Ga2O3, an ultrawide-bandgap semiconducting material, sees widespread use in optoelectronic, pharmaceutical, and other industrial applications. Additionally, as antibiotic resistance grows, interest rises in the antibacterial properties of Ga2O3 and other gallium-containing compounds. In many cases, GaOOH is a precursor to synthesis of Ga2O3 with similar physiochemical properties. For microparticles, surface effects become heavily amplified. In particular, the surface effects may significantly influence antibacterial action. We synthesize GaOOH and Ga2O3 microparticles via hydrothermal growth. We employ scanning electron microscopy to image samples and energy dispersive X-ray spectroscopy to characterize the stoichiometry. X-ray diffraction spectroscopy is used by us to monitor bulk structural differences between the GaOOH precursor and Ga2O3. To monitor crystal defects we utilize photoluminescence spectroscopy. For antibacterial assays, we test our materials against Staphylococcus aureus bacteria using optical density measurement at 600 nm.

Several viruses can cause cells to fuse into large multinucleated cells called syncytia. Syncytia formation allows the virus to spread without entering the extracellular space, where it might be exposed to immune responses. However, there is evidence that antibodies can also hinder the fusion process. This project uses mathematical analysis to find different possible infection outcomes. A stability analysis of the coinfection model is used to find the fixed points of the model and their stability. This gives us parameter space regions that lead to different possible infection outcomes. Simulations were made to verify the mathematical analysis and see how different syncytia formation properties affect the resulting dynamics. These findings could help develop strategies for controlling viral spread.

Graphene Quantum Dots (GQDs) are nanoscale carbon based graphene sheets that exhibit unique fluorescent properties throughout a wide range of wavelengths. Given their uniquely small size, low toxicity, biocompatibility, and fluorescent capabilities, GQDs have many unique and important roles. To name a few, GQDs are used in drug delivery, fluorescent imaging, and biosensing thanks to their unique ability to fluoresce under different wavelengths of light. Furthermore, there are different types of GQDs with their own unique properties. Knowing this, five amphipathic molecules, called surfactants, were added to two different types of GQDs to test if they would impact the resulting fluorescence. Furthermore, concentrations of these added surfactants were varied to test how different concentrations of a given surfactant might affect the fluorescence for a given GQD. We observed that some of these surfactants provided a beneficial boost to GQDs fluorescence, while others slightly inhibited the fluorescence. Moreover, we saw that the increase in fluorescence varied based on the concentration of surfactant added yielding lower fluorescence for extremely low and high concentrations, while increasing the fluorescence at a more moderate concentration.

With cancer rates increasing at an alarming rate, many traditional methods for cancer treatment begin to feel outdated. This is where engineering nanomaterials, such as Graphene Quantum Dots (GQDs), offer a promising approach to making chemotherapy a more targeted treatment and therefore minimizing the side effects. This study focuses on optimizing drug delivery mechanisms using GQDs, specifically Reduced Graphene Quantum Dots (RGQDs) synthesized via a top-down approach from reduced graphene oxide, and Hyaluronic Acid Graphene Quantum Dots (HAGQDs) synthesized bottom-up from hyaluronic acid. The process is done by loading chemotherapeutics Gemcitabine, Paclitaxel, and Doxorubicin (DOX) HCl onto GQDs through sonication, this is followed by a centrifugal purification which isolates properly drug-loaded GQDs. To evaluate their controlled release, photothermal properties of GQDs are utilized. Samples are excited with an 808 nm laser at 1, 5, and 10 minutes, and they are later compared to a control group. This analysis provides insights into how laser stimulation affects drug release efficiency, paving the way for advancements in GQD based cancer treatments.

We introduce a structural method used for quantifying the spatial heterogeneity(or clumpiness) of viral syncytial cells in a transfection bioassay. The solution lies in an inter-disciplinary process based on simplicial topology being applied to a biological system. Our method revolves around using topological theories including Delaunay tessellations and Voronoi graphs to signify cell-cell interaction probability. The main emphasis is the subset of Delaunay tessellation called Alpha shapes. By applying a filtration to the overall Delaunay tessellation, we can obtain unique Alpha Shapes that have cell-cell interactions removed. The emphasis of the filtration is to find the correct shape where there were no connection crossing syncytia, only between healthy neighborhoods of cells. The process allows for the associated alpha number to be assigned to the clumpiness. Alpha numbers can then be used to separate different bioassays, or quantify temporal changes found in a single viral transfection due to syncytia.

In virology, mathematical models are often deployed to examine and test various behaviors of viruses. For example, one for the flu it is speculated that lethality is linked to the virus’s ability to propagate down the trachea, specifically in how ciliated cells push virus up through mucous layers in a process known as advection. We propose a model for this process, believing that this model can reveal links and critical points between lethality and advection. To solve this model, we utilize three techniques: Laplacian transform, non-linear analysis, and quasi-state analysis. We discuss the findings of each method.

Our neighboring galaxies, the Large Magellanic Cloud (LMC) and Small Magellanic Cloud (SMC), interact with each other as they move through the hot, outer region of the Milky Way. This interaction can pull and sweep away gas from the edges of the galaxies, forming large, stretched-out clouds of gas. The LMC has two gas filaments that resemble arms, which connect to a region where stars are formed, possibly hinting toward their origin or their final destination. In this study, we used radio observations and data from the Hubble Space Telescope to search for signs of these gas arms near the star-forming region. We find a continuous stream of gas that could be the arms located at least partially in front of the LMC. The positioning of these arms raises two competing questions: 1) Is the gas flow fueling new star formation in the LMC, or 2) Is gas from exploded stars in the LMC flowing out into these arms? While the inflow of gas makes sense for these gas flows, we also conducted simulations of outflows from the starburst region. Our results suggest that it is possible for debris from exploded stars to be swept into the arms. Future observations will help us better reconstruct the arms’ evolutionary history.

Some viruses have the ability to form syncytia. Syncytia are multi-nucleated cells formed via membrane fusion. Syncytia formation allows viruses to spread infection to other cells without entering the extracellular space where it could be exposed to antiviral drugs or immune responses such as antibodies. This project explores how syncytia formation can help viruses avoid antiviral drugs. Drug efficacy parameters are applied to a mathematical model of differential equations to explore the impact of antiviral drugs on cell infection, cell fusion, and viral production to model respiratory syncytial virus. The models show that as syncytia formation increases the drugs become less effective. This information will help physicians treat patients with syncytia forming viruses.

After the COVID-19 pandemic, over 40 million children worldwide are at risk of measles due to delayed vaccination and temporary SARS-CoV-2 viral dominance. The lasting immunosuppression caused by the disease presents a major health threat, and treatment options are urgently needed, especially for low- and middle-income countries. The manuscript by Cox et al. (2024) explores features of canine distemper virus (CDV) in ferrets, using this model as a surrogate for measles to evaluate two possible antiviral treatments, ERDRP-0519 and GHP-88309. Ferrets were infected with a lethal challenge of CDV and treated with either drug or therapeutic vaccination. We aim to characterize both the infection dynamics and efficacy of the two drug treatments using the data from the PBMC (peripheral blood mononuclear cell) associated viremia titers of CDV infected ferrets and the lymphocyte counts measured during the duration of the study. A differential mathematical model was fitted to the experimental data by minimizing the sum of squared residuals (SSR), and errors in the parameter fits were estimated using Monte Carlo Markov Chain (MCMC). We visualized the key parameter distributions for each dataset using histograms, allowing us to directly compare how each treatment influences infection dynamics. The results revealed that ERDRP-0519 reduced viral entry and enhanced clearance while GHP-88309 improved target cell growth and increased the rate of infected cell death. These findings suggest that both drugs are potentially effective measles treatment options, with ERDRP-0519 having a direct antiviral effect and GHP-88309 aiding in immune recovery. Overall, these insights provide a foundation for optimizing treatment strategies and highlight the potential for both drugs to combat measles and related morbillivirus infections, especially in areas with limited resources and vaccines.

Gliomas account for approximately 27% of all primary central nervous system tumors and exhibit highly aggressive growth patterns, making conventional treatments ineffective. Previous research has demonstrated that a replication-competent Sindbis virus (SINV) combined with cytokines (IL-7, IL-12, and GM-CSF) shows promising results in slowing down glioma progression. While prior research demonstrated that SINV combined with cytokines reduces tumor growth, a quantitative understanding of its effects remains limited. This study aims to develop and fit a mathematical model of oncolytic virus infection to data from previous research to quantify key biological processes in glioma treatment. By parameterizing the Sindbis virus-glioma interaction and estimating the effects of cytokine therapy, this model aims to evaluate the efficacy of different SINV variants, with and without cytokine combinations, in controlling tumor growth. We use an Ordinary Differential Equation (ODE) model to describe tumor growth inhibition by the oncolytic SINVs. The model includes variables for uninfected and infected tumor cells, viral load, and cytokine concentration. The data extracted from published tumor growth curves will be used to estimate key parameters, including viral replication rate, tumor growth rate, and cytokine effects. Parameter fitting will be conducted by minimizing the Sum of Squared Residuals (SSR) between model predictions and experimental data. Error in the parameters will be estimated through bootstrapping to find the best fit parameters with 95% confidence intervals. Preliminary analysis suggests that the model effectively captures tumor growth rates observed in the experimental data. Parameter estimation provides insights into the viral infection rate, cytokine-induced tumor suppression, and the timing of viral injections. These findings will help refine our understanding of how the SINVs and cytokine therapy interact in glioma treatment. This study provides a quantitative framework for evaluating the therapeutic effects of an oncolytic SINV combined with cytokines in glioma treatment. By providing parameter estimates for key biological processes, our model can help optimize treatment strategies and guide future experimental research in oncolytic virotherapy.

Mathematical modeling of viral kinetics can be used to gain further insight into the viral replication cycle and virus-host interactions. However, many virus dynamics models do not incorporate the cell-to-cell heterogeneity of virus yield or the time-dependent factor of virus production. A recent study of the kinetics of the vesicular stomatitis virus (VSV) in single BHK cells determined that both the virus production rate and the yield of virus particles vary widely between individual cells of the same cell population. We used the results of this study to determine the distribution that best describes the time course of viral production within single cells. The best distribution was then used to incorporate time-varying production into a standard model of viral kinetics. The best-fit model was determined by fitting potential distributions to cumulative viral production from single cells and comparing the Akaike Information Criterion (AIC). The results show that the best fit for most cells was log-normal. Time-dependent viral production was modeled with an integro-differential equation that incorporated the log-normal probability distribution into a standard constant production model of viral kinetics. This time-dependent model was compared to one of constant production by examining the differences between the viral peak, time of the peak, upslope, downslope, and area under the curve. These findings could have further-reaching implications for helping define the time course and nature of a particular virus infection within the human body as well as improving the dose-timing and efficacy of anti-viral treatments.

Syncytia are multinucleated cells that can occur due to virus infection of cells. Mathematical models in the form of ordinary differential equations can be used to simulate the growth of syncytia. Several novel ODE models can explain syncytia growth. Before employing these models on actual data, it is essential to analyze their structural (theoretical) and practical identifiability using computer software. Structural identifiability is an inherent property of each model and its parameters, referring to our ability to determine parameter values for the model given particular experimental measurements. Practical Identifiability analysis of a model is concerned with determining our ability to accurately determine parameter values given experimental error. Combining these two techniques enables us to determine whether or not the parameters of our syncytia models can be accurately determined. Obtaining accurate parameter values allows us to make conclusions about our data that can provide insight into the nature of the spread of syncytia. From this, we can plan experiments to parameterize the syncytia growth in the contexts of our models.

Mpox virus is a type of virus similar to smallpox that can cause diseases in humans. Several experiments have been done to collect data on how mpox evolves within an infected host. This data can be analyzed within the context of mathematical models to determine important characteristics of mpox. From this analysis, we can estimate the growth rate, reproduction number, and infecting time of mpox.  We can also construct confidence intervals to estimate the error in our predictions using bootstrapping.  Bootstrapping allows us to analyze parameter correlations within mpox data to understand how parameter values within the model affect each other in our model. From these values and confidence intervals, we can learn about how mpox evolves within the body over time. This information, in turn, may allow us to make predictions on how mpox evolves within people during infection that could inform future treatment regimens.

Iron zinc oxides are multifunctional materials with applications in luminescent devices, catalysis, spintronics, and gas sensors. Specifically, iron-doped zinc oxide (FeZnO) combines magnetic and chemical stability properties, making it suitable for technological and environmental applications. This study explores how synthesis parameters, including pH and dopant concentration, influence the morphology and properties of FeZnO nanoparticles. Hydrothermal synthesis was employed to prepare FeZnO with iron doping concentrations ranging from 1–10% and ZnO. Morphological and compositional analyses were performed using scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX). We also observed doped FeZnO antibacterial action for some of the synthesized samples in e-coli cultures. Future work will focus on improving dopant distribution, exploring antibacterial activity, and leveraging computational tools to refine material design for specific applications.

Star clusters are incredibly useful tools in the pursuit of understanding our Universe better. They can be used to discover how our Galaxy, the Milky Way, formed and evolved over time, delve into the secrets of how stars form and even track how the different chemistry around our Galaxy. However, determining whether a group of stars is truly a star cluster or just a group of stars is a difficult task. In this poster, we will go over what a star cluster is, how we determine membership of the star cluster and the current work we are doing to investigate galactic chemical abundance gradients using star clusters.

Oncolytic Herpes Simplex Viruses (oHSVs) target a wide range of different cells and specific mutations, allowing them to proliferate in tumor cells. Recent work has modified the virus to preferentially enter cells bearing epidermal growth factor receptors (EGFRs). This study focuses on characterizing the efficacy of different strains of EGFR-targeting oHSV by fitting a mathematical model that includes an interferon response to experimental data from U251 tumor-bearing mice. Using a combination of parameter fitting, optimization techniques, and ordinary differential equations (ODEs), we modeled tumor growth, viral dynamics, and immune response. Our findings suggest that an interferon-inclusive model best explains the growth and oHSV treatment of EGFR-bearing tumors. These results highlight the importance of immune interactions in oncolytic viral therapy and contribute to optimizing oHSV-based treatments for better clinical outcomes.

The use of fluorescent compounds as biological markers or probes is widely used in assays for probing various properties, including but not limited to pH, temperature, or the presence of various proteins. This has allowed fluorescence to enter the fields of microscopy, diagnostics, and spectroscopy. Among the many dyes used for such applications are those that exhibit phosphorescence. Unlike fluorescence, which has a lifetime of several nanoseconds, phosphorescence lifetimes can be several seconds, allowing for the use of techniques such as gated detection, which can eliminate distracting background noise or Raman scattering. Since phosphorescence uses the triplet state rather than the singlet state, it requires less energy, which correlates with longer wavelengths. The phosphorescence emission of some dyes can extend from 425nm (blue) to 675nm (red), which encompasses almost the entire visible spectrum. This is especially useful when considering that longer wavelengths may be used when utilizing direct triplet state excitation, which allows for excitation wavelengths well into the visible range. The ability to utilize longer excitation wavelengths has numerous possibilities, among which include being safe to use with live cells, which opens the door for using phosphorescence as a technique for biological imaging. Not only does phosphorescence allow imaging to occur at longer wavelengths, which mitigates damage to cells and minimizes exposure to harmful ultraviolet radiation, but it also allows for much more affordable equipment and procedures, possibly making diagnostic care more accessible.

Multipartite viruses are a unique class of viruses that divide their genome into multiple segments, each packaged into a separate viral particle. Unlike traditional viruses, which encapsulate their entire genome within a single particle, multipartite viruses require all genome segments to infect the same host cell for successful replication. This study investigates the infection dynamics of multipartite viruses through mathematical modeling, with a focus on bipartite and tripartite viruses. By comparing their behavior to single-particle viruses, we analyze the factors influencing viral persistence and spread. Our results indicate that the higher number of particles in a virus, the harder it is to maintain an infection. While multipartite infections exhibit shorter durations of infections compared to single-particle infections, their ability to persist suggests a potential benefit. These findings can help develop an understanding into the adaptive mechanisms of multipartite viruses and contribute to a broader understanding of viral evolution and host-virus interactions.

Surface plasmon–coupled emission (SPCE) is a powerful phenomenon that utilizes the near-field interaction between excited fluorophores and thin metallic films, together with a glass substrate, to significantly improve fluorescence detection sensitivity. By coupling the fluorophore’s oscillating dipole to surface plasmons, SPCE channels a substantial fraction of the emitted photons into a defined angle, generating a highly directional and polarized emission that can achieve up to 50% light collection efficiency. This intrinsically wavelength-resolved emission not only simplifies optical system design but also elevates the signal-to-noise ratio by reducing background interference. Compared to conventional isotropic free-space fluorescence, SPCE’s strong directional control and enhanced collection enable the detection of analytes at extremely low limits. Hence, this paper elucidates how SPCE’s unique advantages can be leveraged to achieve highly sensitive detection of critical biomarkers, paving the way for more rapid and efficient diagnostic applications.

Micro- and nanoscale metal oxides are used in a variety of applications. ZnO and Ga2O3 semiconductors are two metal oxides that have a wide bandgap and find themselves used in today’s electronics, gas sensors, and photodetectors. These two materials are also used in a wide range of temperatures, which means that the chemical bond lengths, vibrational states, defect states, and band-gaps all should be variable. In our experiments, we investigate the T-dependencies of positions, intensities, and widths of Raman peaks/bands for micro- and nanoscale ZnO and Ga2O3. In our studies, in addition to the temperature-dependent Raman spectroscopy we employ scanning electron microscopy (morphology of particles), energy dispersive X-ray spectroscopy (stochiometry) and temperature-dependent photoluminescence spectroscopy (electronic structure).

Human Immunodeficiency Virus (HIV) can exist as syncytia-forming or non-syncytia-forming strains, each utilizing different mechanisms of infection. Understanding the competition between these strains is crucial, as syncytia formation has been linked to increased disease progression and immune system decline. This study develops a mathematical model to analyze their competition, incorporating parameters such as fusion rate, syncytia lifespan, and viral production. Stability analysis and simulations will determine conditions under which one strain dominates or both coexist. By varying key parameters, we aim to understand how syncytia formation influences viral dynamics and infection persistence, providing insights into HIV pathogenesis and potential treatment strategies.

Cancer remains a major global health challenge, with over 20 million new cases diagnosed annually. Conventional treatments like chemotherapy, while effective, often require high doses due to non-specific targeting, leading to severe side effects. To overcome these limitations, we developed a targeted drug delivery platform using graphene quantum dots (GQDs), which offer high biocompatibility, near-infrared (NIR) fluorescence, and photothermal properties. In this study, hyaluronic acid-conjugated GQDs HA-GQDs and RGQDs, synthesized top down from reduced graphene oxide, are loaded with doxorubicin, paclitaxel, and gemcitabine, were tested in vitro using a custom-built, fully automated system for NIR laser irradiation and real-time spectral monitoring. Drug release was triggered by GQD-mediated photothermal heating and evaluated via MTT assays and fluorescence tracking. This work presents a novel, cost-effective nanocarbon-based drug delivery system integrating targeted therapy and photothermal control for enhanced cancer treatment.

Between the Andromeda (M31) and Triangulum (M33) galaxies lies a population of neutral hydrogen clouds which have velocities in between M31 and M33. The origin of these clouds is unknown, and it is thought that they could represent (1) a tidal bridge that links M31 with one of its satellite galaxies, (2) an inflowing intergalactic medium stream, (3) halo gas condensations, or (4) tidally-stripped material from a population of satellite galaxies. To ascertain the origin(s) of these clouds, we embark on a UV absorption and radio-line study to constrain their chemical composition. We assessed the ionization state of the gas using photoionization modeling with Cloudy that we anchored using HI and ion column densities that we measured from our Green Bank Telescope and HST/COS datasets. Through this work, we resolve the properties of a single gaseous stream of M31 along multiple sightlines, aiding in our understanding of L* galaxy ecosystems.

Modulating the Interferon Response to Enhance Oncolytic Virotherapy in Cancer Treatment
Anushka Velala, Hana Dobrovolny Ph.D
TCU Department of Physics, Fort Worth, TX
Background: Oncolytic viruses (OVs) are a promising immunotherapeutic strategy that can selectively target and lyse cancer cells while stimulating an anti-tumor immune response. However, their efficiency is often limited by the interferon (IFN) response, which acts as a key antiviral defense mechanism in host cells. Understanding the interplay between oncolytic viruses and IFN signaling is crucial for optimizing viral-based cancer therapies that have potential of success.
Hypothesis/Objective: This study aims to investigate how oncolytic viruses interact with the IFN response in a simulated tumor microenvironment. We hypothesize that higher values of variation in the IFN modulation can significantly negatively affect viral replication and therapeutic oncolytic efficacy.
Study Design and Research Methods: An analysis was conducted using a mathematical model with systems of differential equations. This model encompasses factors such as tumor growth, oncolytic infection dynamics, viral production and clearance, and the IFN-mediated immune response. Furthermore, sensitivity analysis was conducted to assess the influence of key parameters, including viral production rate, infection rate, and IFN clearance, on the treatment outcomes.
Results: Various simulations indicate that higher IFN levels correlate with reduced viral spread, leading to diminished oncolytic activity. However, parameter variations suggest that therapeutic efficacy can be optimized by adjusting certain parameters to mitigate excessive IFN responses. For instance, higher values of IFN efficacy are correlated with stronger IFN-mediated suppression of viral production, leading to lower sustained viral loads, while lower IFN efficacy levels allow for prolonged high viral replication. Similarly, IFN clearance rate affects how long IFN-induced killing of infected cells and uninfected cells persists, which can modulate the viral load over time. The most effective interferon response is a low-level response with low IFN clearance and high values of IFN efficacy, coupled with higher values of IFN-induced killing of uninfected cancer cells.
Conclusions: These findings underscore the role of the IFN response in modulating OV therapy and suggest that targeted suppression of IFN signaling could enhance OV efficacy in resistant tumors. This research provides insights for optimizing oncolytic virotherapy and improving clinical outcomes in cancer treatment, given the rising prominence of immunotherapy.

Gallium oxide is a wide-bandgap semiconductor gaining significance for its outstanding optoelectronic and gas-sensing properties. Although gallium oxide is known for its antibacterial efficacy, limited research is available on the antimicrobial properties of gallium oxyhydroxide (GaOOH). This study investigates GaOOH's antibacterial action by examining the effect of the growth solution's pH on its chemical and physical properties and their correlation with bacterial growth inhibition. The hydrothermal method was used to synthesize GaOOH microparticles (MPs). Deionized water, ammonium hydroxide, and gallium nitrate hydrate salt were mixed to create samples with pH levels ranging from 5 to 10 at 60°C. Subsequent analysis, including scanning electron microscopy, Fourier-transform infrared (FTIR) spectroscopy, and photoluminescence spectroscopy, revealed that higher pH levels increased the average GaOOH MPs length and created more crystal lattice defect sites. The correlation between surface chemistry and pH was evident in the position of higher energy FTIR Ga-OH bending bands. Antibacterial studies demonstrated a greater inhibition of Escherichia coli, a Gram-negative bacterium, at higher pHs. This suggests a potential role of defect sites in GaOOH's antimicrobial activity. There was significant inhibition of Staphylococcus aureus growth. However, no conclusive correlation with pH was established, possibly due to the characteristics of the Gram-positive cell wall. Future studies should further explicate the relationship between GaOOH MPs morphologies and growth inhibition of Escherichia coli and Staphylococcus aureus.