD) and congenital heart diseases (CHD) are still the leading cause of morbidity and mortality worldwide, there is a need for a robust and reliable cardiac model. Cardiac organoids are complex, three-dimensional cellular constructs that recapitulate the processes of the human embryonic heart. However, certain vital morphological features within the fetus are not yet replicable with cardiac organoids. Here we report our investigation to generate cardiac organoids with chamber formations. Our method involves modulating the Wnt pathway at two different instances while also implementing two cell seeding densities, all to determine the most optimized that to produce chamber formations within cardiac organoids.

As a large generation ages, the collective financial and ethical responsibility to prevent egregious bodily harm through fall prevention and gait assistant exoskeleton devices increases. Risk for falls increases with age and the severity of the fall does as well. To support this elderly population, motorized exoskeletons can both increase stability as well as respond faster to fall scenarios, but current models do not more around the existing biological framework. Giving participants a range of motion in key pelvic areas can closely approximate synchronous rotation around the femoral head, while limiting an increase in their sagittal profile. Utilizing 3D printed components while incorporating existing orthic methods provide short production times on modular designs. Although primarily mechanically based, these designs consider electronic requirements and are capable for supporting movement for a 200 lbs. user at a brisk walking pace for 1 hour.

The Emotiv Insight is a commercially available, low-cost, mobile EEG device that is commonly overshadowed by its costlier counterpart, the Emotiv EPOC. The purpose of this report is to investigate if the Emotiv Insight is a suitable headset to be used as a controlling factor in conjunction with an Arduino microcontroller and various electrical components that are used towards the development of an open-sourced, affordable electric wheelchair with the primary goal of providing those who either do not have the financial resources or the physical capability to operate a traditional wheelchair due to their disability a viable option to improve their quality of life. All of the C++ code, STL files used to fabricate the 3d-printed components are uploaded to a GitHub repository as open sourced files to allow individuals with access to a 3d-printer to either build the open sourced wheelchair for their personal use, or refine the design to suit their needs.

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.

Members of the elderly population is disproportionately prone to experiencing mobility impairment due to their aging bodies and as a result have frail bodies that are at a higher risk of grave injury due to falling. In order to combat this assistive mobility devices such as exoskeletons have been developed to help patients enhance their range of motion. With additive manufacturing techniques, such as fused deposition modeling (FDM), becoming a more mainstream form of design, the inclusion of lightweight polymers such as nylon 12 as primary construction materials for these devices has increased. In this thesis computer aided design (CAD) software was used to design a prototype lower body exoskeleton and simulation software was used to give the device the characteristics of Stratasys' nylon 12 carbon fiber FDM material to verify it if could be used as the primary construction material for this device when extruded from a FDM printer on either the XZ or ZX printing plane. From the simulations it was found that the material printed along the XZ plane could create a device that could withstand the weight of an average elderly male patient (200 lbs.) as well as the 35 lbs. of force applied to the …

In this research, we aimed to develop thin-film devices on a polymer substrate and an alternative 3D-printed device with macroelectrodes for treating gastrointestinal (GI) conditions. First, the fabrication of thin-film devices was demonstrated on a softening thiol-ene/acrylate polymer utilizing titanium nitride (TiN) as electrode material. This was achieved by utilizing cleanroom fabrication processes such as photolithography, wet and dry etching. The functionality of the device was shown by performing electrochemical characterization tests, mainly cyclic voltammetry, electrochemical impedance spectroscopy, and voltage transient. We synthesized a novel thiol-ene/acrylate polymer based on 1,3,5-triallyl-1,3,5-triazine-2,4,6(1H,3H,5H)-trione (TATATO), trimethylolpropanetris (3-mercaptopropionate) (TMTMP), and polyethylene glycol diacrylate (PEGDA). We show that this stretchable shape memory polymer substrate is well suited for cleanroom processes. Finally, for the high throughput of the wearable devices with electrodes size 10 mm in diameter, we implemented single electrode fabrication using printed circuit boards (PCBs) and depositing gold (Au) and TiN on the plated side of PCBs utilizing the sputtering tool. This step was followed by the assembly of those single electrodes on the flexible 3D printed device. We showed that the TiN electrode material performed better in terms of charge storage capacity and charge injection capacity than the widely used stainless steel electrode material …