Quantifying the Reach of Social Determinant–Focused Supplemental Benefits in Medicare Advantage: A Health Informatics Approach
Medicare Advantage (MA) plans now offer supplemental benefits that go beyond traditional medical coverage. These include things like transportation to appointments, meal delivery, housing support, utility assistance, and pest control, all of which target the social determinants of health (SDOH) that affect patient outcomes well before a doctor visit ever happens. Federal policy, particularly through the expansion of Special Supplemental Benefits for the Chronically Ill (SSBCI), has given plans more flexibility to offer these services. But offering a benefit and actually getting it to the people who need it are two different things. I wanted to find out how many plans are really providing these benefits, and how many beneficiaries are actually enrolled in them.
Using SAS, I combined multiple CMS administrative datasets, including Plan Benefit Package (PBP) data and MA Enrollment by Plan files from the Centers for Medicare & Medicaid Services, into one analytic dataset. I linked plan-level benefit indicators to enrollment counts so I could estimate both the proportion of MA plans offering specific SDOH-related benefits and the percentage of beneficiaries enrolled in those plans.
What I found early on is a clear gap. Plans may list SDOH benefits on paper, but enrollment in those plans varies sharply depending on the benefit type. That disconnect between what is offered and who it actually reaches matters, because it tells us that expanding policy alone does not guarantee equity. This project shows that publicly available CMS data, when properly organized and integrated through health informatics methods, can expose these gaps and move the conversation from policy language toward something measurable.

Background: It is estimated that close to 50% of Americans experience stress daily. Research has documented that Latinos report higher levels of stress than other ethnicities. Stress is a normal occurrence and defined as the body and brain's natural, automatic response to any demand, challenge, or perceived threat. There are many factors that can impact stress, including sleep, diet, and exercise which are modifiable risk factors. These factors may affect ethnic groups in different ways. Purpose: The purpose of this integrative literature review is to explore the impact of exercise, diet, and sleep on the stress levels or perceptions of stress in the Hispanic and Non-Hispanic population. Methods: Databases used for this review included: CINAHL, EMBASE, PsycInfo, PubMed, Web of Science, and Scopus. The inclusion criteria were randomized control trials, mixed-method studies, quantitative and qualitative studies, systematic reviews, individuals age 18 or greater, Hispanic and Non-Hispanic populations. The articles reviewed included the years 2006-2026 utilizing the PRISMA extraction system. Conclusion: The findings should help researchers identify strategies to mitigate negative responses to these factors. Based on the increasing Hispanic demographic in the US, future research should address the inclusion of this population to address health disparities and gaps and enhance culturally competent interventions

Key words: exercise, diet, sleep, stress, Hispanic, Non-Hispanic, perceptions, diet quality

Physicians face increasing difficulty accessing relevant clinical evidence due to time constraints and the fragmentation of biomedical literature across multiple databases. Existing search platforms often require separate queries and may prioritize a single source, limiting the breadth and efficiency of evidence retrieval. This project aimed to develop and evaluate an artificial intelligence (AI)-based system designed to aggregate and prioritize clinical information from multiple open-access medical databases.
A multi-source retrieval tool was developed that integrates results from PubMed, Semantic Scholar, Directory of Open Access Journals (DOAJ), CORE, the World Health Organization (WHO), the Centers for Disease Control and Prevention (CDC), PLOS, and the National Network of Libraries of Medicine (NNLM). The system uses AI-assisted query processing and a source-weighting strategy to prioritize databases based on relevance to the query topic. Design decisions emphasized accessibility, ethical use of open-access content, and integration with clinical workflows.
Preliminary testing using representative clinical queries demonstrated the system’s ability to retrieve evidence from multiple complementary sources, increasing evidence diversity compared to single-database searches. Informal physician feedback highlighted the potential value of integrated retrieval for improving search efficiency and supporting evidence-based decision-making.
This work represents an early-stage clinical informatics approach to addressing information overload in healthcare. Future work will include structured usability testing with physicians, refinement of source prioritization algorithms, and evaluation of time savings and clinical relevance. AI-driven evidence aggregation tools may support more efficient clinical decision-making and improve access to high-quality medical information.

According to official AAMC data, 72.7% of entering U.S. medical students in 2025 took a gap year—one or more years between obtaining an undergraduate degree and matriculating into medical school. This represents a 22% increase compared to matriculating students in 2016, less than 10 years prior. Despite this rapidly increasing trend in medical school admissions, little scholarly research exists on how taking a gap year affects admission to medical school. The long-term goals of this study are to 1) identify factors that determine whether a pre-medical student may benefit from a gap year, 2) evaluate how a gap year may strengthen a medical school application, and 3) determine whether a gap year may improve or predict successful matriculation to medical school, questions that are currently not well understood. This project compiles current scholarly literature and data on pre-medical gap years to assess the existing knowledge on this topic. This study conducted a PRISMA systematic review of pre-medical gap year literature, categorizing works based on whether gap years were viewed favorably, neutrally, or negatively and analyzing them within the framework of the AAMC Premed Competencies. The literature review found that themes consistent with the AAMC Premed competency “commitment to learning and growth” were mentioned most frequently in discussions and opinions of gap years. Development of the competencies “interpersonal skills” and “empathy and compassion” during a gap year was most strongly supported by both qualitative and quantitative data. Notably, the review revealed that most available research examines gap years retrospectively, analyzing qualities of current medical students or residents that were influenced by their gap year. However, little research examines undergraduate students prospectively and their decision-making process regarding whether to take a gap year before applying to medical school. These findings highlight a significant gap in pre-medical gap year research that should be addressed in future studies to better guide pre-medical students and their advisors in decisions about taking a gap year and how it may affect admission outcomes.

The Frogs Aiding Dragons College Initiative works with the TCU organization Frogs Aiding Immigrants and Refugees (FAIR) to support Fort Worth immigrant and refugee communities, especially through partnerships with the International Newcomer Academy (INA). INA is a school specifically for 6th-9th grade refugee students. Many of these students have had no educational background or don’t fluently read or speak English. So, the goal of Frogs Aiding Dragons College Initiative is to encourage students to continue pursuing an education and convey that college is a possible goal for them. We work with a group of 62 9th graders where we bring them to TCU and host a Thanksgiving feast, campus tour, and panel with TCU immigrant students. We then bring the college experience to INA with presentations and hands-on activities from various students representing various TCU departments, including Chemistry, Pre-Health, the Fine Arts, and Engineering. We assess the effectiveness of this initiative using a survey measuring INA students’ attitudes towards desire to attend college, how much they know about college, and if they feel like they have more resources to apply to and attend college.

In quantitative studies comparing a treatment and a control group, treatment effect is often viewed simply as the difference in group means. However, any treatment can have an impact beyond simply shifting the mean outcome. In this work, we consider a linear treatment effect (LTE) model, meaning we simultaneously consider the difference in means and the ratio of standard deviations between two populations to better characterize the effect of the treatment. Estimation is done using an empirical likelihood (EL) formulation. The EL framework provides a nonparametric approach for conducting inference without making strong assumptions about the underlying population model. Generally, the EL statistic has a limiting chi-square distribution. However, in small sample settings, the EL statistic can exhibit strong deviations from this ideal. To address this issue, we investigate the use of the Bartlett correction, which is a multiplicative adjustment to the EL statistic to improve the chi-square approximation. This correction has been shown to substantially improve confidence region coverage accuracy, especially for small and moderate sample sizes. Through simulation, we examine the performance of the EL statistic in the LTE model, with and without a Bartlett correction applied. Our results demonstrate that the Bartlett-corrected EL approach provides improved performance, yielding confidence regions with coverage closer to desired nominal levels.

In this thesis, we introduce a way to implement Stochastic Processes - particularly Markov chain properties - for analyzing Liar’s Poker, a variant of Poker Texas Hold’Em that incorporates hidden information and a card-switching mechanic. Poker, and in particular Liar's Poker, presents a complex environment in which probabilities evolve as information is revealed and players make sequential decisions under uncertainty, so Markov modeling of this game requires a more flexible state-based representation. The study focuses on two main objectives: first, to construct a state space and transition matrix that are sufficiently compact for analysis while still capturing meaningful changes in hand-strength and game dynamic; and second, to investigate how the game’s exclusive card-switching feature can be incorporated into an optimal decision-making strategy. To address these goals, the thesis models gameplay as a sequence of probabilistic state transitions driven by card draws, hidden information, and strategic actions. By extending Stochastic Process methods to a poker setting with imperfect information and dynamic transition, this thesis aims to provide a structured mathematical framework for evaluating strategy in Liar’s Poker.

Incurred But Not Reported (IBNR) reserves refer to insurance claims that have already taken place, but have not yet been reported to the insurance provider. This presentation formulates a Bayesian modeling framework to estimate the IBNR reserves. The Bayesian framework allows us to incorporate prior knowledge, typically available from historical data and expert opinions, along with the observed claim data, to estimate model parameters and predict future claim liabilities. We emphasize prior models that have heavy tails and therefore can accommodate extreme, rare losses that can be underestimated otherwise. Specifically, we consider Pareto (Type I) and log-t models for the expected ultimate claim amounts for each insurance period. The data generating mechanisms considered are Poisson, negative binomial, and gamma. The analysis of real data also considers model sensitivity to the choice of the prior parameters. In doing so, we aim to produce more robust reserve estimates and better reflect the uncertainty inherent in unpaid claim liabilities. Ultimately, modeling IBNR reserves is important because it ensures insurance companies set aside sufficient funds to cover future claim obligations and avoid unexpected losses that could impact profitability.

In vitro studies are critical for elucidating the mechanisms contributing to disease development, such as how diet-induced obesity leads to increased risk and incidence of breast cancer. However, they are often limited to short-term trials which fail to mimic the prolonged exposure to energy excess that contributes to obesity development. This project investigates how acute and chronic exposure to the saturated fatty acid, palmitic acid, affects the oxidative stress response in MCF-7 breast cancer cells. In excess, palmitic acid increases cellular lipid load and can lead to the production of reactive oxygen species (ROS), ultimately resulting in inflammation and cellular damage. However, chronic exposure to low doses of oxidative stressors have been shown to be protective against acute stress events via a biologic process termed hormesis. The study compares short-term and long-term treatments to determine whether continuous exposure amplifies oxidative stress or activates cellular defense mechanisms. To do this, I have already been culturing MCF-7 cells in a low dose of palmitic acid, and will continue to do so for 3 months. Once cells have been chronically exposed to a low dose (50 µM) of palmitic acid, they will be acutely treated with a high dose (250 µM) of palmitic acid for 24-48 hr. Basal ROS production (before high dose treatment) will be compared ROS production after the high dose treatment in cells that have been chronically exposed to palmitic acid. Cells that have not been chronically exposed will serve as our controls. These studies will help us understand how chronic exposure to saturated fats, as occurs with diet-induced obesity, alters oxidative balance in breast cancer cells and contribute to a better understanding of how diet influences cancer progression and cellular stress responses.

Background: Chronic stress among older adults increases risk for depression, anxiety, cardiovascular disease, and cognitive decline. Nature-based interventions may improve psychosocial and physiological stress outcomes, though longitudinal evidence in aging populations remains limited.

Methods: A convenience sample of community-dwelling older adults (N = 21; M age = 74.14 ± 4.59 years; range 65–85) participated in a six-week Nature Rx intervention study conducted in partnership with the Fort Worth Botanic Garden and Texas Christian University. The program included three two-week modules meeting twice weekly: garden yoga, forest bathing, and vegetable gardening. Repeated measures for the Well-Being/Personal Health Index (WPHI), positive and negative affect, nature-relatedness, outdoor activity minutes were assessed at baseline and at three subsequent time points across the program. Measures for handgrip strength (HGS), and salivary cortisol were assessed at the beginning of the first and sixth weeks, respectively. Friedman tests with Bonferroni-adjusted post hoc comparisons evaluated changes in psychosocial outcomes. Paired t-tests and repeated-measures ANOVA assessed physiological outcomes. Analyses were conducted using available cases due to incomplete measurements across time points.

Results: WPHI scores did not significantly change over time, χ²(3, n = 18) = 1.886, p = .596. Positive affect significantly increased, χ²(3, N = 18) = 13.437, p = .004, with higher final scores compared with baseline (padj = .012) and Post 2 (padj = .018). Negative affect also showed a significant overall time effect, χ²(3, N = 18) = 11.131, p = .011, though pairwise differences were not significant after adjustment. Nature-relatedness and outdoor minutes did not change (all p > .05). HGS remained stable, t(18) = −1.08, p = .294, and strength classification did not significantly change (Wilcoxon W = 3.00, p = .157). Salivary cortisol significantly decreased from pre- to post-intervention, t(21) = 7.653, p < .001 (d = 1.63); ANOVA confirmed a significant condition effect, F(1, 12) = 33.09, p < .001, ηp² = .734.

Conclusion: The intervention was associated with increased positive affect and statistically significant reductions in physiological stress, despite minimal changes in global well-being or muscular strength. These findings suggest short-term nature-based programs reduce stress burden in older adults. Larger controlled studies are needed to confirm these preliminary results.

The transcription factor, Nuclear factor erythroid 2-related factor 2 (NRF2), functions by activating genes that help protect the body against oxidative stress, inflammation, and various toxins. Thus, identification of small molecules that can increase NRF2 activity could be helpful to increase the body’s natural defense system against chronic disease. The goal of this interdisciplinary project is to use cell lines generated by the Montgomery lab (Nutrition) that express a fluorescent NRF2 reporter to test a small library of novel compounds generated by the Green lab (Chemistry) for their NRF2 activation capacity. First, our reporter system will be validated with known NRF2 activators. We will then use a luciferase reporter assay to screen 15 novel compounds for their capacity to activate NRF2 compared to the known standards. These data can then be used to inform both labs about their antioxidant capacity and help optimize their furthered development and utility.

Prebiotic sodas are marketed as healthy alternatives to traditional soda, but these claims have not yet been substantiated by research. This study evaluated the effects of fasted consumption of the prebiotic sodas Olipop and Poppi, compared with Diet Coke and Coca-Cola Original, on blood glucose, insulin, glucagon-like peptide-1 (GLP-1), satiety, gastrointestinal symptoms, and beverage preference. Objectives were to determine the effects of carbonated prebiotic beverages compared to conventional carbonated beverages on blood glucose, insulin, GLP-1, satiety, gastrointestinal symptoms, and beverage preference. A single-blind, repeated-measures, randomized crossover design was employed with 10 male participants age 19-28 with BMI between 18.4 and 24.9 kg/m2 and no diagnosis of pre-diabetes or diabetes. Participants completed four randomly assigned trials with 12 oz of each beverage and a one-week washout period between each. During each visit, blood samples and satiety questionnaires were collected at baseline and throughout a two-hour trial. Beverage preference was assessed post-consumption, and gastrointestinal symptoms were evaluated using a follow-up questionnaire 24h post-intervention. High interpersonal variability in glucose measurements yielded no significant differences in glucose response to any beverages. Coke yielded the highest spike in insulin concentrations while Diet Coke showed no insulin release. No significant differences were found for GLP-1 release or satiety. Prebiotic sodas were rated the worst, followed by Diet Coke, with Coke being rated the highest. High interpersonal variability to beverage responses highlights the need for personalized nutrition and larger sample sizes in further research. While statistical significance was not reached for many values, Coke resulted in worse biochemical results than any other trial beverages.

The overall goal of our study is to understand how excess adiposity in women with and without
confounding cardiometabolic risk factors influences breast cancer cell growth and oxidative stress
signaling. I have already collected preliminary data indicating that activity of the antioxidant response
gene, NRF2, and expression of NRF2 targets are decreased in serum from obese subject, regardless of
phenotype. We investigated the functional consequences of these responses
by measuring and quantifying differences in reactive oxygen species (ROS) production. We also
investigated if these changes could lead to changes in breast cancer cell growth. To
investigate this, MCF7 breast cancer cells was grown in 6 distinct treatment groups reflecting varied
human metabolic health: CON (healthy control), NWO (normal weight obese), MUO (metabolically
unhealthy obese), and MHO (metabolically healthy obese), alongside the standard fetal bovine serum-
containing media a negative control. Reactive oxygen species production was assessed using a reagent
that fluoresces when it becomes oxidized by ROS. We expect cells grown in serum from obese subjects
will have higher levels of ROS production and increased invasive capacity. However, the results have yet
to be processed as of Mar 6. This research could demonstrate how total systemic metabolic health
influences oxidative stress responses and invasive potential, linking gene expression to real functional
outcomes. These insights could heavily inform medical assessments.

Oral health is an essential part of overall well-being, yet many children in underserved communities lack access to dental education and basic hygiene resources. Although cavities are completely preventable, they remain one of the most common chronic diseases affecting both children and adults. Early education is crucial for building lifelong habits and preventing future dental issues. The New Smiles initiative is a student-led outreach program focused on improving oral hygiene awareness and access to care among elementary students in the Fort Worth community.

Through interactive elementary school presentations, the program teaches proper brushing and flossing, healthy eating habits, and the importance of routine dental visits to kids. To reinforce these lessons, hygiene kits containing toothbrushes, toothpaste, floss, and educational materials were assembled in collaboration with Cook Children’s and distributed to participating students. Additionally, a brief survey was administered to assess students’ baseline knowledge of oral hygiene and evaluate the effectiveness of the educational presentation.

By combining hands-on education, community partnerships, and the distribution of essential hygiene supplies, the New Smiles program aims to promote preventive oral health practices at an early age. This initiative seeks to reduce oral health disparities while empowering children with the knowledge and resources needed to maintain lifelong dental health.

Women who are obese have a much higher risk of being diagnosed with breast cancer than women who maintain a healthy body weight. However, excess body fat, even in the absence of excess body weight, a condition referred to as normal weight obesity also increases breast cancer risk. The goal of our study is to determine how serum from human subjects with three distinct obesity phenotypes, metabolically healthy obese, metabolically unhealthy obese, and normal-weight obese, influences breast cancer cell growth and proliferation. We have already collected preliminary data indicating differences in cell viability via NADH measurement, yet metabolic activity alone does not definitively demonstrate growth or vitality because cells may be metabolically active without entering S-phase or replicating. To conclusively show DNA replication (and thus true proliferation/vitality), our plan is to quantitatively measure differences in DNA synthesis using the Click-iT EdU DNA-synthesis assay, which uses a thymidine analog incorporated into newly synthesized DNA which can be detected by the appearance of fluorescent conjugates. Based on our preliminary findings, we expect that the lower rates of metabolic activity in cells grown in serum from obese subjects are not due to reduced rates of cellular proliferation. These findings could be used to inform improved, targeted nutritional and chemotherapeutic strategies for individuals with distinct obesity phenotypes.

Viral stability, replication, and host-virus interactions are all significantly impacted by temperature. Numerous experimental studies have demonstrated that SARS-CoV-2 grows differently at different temperatures, but it is still unknown which specific infection processes are impacted. In this work, we used a mathematical modeling approach to quantify the effects of temperature on the kinetic parameters controlling SARS-CoV-2 replication. Results from previously published experiments were used to determine the viral load from in vitro infections of Vero E6 and human nasal epithelial (hNEC) cells at 33 and 37 C. We fit a mathematical model of viral infections to estimate model parameters at the two temperatures. Vero E6 cells showed evidence of temperature dependence when parameter distributions were compared; the infection rate, eclipse phase transition rate, and infected cell death rate varied between 33 and 37 C. The parameter estimates in hNEC cells, on the other hand, revealed no statistically significant differences and showed a significant overlap in parameter estimates between temperatures. These results imply that the cellular environment has a significant impact on how temperature affects SARS-CoV-2 replication dynamics. The measurement of temperature-dependent variations in viral kinetic parameters sheds light on SARS-CoV-2 replication and could enhance forecasts of infection dynamics under various environmental and physiological circumstances.

Our Milky Way’s neighbor, the Large Magellanic Cloud (LMC), is a galaxy significantly shaped by powerful explosions from massive, dying stars that drive gas outflows. These explosions release gas and heavy elements, enriching the galaxy's outskirts and contributing to the formation of stars and planets. Understanding these processes is crucial for studying galactic evolution and the mechanisms that drive it. Our research uses observations from the Hubble Space Telescope to characterize the properties of the outflows from the LMC. Our observations are of light from background stars that pass through the LMC’s gas clouds. These clouds block some of the incoming light, and we analyze the missing features to study the physical properties of the outflows. To compare complex stellar spectra on a similar scale, we fit regions of the light that are free from major features blocking it with a best-fit polynomial. This process helps us differentiate components that either belong to the background star or the LMC’s outflowing gas. By examining the missing light, we gain a deeper understanding of how bursts of star formation impact the galactic environment and ultimately connect our existence to the explosive deaths of distant stars.

Zinc oxide (ZnO) is a versatile, inexpensive semiconductor material with unique characteristics. ZnO is particularly known for its inhibitory effects on bacterial growth. ZnO can reduce bacterial growth through mechanisms such as oxidative stress, the deterioration of crucial proteins in the bacterial cell, and the release of Zn²⁺ ions that affect bacterial cell function. The exact mechanism behind ZnO’s antibacterial properties remains unclear. It has been seen that changing the surface and morphology of the particles changes their effectiveness for bacterial inhibition. An additional lesser explored branch of ethanol-based synthesis is solution pH pertaining to ZnO morphology. Our research aims to explore this by doing a wholistic investigation of an ethanol-based synthesis, especially pertaining to how pH affects particle morphology. To produce these materials, we used ethanol-based solvothermal synthesis to create ZnO micro- and nanocrystals. We performed a thorough characterization of these materials to observe changes to the ZnO lattice. This was done by employing scanning electron microscopy (SEM), energy-dispersive X-ray (EDX) spectroscopy, and X-ray diffraction (XRD) spectroscopy.

Graphene quantum dots GQDs possess broad potential in bioimaging and optoelectronics due to their unique optical properties, tunable structure, aqueous solubility, and minimal in vivo and in vitro toxicity. However, despite their solubility, GQD fluorescence may be quenched through interactions with water molecules and aggregation via non radiative decay pathways that reduce emission efficiency. Inspired by the ability of surfactants to prevent quenching interactions for single walled carbon nanotubes, we investigate their utility in preserving GQD fluorescence. Five structurally distinct surfactants, sodium dodecyl sulfate SDS, sodium dodecylbenzene sulfonate SDBS, sodium deoxycholate SDC, sodium cholate SC, and Pluronic F127, are tested across a range of concentrations for preserving fluorescence of top down and bottom up synthesized GQDs to determine optimal conditions. This work reveals that surfactant structure and concentration can non-linearly affect GQD emission in the visible and near-infrared, with SC and SDC providing maximum concentration dependent fluorescence increase. Zeta potential and dynamic light scattering measurements are conducted for each surfactant and GQD system to quantify interfacial charge, colloidal stability, and aggregate size distributions. The present study provides mechanistic understanding of how surfactants influence GQD photophysics, offering strategies to optimize GQD based probes for biomedical imaging and photonic applications establishing a structure-to-function framework that links solution phase organization to fluorescence emission.

Graphene quantum dots (GQDs) have gained significant attention due to their unique optical properties, biocompatibility, and potential applications in bioimaging, biosensing, and optoelectronics. The breakdown of single-walled carbon nanotubes provides an alternative method of producing GQDs that has the potential to be more efficient than current methods. We will investigate the effectiveness of various methods to break down single-walled carbon nanotubes, including through UV-light irradiation. Solutions of carbon nanotubes with sodium hypochlorite are placed under 254nm UV-light for two hours, and fluorescence in the visible spectrum is measured before and after UV-light irradiation to observe the production of GQDs. The use of surfactants in these solutions can affect the resulting fluorescence, so solutions of sodium dodecyl sulfate (SDS) and sodium dodecylbenzene sulfonate (SDBS) are also UV-light irradiated and observed. We will perform transmission electron microscopy (TEM) analysis on the samples to characterize the resulting GQDs and determine their size distribution. The findings from this study will contribute to the broader scientific community by improving an avenue of production for GQDs through conversion of carbon nanotubes into smaller, more functional materials while reducing the toxicity associated with carbon nanotubes.

Defective interfering particles (DIPs) are virions missing the viral genome that allows them to replicate on their own, so they require coinfection with a standard virion to enable replication, interfering with the production of standard virus in the process. DIPs may also stimulate an interferon (IFN) response that further suppresses standard virus replication. Our aim was to evaluate the impact of DIPs and IFN on viral replication. We used Python programming to simulate a mathematical model evaluating the effects of DIPs and IFN on viral replication. Features of the viral titer curve were measured, including peak viral load and area under the viral curve, as functions of IFN parameters and DIP production rates. We examined a range of parameter values for DIP production rate and IFN response strength to assess the effects of DIPs and IFN independently and together. DIP production rate over a range of values resulted in no change in DIP or standard virus population dynamics. However, decreased IFN response resulted in an increase in standard virus and DIP population, while increased IFN response resulted in decreased standard virus and DIP population. DIP production in isolation did not impact viral replication, while IFN demonstrated an inverse relationship to viral replication and DIP production. Increased IFN and DIP production rate led to a reduction in infection intensity. IFN is essential to the antiviral effects of DIPs.

Galaxy simulations are an effective way to study the evolution of galaxies across
cosmic time. They have provided insights into the structural and chemical evolution
of galaxies, gas and star formation, and how LCDM models predict the large scale
structure of universe. Nevertheless, two primary issues have persisted using LCDM -
the core-cusp problem and the diversity of rotation curves for dwarf galaxies of similar
masses. To determine the effect of AGN on these issues, we utilize FIRE-2, which only
includes stellar feedback. We chose this particular galaxy at redshift 0 and compared
the curve to 8 previous observations, and we find that the innermost regions of the
curve are better matched to the data, but diversity still remains a problem. Thus, we
conclude that AGN feedback prescriptions may be removing too much mass from the
center of the galaxy, causing this discrepancy. Hence, more work is necessary to identify
the cause of this issue and potentially resolve it.

Influenza virus causes periodic pandemics and thousands of deaths annually, but many of the details of the viral replication cycle are still poorly understood. This study develops a mathematical model of the dynamic transitions of a virus from the extracellular space through the initial intracellular replication processes. These stages include: binding, endocytosis, HA Acidification, Fusion, and Uncoating. Experimental data from the viral entry phases were fit to a system of differential equations, which represent the biological processes. The model parameters were estimated using optimization techniques that minimize the sum of squared residuals, thereby aligning model predictions with observations. An identifiability analysis was performed to see which parameters can be estimated with the given model and available data. We find that the model fits the experimental data well with identifiable parameters, allowing us to characterize the different stages of viral entry. The model can be used to compare different viral strains or treatment options, in addition to helping explain the kinetics of viral entry.

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.