Diseases Commons^™

B31: Identifying New G Protein Coupled Receptor Kinase 2 And 3 Substrates Among Proteins Closely Linked To Breast Cancer With Positive Prognosis, Theresa Tran

Annual Research Symposium

No abstract provided.

C17: Programmed Cell Death Ligand- 1 Expression As A Prognostic Indicator In Oral Squamous Cell Carcinoma, Dr. Samar Saeed Khan

Annual Research Symposium

No abstract provided.

Predicting Tumor Response To Radiotherapy Based On Estimation Of Non-Treatment Parameters, Yutian Huang, Allison L. Lewis

Spora: A Journal of Biomathematics

Though clinicians can now collect detailed information about a variety of tumor characteristics as a tumor evolves, it remains difficult to predict the efficacy of a given treatment prior to administration. Additionally, the process of data collection may be invasive and expensive. Thus, the creation of a framework for predicting patient response to treatment using only information collected prior to the start of treatment could be invaluable. In this study, we employ ordinary differential equation models for tumor growth and utilize synthetic data from a cellular automaton model for calibration. We investigate which parameters have the most influence upon treatment …

A University Forest Fire: Examining The Spread Of The Coronavirus Through College Social Networks Using A Modified Forest Fire Probabilistic Model, Raechel Griffin

Senior Honors Projects

No abstract provided.

An Analysis Of The Interpretability Of Neural Networks Trained On Magnetic Resonance Imaging For Stroke Outcome Prediction, Esra Zihni, John D. Kelleher, Bryony Mcgarry

Conference papers

Applying deep learning models to MRI scans of acute stroke patients to extract features that are indicative of short-term outcome could assist a clinician’s treatment decisions. Deep learning models are usually accurate but are not easily interpretable. Here, we trained a convolutional neural network on ADC maps from hyperacute ischaemic stroke patients for prediction of short-term functional outcome and used an interpretability technique to highlight regions in the ADC maps that were most important in the prediction of a bad outcome. Although highly accurate, the model’s predictions were not based on aspects of the ADC maps related to stroke pathophysiology.

Using A Hybrid Agent-Based And Equation Based Model To Test School Closure Policies During A Measles Outbreak, Elizabeth Hunter, John D. Kelleher

Articles

Background

In order to be prepared for an infectious disease outbreak it is important to know what interventions will or will not have an impact on reducing the outbreak. While some interventions might have a greater effect in mitigating an outbreak, others might only have a minor effect but all interventions will have a cost in implementation. Estimating the effectiveness of an intervention can be done using computational modelling. In particular, comparing the results of model runs with an intervention in place to control runs where no interventions were used can help to determine what interventions will have the greatest …

Modeling Of Covid-19 Utilizing Various Compartmental Models To Predict Infection Rates Throughout Michigan, Colleen M. Staniszewski

Honors Theses

Compartmental modeling is a method of employing math to create a visual representation of a disease interacting with a select population, typically used in epidemiology analyses. This project applies compartmental modeling equations to data collected on the various aspects of COVID-19 in Michigan. Comparing current data to past predictive models, as well as the visual representations that were developed through the various compartmental modeling methods, allows an assessment of the effects of the preventative measures taken by the state, the various rates at which the infection is able to spread, as well as the potential path and spread of the …

Predictive Modeling And Estimation Of The Doubling Time Of Confirmed Cases Of Covid-19 In Niger, Ibrahim Sidi Zakari, Hadiza Galadima

Community & Environmental Health Faculty Publications

Modeling is increasingly used to assess scenarios and make projections on the future course of new coronavirus disease. This allows for better planning of care as well as a relaxation or tightening of the restrictive measures decreed by the government and the health authorities. The data analyzed in this study covers the period from March 19 to June 05, 2020 and allowed predictions of new cases of COVID-19 based on a growth model with a growth rate that changes linearly over time. In addition, we calculated and predicted the doubling time of the number of positive cases in each region …

Addressing Artificial Intelligence Bias In Retinal Diagnostics, Philippe Burlina, Neil Joshi, William Paul, Katia D Pacheco, Neil M Bressler

Faculty, Staff and Students Publications

PURPOSE: This study evaluated generative methods to potentially mitigate artificial intelligence (AI) bias when diagnosing diabetic retinopathy (DR) resulting from training data imbalance or domain generalization, which occurs when deep learning systems (DLSs) face concepts at test/inference time they were not initially trained on.

METHODS: The public domain Kaggle EyePACS dataset (88,692 fundi and 44,346 individuals, originally diverse for ethnicity) was modified by adding clinician-annotated labels and constructing an artificial scenario of data imbalance and domain generalization by disallowing training (but not testing) exemplars for images of retinas with DR warranting referral (DR-referable) from darker-skin individuals, who presumably have greater …

Long-Term Dynamics Of The Kidney Disease Epidemic Among Hiv-Infected Individuals, Heather Gudaz, Henry A. Ogu, Elissa J. Schwartz

Spora: A Journal of Biomathematics

One of many risks facing HIV+ individuals is the development of kidney dysfunction and end stage kidney disease (ESKD). A differential equation-based mathematical model was developed to assess the impact of antiretroviral therapy on the progression to kidney disease and on reducing mortality due to kidney failure. Analytical and numerical predictions of long-term HIV+ ESKD prevalence show that therapy can lead to either extremely low levels of disease prevalence or increased prevalence, depending on drug efficacy levels and mechanisms of action. Maintenance of HIV+ ESKD prevalence below one individual is possible with sufficient efficacy (e.g., 99%) against the progression from …

Regression Analyses Assessing The Impact Of Environmental Factors On Covid-19 Transmission And Mortality, El Hussain Shamsa, Kezhong Zhang

Medical Student Research Symposium

No abstract provided.

Distribution And Antibiotic Resistance Profiles Of Salmonella Enterica In Rural Areas Of North Carolina After Hurricane Florence In 2018, Yuqing Mao, Mohamed Zeineldin, Moiz Usmani, Sital Uprety, Joanna Shisler, Antarpreet Jutla, Avinash Unnikrishnan, Thanh H. Nguyen

Civil and Environmental Engineering Faculty Publications and Presentations

The frequency and magnitude of extreme events are increasing globally (Arnell & Gosling, 2016). Inundation, as a result of massive flooding, has the potential to change environmental conditions abruptly, and as a result, add pressure to the metabolism and proliferation of microorganisms (Furtak et al., 2020). The resulting overland flows and additional burden from domestic sewer and septic tank systems during an extreme flood event can introduce pathogens into ecologically unstable water bodies. For example, Yu et al. (2018) reported elevated levels of Escherichia coli and antibiotic resistance genes (ARGs) in river water samples 6 months after flooding in Houston, …

The Mechanism Of Β-N-Methylamino-L-Alanine Inhibition Of Trna Aminoacylation And Its Impact On Misincorporation, Nien-Ching Han, Tammy J. Bullwinkle, Kaeli F. Loeb, Kym F. Faull, Kyle Mohler, Jesse Rinehart, Michael Ibba

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

β-N-methylamino-l-alanine (BMAA) is a nonproteinogenic amino acid that has been associated with neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS) and Alzheimer's disease (AD). BMAA has been found in human protein extracts; however, the mechanism by which it enters the proteome is still unclear. It has been suggested that BMAA is misincorporated at serine codons during protein synthesis, but direct evidence of its cotranslational incorporation is currently lacking. Here, using LC-MS–purified BMAA and several biochemical assays, we sought to determine whether any aminoacyl-tRNA synthetase (aaRS) utilizes BMAA as a substrate for aminoacylation. Despite BMAA's previously predicted misincorporation at serine …

Co-Phosphorylation Networks Reveal Subtype-Specific Signaling Modules In Breast Cancer, Marzieh Ayati, Mark R. Chance, Mehmet Koyuturk

Computer Science Faculty Publications

Motivation Protein phosphorylation is a ubiquitous mechanism of post-ranslational modification that plays a central role in cellular signaling. Phosphorylation is particularly important in the context of cancer, as down-regulation of tumor suppressors and up-regulation of oncogenes by the dysregulation of associated kinase and phosphatase networks are shown to have key roles in tumor growth and progression. Despite recent advances that enable large-scale monitoring of protein phosphorylation, these data are not fully incorporated into such computational tasks as phenotyping and subtyping of cancers.

Results We develop a network-based algorithm, CoPPNet, to enable unsupervised subtyping of cancers using phosphorylation data. For this …

The U-Net-Based Active Learning Framework For Enhancing Cancer Immunotherapy, Vishwanshi Joshi

Theses, Dissertations and Capstones

Breast cancer is the most common cancer in the world. According to the U.S. Breast Cancer Statistics, about 281,000 new cases of invasive breast cancer are expected to be diagnosed in 2021 (Smith et al., 2019). The death rate of breast cancer is higher than any other cancer type. Early detection and treatment of breast cancer have been challenging over the last few decades. Meanwhile, deep learning algorithms using Convolutional Neural Networks to segment images have achieved considerable success in recent years. These algorithms have continued to assist in exploring the quantitative measurement of cancer cells in the tumor microenvironment. …

Maternal Proximity To Mountaintop Removal Mining And Birth Defects In Appalachian Kentucky, 1997-2003, Daniel B. Cooper

Theses and Dissertations--Public Health (M.P.H. & Dr.P.H.)

Background: Extraction of coal through mountaintop removal mining (MTR) alters many dimensions of the landscape, and explosive blasts, exposed rock, and coal washing have the potential to pollute air and water with substances known to increase risk of developmental and birth anomalies. Previous research suggests that infants born to mothers living in MTR coal mining counties have higher prevalence of most types of birth defects.

Objectives: This study seeks to examine further the relationship between MTR activity and birth defects by employing individual level exposure estimation through precise satellite data of MTR activity in the Appalachian region and maternal residence …

Physical Activity, Dietary Patterns, And Glycemic Management Of Active Individuals With Type 1 Diabetes: An Online Survey, Sheri Colberg, Jihan Kannane, Norou Diawara

Human Movement Studies & Special Education Faculty Publications

Individuals with type 1 diabetes (T1D) are able to balance their blood glucose levels while engaging in a wide variety of physical activities and sports. However, insulin use forces them to contend with many daily training and performance challenges involved with fine-tuning medication dosing, physical activity levels, and dietary patterns to optimize their participation and performance. The aim of this study was to ascertain which variables related to the diabetes management of physically active individuals with T1D have the greatest impact on overall blood glucose levels (reported as A1C) in a real-world setting. A total of 220 individuals with T1D …

Use Of Lymesim 2.0 To Assess The Potential For Single And Integrated Management Methods To Control Blacklegged Ticks (Ixodes Scapularis; Acari: Ixodidae) And Transmission Of Lyme Disease Spirochetes, Shravani Chitineni, Elizabeth R. Gleim, Holly D. Gaff

Undergraduate Honors Theses

Annual Lyme disease cases continue to rise in the U.S. making it the most reported vector-borne illness in the country. The pathogen (Borrelia burgdorferi) and primary vector (Ixodes scapularis; blacklegged tick) dynamics of Lyme disease are complicated by the multitude of vertebrate hosts and varying environmental factors, making models an ideal tool for exploring disease dynamics in a time- and cost-effective way. In the current study, LYMESIM 2.0, a mechanistic model, was used to explore the effectiveness of three commonly used tick control methods: habitat-targeted acaricide (spraying), rodent-targeted acaricide (bait boxes), and white-tailed deer targeted acaricide (4-poster …

Effect Of Mutation And Vaccination On Spread, Severity, And Mortality Of Covid-19 Disease, Dr Hossam Zawbaa, Hasnaa Osama, Ahmed El‐Gendy, Haitham Saeed, Hadeer S. Harb, Yasmin M. Madney, Mona Abdelrahman, Marwa Mohsen, Ahmed M.A. Ali, Mina Nicola, Marwa O. Elgendy, Ihab A. Ibrahim, Mohamed E.A. Abdelrahim

Articles

Coronavirus disease 2019 (COVID-19) has had different waves within the same country. The spread rate and severity showed different properties within the COVID-19 different waves. The present work aims to compare the spread and the severity of the different waves using the available data of confirmed COVID-19 cases and death cases. Real-data sets collected from the Johns Hopkins University Center for Systems Science were used to perform a comparative study between COVID-19 different waves in 12 countries with the highest total performed tests for severe acute respiratory syndrome coronavirus 2 detection in the world (Italy, Brazil, Japan, Germany, Spain, India, …

Impact Of Vaccine Supplies And Delays On Optimal Control Of The Covid-19 Pandemic: Mapping Interventions For The Philippines, Carlo Delfin S. Estadilla, Joshua Uyheng, Elvira P. De Lara-Tuprio, Timothy Robin Y. Teng, Jay Michael R. Macalalag, Ma. Regina Justina E. Estuar

Mathematics Faculty Publications

Background

Around the world, controlling the COVID-19 pandemic requires national coordination of multiple intervention strategies. As vaccinations are globally introduced into the repertoire of available interventions, it is important to consider how changes in the local supply of vaccines, including delays in administration, may be addressed through existing policy levers. This study aims to identify the optimal level of interventions for COVID-19 from 2021 to 2022 in the Philippines, which as a developing country is particularly vulnerable to shifting assumptions around vaccine availability. Furthermore, we explore optimal strategies in scenarios featuring delays in vaccine administration, expansions of vaccine supply, and …

Addressing The Ecological Fallacy With Lagrangian Inference, Michael Schwob

Calvert Undergraduate Research Awards

Most epidemiologists elect to use statistical models that use population-level data to make inference on the spread of some virus or disease. This has become commonplace in the fields of epidemiology and biostatistics since most data used to construct and verify epidemic models are recorded at the population-level. Obtaining inference from a population-level model may be beneficial in studying the spread of disease in a homogeneous population, but the use of such models to describe a heterogeneous population results in inadequate inference. The inaccuracy of these models is further amplified when one tries to make individual-level inference from these population-level …

Bypassing The Blood-Brain Barrier: A Physical And Pharmacological Approach For The Treatment Of Metastatic Brain Tumors, Samuel A. Sprowls

Graduate Theses, Dissertations, and Problem Reports (ETD)

This dissertation (a) provided an in depth literature review of methods to disrupt the BBB/BTB and improve therapeutic distribution to brain tumors, (b) evaluated the use of azacitidine as a single agent therapy for the treatment of brain metastasis of breast cancer and a potential molecular mechanism by which brain tropic cells are sensitized to hypomethylating agents, (c) determined the impact cannabidiol has on P-glycoprotein mediated efflux at the blood-brain barrier and its potential for use as a single agent treatment for metastatic brain tumors, (d) developed a preclinical radiation therapy protocol for use in small animals and in vitro …

Grouping Algorithms For Informative Array Testing In Disease Surveillance, David Sokolov

Graduate Theses, Dissertations, and Problem Reports (ETD)

In order to maintain normal operations and prevent unnecessary morbidity and mortality during times of disease outbreak, institutions find a need to conduct frequent and widespread testing of their constituents, often under significantly limited testing resource constraints. Faced with the challenge of how best to allo- cate these limited resources to maximum effect, institutions are increasingly turning to group (or “pooled”) testing, which involves testing strategically-chosen groups of patient samples rather than individual samples, producing significant testing resource savings under certain regimes of disease prevalence. While group test- ing can be conducted without any a priori knowledge of individual disease …

A Model For Inhalation Of Infectious Aerosol Contaminants In An Aircraft Passenger Cabin, Bert A. Silich

International Journal of Aviation, Aeronautics, and Aerospace

Aerosol contamination of an aircraft cabin by infectious passengers is a concern of passengers, aircrew and the aviation industry. This may be especially important during a pandemic, such as COVID-19, where the full extent of aerosol transmission is not well understood. A statistical method to determine the number of infectious passengers on board along with a mathematical model estimating the contaminant concentration of aerosols in the cabin and the number of inhaled infectious particles by passengers is presented. An example is used to demonstrated how the results can be estimated during normal operations and emergency conditions with malfunctions of the …

Nebulizer-Based Systems To Improve Pharmaceutical Aerosol Delivery To The Lungs, Benjamin M. Spence

Theses and Dissertations

Combining vibrating mesh nebulizers with additional new technologies leads to substantial improvements in pharmaceutical aerosol delivery to the lungs across therapeutic administration methods. In this dissertation, streamlined components, aerosol administration synchronization, and/or Excipient Enhanced Growth (EEG) technologies were utilized to develop and test several novel devices and aerosol delivery systems. The first focus of this work was to improve the poor delivery efficiency, e.g., 3.6% of nominal dose (Dugernier et al. 2017), of aerosolized medication administration to adult human subjects concurrent with high flow nasal cannula (HFNC) therapy, a form of continuous-flow non-invasive ventilation (NIV). The developed Low-Volume Mixer-Heater (LVMH) …

Adaptive Physics-Based Non-Rigid Registration For Immersive Image-Guided Neuronavigation Systems, Fotis Drakopoulos, Christos Tsolakis, Angelos Angelopoulos, Yixun Liu, Chengjun Yao, Kyriaki Rafailia Kavazidi, Nikolaos Foroglou, Andrey Fedorov, Sarah Frisken, Ron Kikinis, Alexandra Golby, Nikos Chrisochoides

Computer Science Faculty Publications

Objective: In image-guided neurosurgery, co-registered preoperative anatomical, functional, and diffusion tensor imaging can be used to facilitate a safe resection of brain tumors in eloquent areas of the brain. However, the brain deforms during surgery, particularly in the presence of tumor resection. Non-Rigid Registration (NRR) of the preoperative image data can be used to create a registered image that captures the deformation in the intraoperative image while maintaining the quality of the preoperative image. Using clinical data, this paper reports the results of a comparison of the accuracy and performance among several non-rigid registration methods for handling brain deformation. A …

Association Between Fruit And Vegetable Intakes And Mental Health In The Australian Diabetes Obesity And Lifestyle Cohort, Joanna Rees, Simone Radavelli-Bagatini, Johnny Lo, Jonathan M. Hodgson, Claus T. Christophersen, Robin M. Daly, Dianna J. Magliano, Jonathan E. Shaw, Marc Sim, Catherine P. Bondonno, Lauren C. Blekkenhorst, Joanne M. Dickson, Joshua R. Lewis, Amanda Devine

Research outputs 2014 to 2021

Increasing prevalence of mental health disorders within the Australian population is a serious public health issue. Adequate intake of fruits and vegetables (FV), dietary fibre (DF) and resistant starch (RS) is associated with better mental and physical health. Few longitudinal studies exist exploring the temporal relationship. Using a validated food frequency questionnaire, we examined baseline FV intakes of 5845 Australian adults from the AusDiab study and estimated food group-derived DF and RS using data from the literature. Perceived mental health was assessed at baseline and 5 year follow up using SF-36 mental component summary scores (MCS). We conducted baseline cross-sectional …

A Novel Augmented Deep Transfer Learning For Classification Of Covid-19 And Other Thoracic Diseases From X-Rays, Fouzia Atlaf, Syed M. S. Islam, Naeem K. Janjua

Research outputs 2014 to 2021

Deep learning has provided numerous breakthroughs in natural imaging tasks. However, its successful application to medical images is severely handicapped with the limited amount of annotated training data. Transfer learning is commonly adopted for the medical imaging tasks. However, a large covariant shift between the source domain of natural images and target domain of medical images results in poor transfer learning. Moreover, scarcity of annotated data for the medical imaging tasks causes further problems for effective transfer learning. To address these problems, we develop an augmented ensemble transfer learning technique that leads to significant performance gain over the conventional transfer …

Quantifying Anticancer Drug Doxorubicin Binding To Dna Using Optical Tweezers, Zachary Ells

Honors Program Theses and Projects

Doxorubicin is a successful anticancer drug approved for use in the 1970s and is considered to be one of the most effective cancer treatment methods today. Although Doxorubicin has positive survival statistics it has very negative side effects in many cases. Bleeding from the soles of the palms and feet, along with excruciating pain is often exhibited through the administration of this drug. Based on the preliminary findings utilizing optical tweezers we anticipate that this study will provide critical information about the drug binding mechanism. Single molecule biophysics techniques have provided useful insight into the DNA-binding mechanisms of small molecules. …

Applied Molecular Dynamics: From Targeting Viral Helicases, To Understanding The Interactions Of Cucurbituril Complexes In Ionic Solutions, Bryan Raubenolt

LSU New Orleans Theses and Dissertations

Molecular Dynamics simulations are a highly useful tool in helping understand the fundamental interactions present in a variety of chemical systems. The work discussed here illustrates it’s use in determining the conformational dynamics of the Zika and SARS-Cov-2 helicase in a physiological environment, largely in an effort to discover inhibitors capable of rendering the protein inert. Additionally, we show how it can be used to understand paradoxical trends in the anion-induced precipitation of Cucurbituril cavitands.

Viral helicases are motor proteins tasked with unwinding the viral dsRNA, a crucial step in preparing the strand to be translatable by host cells. By …