Medicine and Health Sciences Commons^™

Peripheral Blood Mononuclear Cell Mitochondrial Dysfunction In Acute Alcohol-Associated Hepatitis, Annette Bellar, Nicole Welch, Jaividhya Dasarathy, Amy Attaway, Ryan Musich, Avinash Kumar, Jinendiran Sekar, Saurabh Mishra, Yana I. Sandlers, Et. Al

Chemistry Faculty Publications

Background: Patients with acute alcohol-associated hepatitis (AH) have immune dysfunction. Mitochondrial function is critical for immune cell responses and regulates senescence. Clinical translational studies using complementary bioinformatics-experimental validation of mitochondrial responses were performed in peripheral blood mononuclear cells (PBMC) from patients with AH, healthy controls (HC), and heavy drinkers without evidence of liver disease (HD).
Methods: Feature extraction for differentially expressed genes (DEG) in mitochondrial components and telomere regulatory pathways from single-cell RNAseq (scRNAseq) and integrated 'pseudobulk' transcriptomics from PBMC from AH and HC (n = 4 each) were performed. After optimising isolation and processing protocols for functional studies in …

Data Science For Hospital Antibiotic Stewardship, Saikou Jawla

Theses and Dissertations

Antibiotics are widely used to treat bacterial infections, but their misuse leads to antibiotic resistance. Antibiotic resistance is one of the biggest threats to global health, food security, and development today. Antibiotic resistance leads to higher medical costs, prolonged hospital stays, and increased mortality. Antimicrobial stewardship is an approach to measure and improve the appropriate use of antibiotics in healthcare settings. Data science has the potential to support these programs by providing insights into antibiotic prescribing patterns, identifying areas for improvement, and predicting patient outcomes. We explored the role of data science in hospital antibiotic stewardship programs, including statistical methods …

Modeling Antihypertensive Therapeutic Inertia And Intensification To Support Clinical Action Toward Hypertension Control, Benjamin Martin

All Dissertations

Background

Hypertension is the leading modifiable risk factor for cardiovascular disease and consequent mortality worldwide. In the U.S., more than half of hypertension cases remain uncontrolled, despite availability of effective pharmaceutical treatment options. Evidence suggests that therapeutic inertia, defined as clinician failure to initiate or increase therapy when treatment goals are unmet, is the most influential barrier to improving hypertension control. Substantial rates of therapeutic inertia have been reported in ambulatory primary care settings where hypertension is typically treated and managed. Understanding and overcoming the forces driving therapeutic inertia in hypertension management is a critical strategy to reach population health …

Online Dashboards For Sars-Cov-2 Wastewater Data Need Standard Best Practices: An Environmental Health Communication Agenda, Colleen C. Naughton, Rochelle H. Holm, Nancy J. Lin, Brooklyn P. James, Ted Smith

Faculty Scholarship

The COVID-19 pandemic has highlighted the benefits of wastewater surveillance to supplement clinical data. Numerous online information dashboards have been rapidly, and typically independently, developed to communicate environmental surveillance data to public health officials and the public. In this study, we review dashboards presenting SARS-CoV-2 wastewater data and propose a path toward harmonization and improved risk communication. A list of 127 dashboards representing 27 countries was compiled. The variability was high and encompassed aspects including the graphics used for data presentation (e.g., line/bar graphs, maps, and tables), log versus linear scale, and 96 separate ways of labeling SARS-CoV-2 wastewater concentrations. …

Enhanced Iot-Based Electrocardiogram Monitoring System With Deep Learning, Jian Ni

UNLV Theses, Dissertations, Professional Papers, and Capstones

Due to the rapid development of computing and sensing technologies, Internet of Things (IoT)-based cardiac monitoring plays a crucial role in providing patients with cost-efficient solutions for long-term, continuous, and pervasive electrocardiogram (ECG) monitoring outside a hospital setting. In a typical IoT-based ECG monitoring system, ECG signals are picked up by sensors located on the edge, and then uploaded to the remote cloud servers. ECG interpretation is performed for the collected ECGs in the cloud servers and the analysis results can be made instantly available to the patients as well as their healthcare providers.In this dissertation, we first examine the …

Synthesis, Radiolabeling And Evaluation Of A Suite Of Tracers With 44Sc For Detecting Extracellular Dna, Zhiyao Li

McKelvey School of Engineering Theses & Dissertations

Neutrophil extracellular traps involve the rapid translocation of DNA to the outside of the cell under certain stimuli. This structure forms a fibrous network that is able to limit the spread of pathogens and to kill microorganisms. It has also been shown to be present in various pathological processes such as inflammation, autoimmune diseases, and cancer metastasis. Currently, the formation process of NETs in vivo is being extensively studied. However noninvasive detection and quantitation has yet to be achieved. A class of PET tracers are described here that consists of a DNA dye as the backbone that is labeled with …

Anxiety-Like Behavior In C57bl/6j Mice Is Sexually Dimorphic And Altered By Buprenorphine, Ohm Sharma

Chancellor’s Honors Program Projects

No abstract provided.

Design And Synthesis Of Peripherally Selective Endocannabinoid Enzyme Inhibitors For Ocular Indications, Kezia Reji Thomas

Senior Honors Theses

Peripherally selective compounds have been found to stimulate endocannabinoid receptor activity, which has been observed to have positive physiological effects such as ocular wound healing and inflammation control. The activation of the cannabinoid 1 receptor via binding of the endogenous ligands, anandamide and 2-arachidonoylglycerol, has been indicated to elicit these effects. Both ligands are controlled by two hydrolase enzymes, fatty acid amide hydrolase (FAAH) and monoacylglycerol lipase (MAGL), which can be targeted for therapeutic inhibition. Sulfonamide derivatives of JZL195 containing carbamate functionalities in the southern region of the inhibitor compounds were produced using novel carbamate exchange reactions. Polar functionalities were …

Development Of Surface-Modified Liposomes For Drug Delivery Applications, Megan Louise Qualls

Doctoral Dissertations

Liposomes are spherical vesicles composed of a lipid bilayer membrane that assembles around an internal aqueous core. This duality gives liposomes the ability to encapsulate both hydrophobic cargo within the lipid bilayer and hydrophilic cargo in the aqueous core, making them versatile molecular carriers for drug delivery. Liposome platforms have many advantages and are promising drug delivery carriers, and research is ongoing to improve their designs for continued clinical applications. Many liposome types have been developed, but further work is needed to improve surface modification, site-specific targeting, and triggered cargo release in order to further the therapeutic applications of these …

A Machine Learning Approach For Predicting Clinical Trial Patient Enrollment In Drug Development Portfolio Demand Planning, Ahmed Shoieb

Masters Theses

One of the biggest challenges the clinical research industry currently faces is the accurate forecasting of patient enrollment (namely if and when a clinical trial will achieve full enrollment), as the stochastic behavior of enrollment can significantly contribute to delays in the development of new drugs, increases in duration and costs of clinical trials, and the over- or under- estimation of clinical supply. This study proposes a Machine Learning model using a Fully Convolutional Network (FCN) that is trained on a dataset of 100,000 patient enrollment data points including patient age, patient gender, patient disease, investigational product, study phase, blinded …

Inaugural Artificial Intelligence For Public Health Practice (Ai4php) Retreat: Ontario, Canada, Jacqueline K. Kueper, Laura C. Rosella, Richard G. Booth, Brent D. Davis, Sarah Nayani, Maxwell J. Smith, Dan Lizotte

Computer Science Publications

The Artificial Intelligence (AI) for Public Health Practice Retreat was a hybrid event held in October 2022 in London, Ontario to achieve three main goals: 1) Identify both the goals of public health practitioners and the tasks that they undertake as part of their practice to achieve those goals that could be supported by AI, 2) Learn from existing examples and the experience of others about facilitators and barriers to AI for public health, and 3) Support new and strengthen existing connections between public health practitioners and AI researchers. The retreat included a keynote presentation, group brainstorming exercises, breakout group …

Head And Neck Tumor Histopathological Image Representation With Pre- Trained Convolutional Neural Network And Vision Transformer, Ranny Rahaningrum Herdiantoputri, Daisuke Komura, Tohru Ikeda, Shumpei Ishikawa

Journal of Dentistry Indonesia

Image representation via machine learning is an approach to quantitatively represent histopathological images of head and neck tumors for future applications of artificial intelligence-assisted pathological diagnosis systems. Objective: This study compares image representations produced by a pre-trained convolutional neural network (VGG16) to those produced by a vision transformer (ViT-L/14) in terms of the classification performance of head and neck tumors. Methods: W hole-slide images of five oral t umor categories (n = 319 cases) were analyzed. Image patches were created from manually annotated regions at 4096, 2048, and 1024 pixels and rescaled to 256 pixels. Image representations were …

On Cox Proportional Hazards Model Performance Under Different Sampling Schemes, Hani Samawi, Lili Yu, Jingjing Yin

Department of Biostatistics, Epidemiology, and Environmental Health Sciences Faculty Publications

Cox’s proportional hazards model (PH) is an acceptable model for survival data analysis. This work investigates PH models’ performance under different efficient sampling schemes for analyzing time to event data (survival data). We will compare a modified Extreme, and Double Extreme Ranked Set Sampling (ERSS, and DERSS) schemes with a simple random sampling scheme. Observations are assumed to be selected based on an easy-to-evaluate baseline available variable associated with the survival time. Through intensive simulations, we show that these modified approaches (ERSS and DERSS) provide more powerful testing procedures and more efficient estimates of hazard ratio than those based on …

Employee Attrition: Analyzing Factors Influencing Job Satisfaction Of Ibm Data Scientists, Graham Nash

Symposium of Student Scholars

Employee attrition is a relevant issue that every business employer must consider when gauging the effectiveness of their employees. Whether or not an employee chooses to leave their job can come from a multitude of factors. As a result, employers need to develop methods in which they can measure attrition by calculating the several qualities of their employees. Factors like their age, years with the company, which department they work in, their level of education, their job role, and even their marital status are all considered by employers to assist in predicting employee attrition. This project will be analyzing a …

The Impact Of Virtual Reality On The Healthcare Industry, Peter Sullivan

Honors Projects in Information Systems and Analytics

Virtual reality (VR) took off in 2013 and has touched many public sectors, from gaming, to business, to healthcare. This study looks at virtual reality's impact has affected the healthcare system, with a focus on its use for medical training, patient recovery, patient pain management, and mental health care. A literature review was conducted on the current state of the industry addressing virtual reality's performance in the field, the perception of experts, and an estimation of financial undertakings. Looking at cost analyses brought a fuller approach to the research. Surveying researchers and workers within the realm of healthcare and VR …

Scholars Day 2023 Program Of Events, Carl Goodson Honors Program

Scholars Day

This is the program of events for the 2023 Scholars Day Conference, where undergraduates across disciplines present their scholarly and creative works.

From Deep Mutational Mapping Of Allosteric Protein Landscapes To Deep Learning Of Allostery And Hidden Allosteric Sites: Zooming In On “Allosteric Intersection” Of Biochemical And Big Data Approaches, Gennady M. Verkhivker, Mohammed Alshahrani, Grace Gupta, Sian Xiao, Peng Tao

Mathematics, Physics, and Computer Science Faculty Articles and Research

The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are able to model complex chemical and biological phenomena. In recent years, machine learning approaches have been developed and actively deployed to facilitate computational and experimental studies of protein dynamics and allosteric mechanisms. In this review, we discuss in detail new developments along two major directions of allosteric research through the lens of data-intensive biochemical approaches and AI-based computational methods. Despite considerable progress in applications of AI methods for protein structure and dynamics studies, the intersection between allosteric …

Hipaa Vs. Medical Research: Improving Patient Care Through Integration Of Data Privacy And Data Access, Katherine D'Ordine

Honors Projects in Data Science

The purpose of this research is to understand the current relationship between data access and data privacy in the health care industry and attempt to find a way that important health care research can still be conducted amidst HIPAA regulations. There is a lack of extensive research on the impacts of data privacy on health care research due to access regulations, so a survey was created regarding current data processes and recommendations for creating a healthier relationship between privacy and access for research. It was distributed to anyone in health care, analytics, or research to get a variety of perspectives. …

A Pharmacoepidemiological Study Of Myocarditis And Pericarditis Following The First Dose Of Mrna Covid-19 Vaccine In Europe, Joana Tome, Logan Cowan, Isaac Fung

Department of Biostatistics, Epidemiology, and Environmental Health Sciences Faculty Publications

This study assessed the myocarditis and pericarditis reporting rate of the first dose of mRNA COVID-19 vaccines in Europe. Myocarditis and pericarditis data pertinent to mRNA COVID19 vaccines (1 January 2021–11 February 2022) from EudraVigilance database were combined with European Centre for Disease Prevention and Control (ECDC)’s vaccination tracker data. The reporting rate was expressed as events (occurring within 28 days of the first dose) per 1 million individuals vaccinated. An observed-to-expected (OE) analysis quantified excess risk for myocarditis or pericarditis following the first mRNA COVID-19 vaccination. The reporting rate of myocarditis per 1 million individuals vaccinated was 17.27 (95% …

Open Data Indicates That Collegedale Could Be A Bluezone, Tristan Deschamps, Alva Johnson

Campus Research Day

A blue zone is an indicator of exceptional health in a community. Adventists have a blue zone community in Loma Linda, but there has been little research into other Adventist populated areas that could be blue zones. Therefore, our goal is to show that open data suggests that a blue zone may exist near Southern Adventist University, specifically in Collegedale. This data has been gathered from different federal sources, including, the CDC, the US Census Bureau, the Tennessee Department of Health, official state records, and federal documents that are available to the public.

Interactive Dashboard Of Diabetes In The Us, Marc Butler

Campus Research Day

The contribution of this research project is the construction and interactive dashboard in order to facilitate the visualization of diabetes-related data to the public

Slide Preparation For Dna Attachment For Use In Optical Tweezers, Dylan J. Kirkeby, Karissa Carlson, Emily Grace, Eric Jones

Celebration of Research

Our research team, ISLAND CURE, is a multidisciplinary team of professors and undergraduate students with the goal to design and build instruments to make biological measurements on a limited budget. One of the apparatuses we are designing, is optical tweezers, which are a Nobel Prize-winning technology capable of trapping microscopic and sub-microscopic particles using a laser beam. Using a 1064 nm beam, we will trap a single strand of DNA using beads and this will enable us to exert minute forces upon the DNA. This experiment will give us a better understanding of the forces on damaged DNA; specifically, the …

Enhancement Of Deep Learning Protein Structure Prediction, Ruoming Shen

Modeling, Simulation and Visualization Student Capstone Conference

Protein modeling is a rapidly expanding field with valuable applications in the pharmaceutical industry. Accurate protein structure prediction facilitates drug design, as extensive knowledge about the atomic structure of a given protein enables scientists to target that protein in the human body. However, protein structure identification in certain types of protein images remains challenging, with medium resolution cryogenic electron microscopy (cryo-EM) protein density maps particularly difficult to analyze. Recent advancements in computational methods, namely deep learning, have improved protein modeling. To maximize its accuracy, a deep learning model requires copious amounts of up-to-date training data.

This project explores DeepSSETracer, a …

Review Of Nighttime Temperature Effects On Long-Term Health Condition Through Sleep Studies, Sydnie Matkins

Modeling, Simulation and Visualization Student Capstone Conference

Over the past 40 years, there has been increasing interest in human sleep quality and duration. This nonsystematic review looked at over 80 peer-reviewed papers on the association among sleep, temperature, and long-term health conditions. Generally, warmer temperatures lend to poorer sleep quality, and poor sleep quality lend to mental illness and a higher risk of coronary heart disease and mortality. Future research should be to conduct a study that relies more on health records rather than questionnaires to accurately map current and future health quality.

From Policy Promotion To Research Output: Brief Analysis Of Technical Challenges Of Hospital-Led Artificial Intelligence Research, Yu Zhuang, Cheng Zhou

Bulletin of Chinese Academy of Sciences (Chinese Version)

In recent years, artificial intelligence has become a key direction of medical and health-related research and a hot spot of international competition. In order to investigate the current situation and challenges in hospital-led artificial intelligence researched, this study selects 14 national pilot hospitals to promote the high-quality development of public hospitals as samples, adopts a combination of quantitative and qualitative methods, analyzes the research articles related to artificial intelligence published by the sample hospitals in recent years, and analyzes the technical challenges in the hospital-led artificial intelligence research. The results show that although the number of hospital-led artificial intelligence research …

Computational Design And Molecular Modeling Of Morphine Derivatives For Preferential Binding In Inflamed Tissue, Makena Augenstein, Nayiri Alexander, Matthew Gartner

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

The opioid epidemic has impacted over 10 million Americans in 2019. Opioids, like morphine, bind non-selectively in both peripheral tissue, leading to effective pain relief, and central tissue, resulting in dangerous side effects and addiction. The inflamed conditions of injured tissues have a lower pH (pH = 6–6.5) environment than healthy tissue (pH = 7.4). We aim to design a morphine derivative that binds selectively within inflamed tissue using molecular extension and dissection techniques. Morphine binds to the μ-opioid receptor (MOR) when the biochemically active amine group is protonated. Fluorination of a β-carbon from the tertiary amine group led to …

The Role Of Machine Learning In Improved Functionality Of Lower Limb Prostheses, Joaquin Dominguez, Richard Kim, Robert Slater

SMU Data Science Review

Lower-limb amputations can cause a plethora of obstacles that lead to a lower quality of life. Implementing machine learning techniques means advanced prosthetics can contribute to facilitating the lives of those that live with lower-limb amputations. Using the publicly available HuGaDB data set, the current study investigates several classification models (random forest, neural network, and Vowpal Wabbit) to predict the locomotive intentions of individuals using lower-limb prostheses. The results of this study show that the neural network model yielded the highest accuracy, comparable precision, and recall scores to the other models. However, the Vowpal Wabbit model's advantage in speed may …

Volume 14, Ireland Seagle, Dalton C. Whitby, Cassandra Poole, Rachel Cannon, Heidi Parker-Combes, Devon G. Shifflett, Antonio Harvey

Incite: The Journal of Undergraduate Scholarship

Table of Contents:

• Introduction: Dr. Amorette Barber
• From the Editor: Dr. Larissa "Kat" Tracy
• From the Designers: Rachel English, Rachel Hanson
• Hungry Like the Wolf: The Wolf as Metaphor in Paramount Network’s Yellowstone: Ireland Seagle
• “Floating Cities”: Illustrating the Commercial and Conservation Conflict of Alaskan Cruise Ship Tourism: Dalton C. Whitby
• What Can You Do When Your Genes are the Enemy? Current Applications of Gene Manipulation and the Associated Ethical Considerations: Cassandra Poole
• La doble cara: un tema romántico en las obras de Larra y Hawthorne: Rachel Cannon
• Resolving a Conflict: How to …

The Compound Risk Of Heat And Covid-19 In New York City: Riskscapes, Physical And Social Factors, And Interventions, Janelle Knox-Hayes, Juan Camilo Osorio, Natasha Stamler, Maria Dombrov, Rose Winer, Mary Hannah Smith, Reginald Blake, Cynthia Rosenzweig

Publications and Research

Climate change is disrupting the fundamental conditions of human life and exacerbating existing inequity by placing further burdens on communities that are already vulnerable. Risk exposure varies by where people live and work. In this article, we examine the spatial overlap of the compound risks of COVID-19 and extreme heat in New York City. We assess the relationship between socio-demographic and natural, built and social environmental characteristics, and the spatial correspondence of COVID-19 daily case rates across three pandemic waves. We use these data to create a compound risk index combining heat, COVID-19, density and social vulnerability. Our findings demonstrate …

Knowledge, Attitude, And Behavioral Intention About Oral Cancer Among Public Health Students In Southeast Georgia, Ravneet Kaur, Gulzar H. Shah

Department of Biostatistics, Epidemiology, and Environmental Health Sciences Faculty Publications

Background: Oral cancer (OC) is a significant public health problem; however, the degree to which the future public health workforce is aware of this issue is not well researched. The purpose of this study is to explore the level of knowledge, attitudes, and behavioral intentions about OC among public health students.
Materials and Methods: A sequential exploratory mixed-method research design was employed for this study. Using quantitative and qualitative measures, a survey was administered to 129 public health students. Subsequently, to understand the quantitative findings, two follow-up focus groups were conducted with survey participants.
Results: We found …