Health Sciences – Page 11 – UROP Spring Symposium 2021

Health Sciences

A Systematic Examination of Race in Health-Focused Human-Computer Interaction Research

The human-computer interaction (HCI) community has a long tradition of health-related research. Epidemiological and public health research has revealed widespread racial disparities in healthcare. We conducted a systematic review of HCI research on race and healthcare, to identify common themes and gaps within health-related HCI research. Beginning with an initial set of 418 articles drawn from two major HCI venues, we applied a set of exclusion criteria resulting in an eventual dataset of 24 articles. We conducted a thematic analysis, with a special focus on examining how race is understood and operationalized. We found considerable variation in definitions of race across articles, with some focusing on skin color, others on socio-cultural differences, and still others not providing any explicit definition of race. This variation was further reflected in common research practices such as the method used to identify participants’ race and the level of specificity used in categorizing participants’ race and ethnicity. We also found that some articles posed racial issues as an area of future work, without including them in their current investigation. We discuss implications for health-related HCI research, including the need for race-consciousness in research design and for specificity in defining race.

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Increasing Diversity in the Medical Workforce

Despite evidence that having a diverse physician workforce has a multitude of benefits, it is still not representative of the total United States population. Evidence supports the conclusion that minority doctors are more likely to work in underrepresented minority communities in need of healthcare expansion, as well as aiding in the reduction of health-related disparities (Ibrahim 2019). This study examines what influences minorities’ decision to continue or leave the pipeline to medicine, in hopes of identifying potential barriers. In order to better understand the issue, preliminary research was conducted to find out current statistics about underrepresented minorities in medicine. After examining past research, we formulated our own hypotheses of potential barriers including inadequate mentorship, an unstable educational foundation, and hostile learning climates in post-secondary education, and used these hypotheses to draft focus group questions. We will host interviews so that students of color can discuss and share their experiences pursuing the pipeline to medicine. We anticipate that the data collected will support our hypothesis that foundational issues and lack of support have impacted retention rates of medically-interested students. Based on that data, we plan on drafting a survey that will allow us to garner more responses. After the conclusion of this data collection process, we hope to draft a manuscript that could be used by medical institutions in order to understand how to recruit and retain higher numbers of underrepresented minority students.

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Application for Cardiac Signal Visualization and Annotation

Massive amounts of data are being generated at a high velocity in the medical industry, at a rate and dimension that humans cannot catch up understanding them. Machine learning (ML) has the potential to analyze the medical time series data to support physicians in clinical decision making. However, in order to build useful machine learning (ML) algorithms, it is important to have annotated medical data set that can be used to train ML models. This project at the start is about building a viewer for surface ECG and intracardiac electrogram signals acquired during cardiac ablation procedures. Building the framework at early stages to support basic functionality is engineering-oriented with design decisions. We are currently developing a web application in Python. Specifications were created based upon the needed functionality of the application after talking with multiple cardiologists. The next step would be inviting medical students and cardiologists to use the app and gather feedback. Eventually, we want to build a platform/infrastructure that allows for development and evaluation of ML algorithms, and to improve cardiovascular disease management and treatment. By working with cardiologists and intelligent algorithms, we want to build a tool that can quickly review, search, annotate and analyze signals at high throughput. The end goal is that researchers across the country can use our tool to review their own data, and also deploy a real-time graphical decision support tool to assist cardiac procedures.

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Whole Health Educational Resource Development and Evaluation for Veterans and VA Staff

The Veterans Association (VA) is one of the largest integrative healthcare systems in the country. Its success in patient outcomes is contributed to the widespread practice of Whole Health in the VA system. Whole Health is known as “Personalized, Proactive, Patient-driven Care” and it focuses on several areas of health and well-being while empowering the patient to take an active role in their healthcare alongside providers. The impact of Whole Health and use of its techniques and practices among veterans and patients who receive this healthcare is not widely known. This study aims to assess how many skills and practices from the Whole Health system are being used and applied by patients (Veterans) in their everyday lives. Specifically, questions are designed to reflect practices from the 8 modalities of Whole Health. To conduct our test, we created an online survey using Qualtrics and distributed it among colleagues, students, patients, and veterans in association with the VA Ann Arbor. We collected basic demographic information including sex, age range, and Veteran status and asked a series of questions designed to assess the practice of mindfulness and other techniques among respondents and also to assess how much of the Whole Health practice they have experienced. At the time of publication of this abstract, the data has been collected and preliminary analysis is in process. Specific, detailed, and final results will be available at the time of the Symposium presentation.

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Understanding Graduate Student Perspectives of Mental Health

Mental health challenges are rising in higher education across the United States. We investigate doctoral students’ experiences concerning mental health in academia and their perceptions of university responses to wellbeing concerns. Through 30 semi-structured interviews with students enrolled in all levels of the PhD, we find that while participants are aware of wellbeing resources offered by the university, their perceptions suggest the short-term nature of counseling is not fit for graduate students’ needs. Although participants from all backgrounds reported feeling anxiety and impostor syndrome on account of their academic experiences, such problems are exacerbated for members of minoritized groups, many of whom do not see themselves reflected in role models or the canon of literature in their field. We identify tensions among expectations of knowledge construction and faculty advising. Our findings have implications for stronger institutional relationships with external counselors, increased peer group support, and effective communication about doctoral expectations.

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Understanding Graduate Student Perspectives of Mental Health

Mental health challenges are rising in higher education across the United States. We investigate doctoral students’ experiences concerning mental health in academia and their perceptions of university responses to wellbeing concerns. Through 30 semi-structured interviews with students enrolled in all levels of the PhD, we find that while participants are aware of wellbeing resources offered by the university, their perceptions suggest the short-term nature of counseling is not fit for graduate students’ needs. Although participants from all backgrounds reported feeling anxiety and impostor syndrome on account of their academic experiences, such problems are exacerbated for members of minoritized groups, many of whom do not see themselves reflected in role models or the canon of literature in their field. We identify tensions among expectations of knowledge construction and faculty advising. Our findings have implications for stronger institutional relationships with external counselors, increased peer group support, and effective communication about doctoral expectations.

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Effects of Social Determinants of Health on Infant Mortality in Washtenaw and Wayne County

Infant mortality is the death of an infant within the first year of life, and this is a very useful indicator of population health. The United States has one of the highest infant mortality rates among developed countries, and while the reason for this remains unclear, it is hypothesized that social determinants of health play a large role. Social determinants of health are conditions in the lives of people that affect health risks and outcomes. It is still unclear how these determinants influence infant mortality, and which determinants have the most influence. In Michigan, the infant mortality rate as of 2018 was 6.8 deaths per 1,000 live births (Centers for Disease Control and Prevention (CDC), 2018). This puts Michigan high on the spectrum within the United States, and just as these rates differ by country, they also differ by state and even county. It was hypothesized that by studying two counties with different infant mortality rates, the difference could be attributed to the social determinants of health that vary among these populations. Therefore, if it can be determined that particular determinants have a more significant influence on infant mortality rates, then vulnerable populations can be more easily identified, and implementation efforts can be better catered to these populations and their disadvantages. Infant mortality data were collected from the Michigan Department of Health and Human Services from 2010-2018 for both Washtenaw and Wayne County, Michigan. Additional data were collected from the United States Census Bureau from 2010-2018 regarding social determinants such as poverty rate, unemployment rate, uninsured rate, race, and education level for both counties. After performing a series of calculations including linear regressions and logistic regression curves to find odds ratios, no correlation was found between the infant mortality rates and any of the determinants. Therefore, it can be concluded that one social determinant of health is unlikely to be a good predictor of infant outcomes. Instead, high infant mortality rates are likely a result of interactions between several determinants that, together, increase an infant’s risk. Therefore, simultaneous targeting of multiple determinants is necessary to implement meaningful interventions.

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Roles of Migratory Neural Crest Cells in CHARGE Syndrome

CHARGE Syndrome is characterized by a number of congenital abnormalities (Coloboma of the eye, Heart defects, Atresia of the choanae, Retardation of growth, Genital abnormalities, and Ear abnormalities). The primary cause of CHARGE is mutations of the gene CHD7 (Chromodomain Helicase DNA binding protein 7). One of the main hallmarks of CHARGE is deafness, but the etiology of hearing loss in CHARGE is unknown. Defects in neural crest derived tissue has been linked to other aspects of CHARGE syndrome. Here we look at the connection between CHD7 mutations and their effect on neural crest cell migration in the development of the inner ear. We used a neural crest specific CHD7 knockout mouse line (Wnt1Cre2;Chd7flox/flox) to observe the migration of neural crest cells in tissue sections. We found that CHD7 loss in neural crest cells does not impair their migration to the developing inner ear. Work is ongoing to determine if the development of neural crest-derived glial cells is impaired in the Chd7Gt/+ mouse model of CHARGE syndrome. Collectively, these studies improve our understanding of CHD7 in neural crest development in CHARGE syndrome.

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Imaging of the effects of GLP-1 on pancreatic ilets (morphometry, in vivo imaging…)

GLP-1 stands for glucagon-like protein-1. The GLP-1 is an incretin mostly secreted by intestinal epithelial endocrine L-cells, which are the cells lining the inside of the large intestine. It is secreted into the bloodstream when a meal is eaten. GLP-1 travels through the bloodstream to influence many different organs in our body such as our brain, liver, and pancreas. This research focuses on how GLP-1 affects islets in the pancreas. Since the GLP-1 protein has the ability to decrease blood sugar levels by promoting the production of insulin, the protein has been a topic of interest for pharmacological research. GLP-1 is currently being used in treatments for type 2 diabetes. The effects of the GLP-1 protein are known in adults, however, nothing is known about GLP-1 influence during development. This project investigates the effects of the GLP-1 protein on islets in the pancreas during development. We hypothesize that mice without GLP-1 receptors will have less islet cell mass and less proliferation. If there is no difference between pups with GLP-1 receptor and without the GLP-1 receptor, we expect to see no difference in mass nor proliferation of cells. Not much research has been done on the effects of GLP-1 during embryonic development, which is what this project is attempting to uncover. Further exploring this protein’s effect on islet activity in developing pups can supply the field with more important information.

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Mimicking the Architecture and Modulus of Native Brain Tissue onto Neural Implants to Improve Biocompatibility

The objective of this project is to increase longevity of microelectrodes, identify biomarkers to isolate the cause of neuroinflammation, and to analyze large banks of collected data using machine learning Matlab scripts. The data used for this project is collected from two groups of mice, wild type and CD14 knockout (mice with the CD14 gene repressed). Surgeries were conducted on both groups and data was collected two weeks after. Matlab scripts utilized machine learning to analyze the data and isolate patterns of unusual fold changes compared to set upper and lower standards (ie. 0.01 and 1). The scripts utilized to identify biomarkers are unique to this lab and the theory behind them have broad possible applications for other data analysis based on an initial condition to separate data with specific trends. Specific to our research, we find patterns in the up and down regulated genes and compare the mean fold change between different data groups. This project is a continuous work in progress – our goal is to continue to further narrow down the gene targets and identify more specific biomarkers to better target with therapeutic drugs. With successful identification, we hope to be able to decrease the inflammatory reaction due to insertion and increase the efficiency of intracortical microelectrodes. Developing a way to stop or mitigate the inflammatory response would increase the lifespan of devices that rely on the recording capabilities of microelectrodes. From prosthetic devices to increasing the understanding of the human brain, a decrease in the inflammatory response creates a longer period of time for a stable signal, meaning more opportunities for microelectrode applications.

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