Syncytia are a type of multinucleated cell that can be formed by virus infection. Quantifying their growth is of particular interest for understanding virus infection within the body. One useful tool we have to understand the growth of these cells is ordinary differential equation (ODE) models. Current models neglect the regeneration of cells that form the syncytia. To account for regeneration, we will discuss a proposed modification of a basic model for cell-cell fusion, which will consider the addition of a logistic growth term. In addition, we will also consider a non-negligible death rate of syncytia. By making these modifications, we can better replicate syncytia dynamics. We present mathematical analysis of this model, which gives insight into the factors that generate long-term syncytia formation as well as the overall biological characteristics of such an infection.

Graphene quantum dots (GQDs) are spherical nanoparticles comprised of stacked layers of graphene known in part for their biocompatibility and fluorescence, which leads to many potential uses in medicine as a diagnostic tool. Solutions of GQDs are known to fluoresce less when the GQDs are allowed to clump together, leading to processes such as sonication being used to break apart these clumps in research environments. Similarly, the addition of surfactants to a solution of GQDs has also been found to modify fluorescence response of the solution. This research explores the effect of introducing four different human blood proteins on the fluorescence response of reduced graphene quantum dots (rGQDs). Fibrinogen, transferrin, gamma globulin, and albumin were added to samples of rGQDs in increments around their respective concentrations in human blood. Generally, we found that the addition of any of the blood proteins lowered fluorescence response in the visible spectrum. In the near-infrared spectrum, smaller concentrations of blood proteins generally increased fluorescence response, while larger concentrations reduced fluorescence response below the control. This has implications for deep-tissue imaging relying on the near-infrared fluorescence of intravenous GQDs.

Pandemics require quick decisions about how to distribute a limited number of vaccines, even when the disease is not fully understood and vaccine delivery is limited. We create a disease model that divides the population into groups based on how likely they are to be hospitalized and how likely they are to get infected, so we can test different group-based vaccination strategies. We compare vaccinating only one group, simple step-by-step priority policies that vaccinate groups for set time periods, and a sensitivity analysis to see which model factors most affect outcomes.

We find that vaccinating people who are both high-risk for hospitalization and highly likely to become infected leads to the biggest reductions in total hospitalized time and deaths, while vaccinating lower-risk groups gives little improvement in severe outcomes. A short step-by-step policy that quickly prioritizes high-risk groups can reduce infections and deaths within about 20 days. The sensitivity analysis shows that the death rate and the rate at which infected people move into hospitalization have the strongest influence on severe outcomes, showing that hospital and clinical processes matter a lot in addition to vaccination. Overall, these results support clear, practical, and easy-to-apply prioritization rules for reducing severe disease when vaccine supply is limited.

Influenza can cause severe respiratory illness and spreads quickly, making prevention especially important for people at higher risk of complications. Because vaccines are not always fully protective, effective antivirals can provide an added layer of defense before infection begins. CD388 is a new antiviral being tested as a preventive treatment for influenza. In this project, we used a mathematical model to better understand how the drug changes the course of infection inside the body. Viral load data from a human challenge study were fit to a target-cell model with an eclipse phase, allowing us to estimate key infection parameters. Compared to placebo, CD388 lowered peak viral load and reduced overall viral burden by about 22%, largely by suppressing viral production. Bootstrap analysis was used to assess uncertainty in the parameter estimates. These results help explain how CD388 limits viral spread and supports its potential as a prophylactic therapy.

Favipiravir, an antiviral that inhibits viral RNA-dependent RNA polymerase, has demonstrated promise as a therapeutic for RNA viral infections such as SARS-CoV-2. Mathematical modeling of viral kinetics provides a tool for analyzing the progression of viral infections and the action of antiviral drugs. In the present investigation, the viral kinetics of SARS-CoV-2 infection in cynomolgus macaques treated with the antiviral drug favipiravir were analyzed using a target cell-limited mathematical model of viral infection. Parameters of the model representing the dynamics of viral infection and replication were estimated by fitting the model to the viral kinetics data. Statistical resampling techniques were applied to analyze the uncertainty of the parameter estimates and to compare viral kinetics between the different treatment regimens. The results demonstrate that antiviral treatment induces measurable effects on viral kinetic parameters, reflecting dose-response effects on viral infection dynamics.

About one quarter of patients hospitalized with influenza-like illnesses are infected with more than one respiratory virus. Coinfections can lead to more serious outcomes for patients and are more complex to treat than infections with single viruses. Mathematical models can be used to help us understand the dynamics of viral coinfections and optimize treatment. Previous mathematical models of viral coinfections assume a cell-free transmission pathway with virus leaving one cell and traveling to nearby uninfected cells. However, viruses can also tunnel directly from one cell to another, which can affect how coinfecting viruses interact. This project analyzes a system of coupled ordinary differential equations that includes both cell-free and cell-to-cell transmission to better replicate actual viral spread. We measure coinfection duration for combinations of five common respiratory viruses as a function of the amount of cell-to-cell transmission. We find that coinfection duration depends nonlinearly on the cell-to-cell transmission rates, with differing patterns for different coinfecting viruses. This study highlights the importance of considering different transmission modes when modeling viral dynamics.

Oncolytic adenoviruses are promising cancer therapies because they can selectively infect and destroy tumor cells, however their replication in cancer cells is sometimes limited leading to incomplete tumor suppression. Recently, researchers have started to modify viruses to enhance their replication in cancer cells. In this study, we use a system of ordinary differential equations (ODEs) to model tumor growth and compare viral treatment dynamics of a modified oncolytic adenovirus ICVB-1042 and a wild-type adenovirus type 5 (Wt Ad5). The model was fit to experimental allowing us to estimate important model parameters for both viruses: infection rate, infected cell death rate, rate of cell protection by the immune response, rate of cell resistance loss, viral production rate, and viral clearance rate. We found differences in the viral production rates and the clearance rates between the two viruses, providing insight into how genetic modifications have altered viral dynamics. These findings highlight how viral properties determine the effectiveness of oncolytic virus therapy.

Title: Evaluation of A Learning Intervention to Support Dementia Care over Two Months
Authors: Emily Q. Anderson, Morgan Shumaker, & Uma Tauber
The Micheal and Sally McCracken Annual Student Research Symposium: April 17, 2026

Abstract:
Research indicates that providing care for individuals with Alzheimer’s Disease and Related Dementia (ADRD) can be highly demanding. Caregivers, who are often relatives or friends, face elevated stress levels and may lack adequate knowledge or resources to effectively support their loved ones (Jorge et al., 2021). This gap in knowledge often leaves caregivers feeling incompetent and may also lead to a lack of confidence in their caregiving skills. We have previously found that our digital health education intervention improves caregivers’ knowledge of behavioral and psychological symptoms of dementia (BPSD). In this research, we extend on our prior work by establishing the impact of our intervention on caregivers’ long-term retention – 2 weeks and 2 months post-intervention. Caregivers were taught 12 categories of BPSD (e.g., anxiety, agitation) by reading information and then either rereading or taking practice tests with detailed, corrective feedback, which has been shown to enhance learning and retention (Ariel et al., 2023; Carpenter et al., 2022; Dunlosky et al., 2013). The study consisted of 3 sessions. Caregivers first completed a screening process to determine their eligibility to participate. Session 1 consisted of teaching caregivers about the BPSD via reading and then rereading or taking tests with feedback. Session 2 consisted of a second round of the learning intervention, as well as taking survey assessments and knowledge tests. Finally, Session 3 consisted of completing final tasks and knowledge assessments. Our goal was to have caregivers learn what physicians would want them to know for how to care for their loved one living with dementia. This study provides caregivers with the knowledge and in turn, confidence to manage BPSD. Data is being collected concurrently, with nearly half of the target participants enrolled so far; thus,, the current presentation reports preliminary observations from this initial cohort.

I-sharing is the belief in an identical shared subjective experience between two individuals. Shared subjective experiences play an important role in interpersonal contact and it can lead to increased liking between subjects. Interestingly, the liking that I-sharing builds can override in-group bias, leading individuals to prefer I-sharing dissimilar partners over non I-sharing similar partners. While increased liking from I-sharing has been explored, intergroup perceptions, such as sexist beliefs, after I-sharing have not been explored. Unlike I-sharing, quality and quantity of time spent with women have both been linked to a reduction in sexist beliefs in men, which may suggest that I-sharing could induce a similar effect (Vasquez, 2020). Since sexist beliefs are connected to violent behavior and acceptance of violent behavior towards women in men, any reduction in sexist beliefs is significant (Gutierrez, 2023). This study seeks to explore the relationship between men I-sharing with women and sexist beliefs.

Social media has become deeply ingrained in young people’s daily lives. Coincidentally, mental health problems have also risen sharply over the past decade. This project examines the bidirectional relationship between social media use and mental health outcomes among TCU college students, who face unique challenges as they transition to a new phase of adult life. The causal relationships between undergraduate social media use and mental health remain unclear, with mixed findings in the literature. This is largely due to the overreliance on cross-sectional data and crude measures of social media use that focus solely on total screen time, which fail to capture fluctuations across the day and week, given the ever-changing contexts and diverse nature of social media use. Moreover, when studies consider mental health as a trait or categorical diagnosis, they fail to capture the fluctuating and co-occurring nature of symptoms. To address these limitations, we propose an Ecological Momentary Assessment (EMA) study leveraging Dr. Shi’s previously developed MediaHealth app for data collection.

Over 7 days, participants will receive push notifications through the MediaHealth app, prompting them to upload daily phone-use screenshots (from the Screen Time settings on their devices) and complete a questionnaire each day to report on their mental health states and social media use. These real-time assessments will capture daily stability and variations in social media use and mental health symptoms, helping to clarify potential causal pathways and inform strategies to support healthier digital behaviors among TCU students. This study aims to find how social media use among college students affects mental health, specifically looking at daily stress. It is expected that students who use social media more often will show an increase in their daily stress than those who use it less often. It can also be expected that stress can lead to increased use of social media among college students.