This thesis project was inspired by healthcare disparities between rural and urban communities. This topic was operationalized in this paper through a comparison of dental care access in rural and urban areas of both Panama and Arizona. The significance and origin of the research were first introduced, followed by a literature review to establish baseline care access information in each of the locations. The methods of the research conducted include on-site observational data collection as well as patient interviews and conversations summarized by thematic analysis. Data showed different primary barriers to care in each location with an emphasis on geographic and policy variables. The results were discussed in terms of the factors identified as contributing to the disparities in access between the rural and urban communities in each location, and further, through a comparison of these disparities and their root causes, between Panama and Arizona. Recommendations were detailed for steps to continue this study and expand its scope, as well as suggested measures to minimize the access disparities faced by rural communities.

Stroke survivors must overcome motor deficits that greatly impede their balancing ability, thus stunting their independence and overall quality of life. Robot-aided rehabilitation is a new approach to balance therapy presenting notable advantages in efficiency, precision, and consistency. Prior studies have demonstrated the success of visual feedback, force plates, surface perturbations, and compliant surfaces in improving balance control for people with neuromuscular disorders. However, this study is the first to investigate the effect of incorporating each aspect into a stroke balance training program. The side-specific robotic platforms used could generate perturbations while also simulating compliant surfaces. During the 6-week study, 2 subjects each completed 9, 2-hour long training sessions, along with a clinical pre- and post-assessment. Subjects utilized visual feedback of center of pressure and weight distribution to strive for successful balance, and the platforms perturbed if balance was maintained for a sufficient time period. To increase training difficulty, platform stiffness decreased with increased performance. Improvements in functional postural balance for both subjects were demonstrated by the Berg Balance Scale, Mini-BESTest, Timed 10-Meter Walk Test, and 5 Times Sit-to-Stand Test. Decreases in Time to Perturb and Time to Stabilize were suggestive of improved dynamic postural balance. Decreased platform stiffness indicated sustained improvements in increasingly challenging environments, and a 3-month follow up revealed retained functional balance improvements. These results demonstrate the effectiveness of patient-adaptive perturbation-based robotic training on compliant surfaces in improving postural balance for chronic stroke patients.

As the need for whole heart transplantation to treat heart failure grows faster than the supply, alternative methods are in increasing demand. Transplantation of cardiomyocytes to replace injured myocardium after MI has shown promise. Myocardium is notoriously ineffective at proliferating after switching from hyperplastic to hypertrophic growth. FOXM1 has been established as having a strong role in cell cycle regulation in cancerous tumors and cardiomyocytes, and these experiments show the relationship between FOXM1 and iPSC-derived cardiomyocyte proliferation and attempt to improve a treatment option for heart failure through manipulation of this gene. Our experiment concludes that FOXM1 knockout increases iPSC-CM cell proliferation, and can be further explored to better increase cardiomyocyte proliferation.

This thesis project explores the US healthcare landscape to describe and explain some of the defining trends and models in use today, and discusses how they might be improved. Specifically, it focuses on the areas of healthcare delivery and payment and aims to answer the question: “What does healthcare delivery and payment currently look like in the United States, and why isn’t it working?”

Transcription factors play a crucial role in gene expression regulation, directly interpreting the genome to influence cellular functions and processes. Understanding Transcriptional Regulatory Networks (TRNs) provides insights into gene expression dynamics and regulatory mechanisms, critical for comprehending biological processes and disease states. Through single-cell RNA sequencing (scRNA-seq), analyzing gene expression at single-cell resolution offers opportunities to elucidate cellular heterogeneity and expression at a higher resolution. This paper presents preliminary validation of scRegNet, a novel network inference pipeline designed for single-cell transcriptomic data analysis, integrating correlative and causal inference methods to construct TRNs. Here, the pipeline incorporates Pearson correlation and Granger causality testing along with a comprehensive TF-target gene database and trajectory inference algorithms. We demonstrate scRegNet's efficacy using a mouse pancreatic endocrinogenesis dataset, identifying key TFs associated with endocrine cell differentiation. Comparison with literature-based TFs validates scRegNet's accuracy, while novel TFs offer hypotheses for further experimental validation. Our results reveal benefits of integrating multiple causal inference methods and trajectory analyses for robust TRN inference across unique datasets. This study highlights scRegNet's potential as a versatile tool for deciphering gene regulatory mechanisms in diverse biological contexts, paving the way for future applications in single-cell transcriptomic research.

Artificial intelligence (AI) and machine learning (ML) algorithms are revolutionizing the field of healthcare by offering new opportunities for improved diagnosis and treatment planning. These technologies have the potential to transform the way medical professionals approach patient care by analyzing vast amounts of data, identifying patterns, and making predictions. This overview highlights the current state of research and development in the field of AI and ML for diagnosis and treatment planning, as well as explore the ethical benefits and challenges associated with their implementation.