Although there is an effective vaccine for SARS-CoV-2, or COVID-19, the virus is still spreading and affecting millions of people worldwide. SARS-CoV-2, along with many other viruses, is able to form large, multi-nucleated cells, known as syncytia. Syncytia formation, along with syncytia death, may affect the SARS-CoV-2 course of infection. We have been able to compute the death rate of syncytia using data from a study by Vanhulle et al. (2023) that used measurements of electrical impedence to study syncytia formation in cell-cell fusion assays. The death rate of syncytia was found using mathematical modeling. This knowledge can help further our understanding of syncytia and viral disease propagation.

Zinc Oxide (ZnO) nanoparticles are attractive candidates for application as antibacterial agents due to high biocompatibility with effectiveness against antibiotic-resistant strains of both Gram-positive and Gram-negative bacteria. Despite this potential, applications are limited by fundamental gaps in understanding of the underlying antibacterial pathways. ZnO nanoparticles are currently more widely used in antibacterial research compared to ZnO microparticles due to the potential for internalization into bacterial cells. Microparticles are nevertheless of interest as a research platform as the increased scale allows both the nonpolar and polar facets of the ZnO crystals to be distinguished. This in turn provides a useful platform to experiment on and study surface interactions with bacteria. In addition, because of their larger size, ZnO microparticles would not internalize inside typical bacteria, allowing for more targeted investigation of other, potentially more potent, antibacterial mechanisms.

Preliminary studies indicate that hydrothermally grown ZnO microparticles exhibit comparable antibacterial activity to commercial ZnO nanoparticles further adding to their utility. The goal of this research is to validate the nature of these behaviors by investigating differences in surface cleanliness between “home-grown” microparticles which were synthesized in the lab through a bottom-up hydrothermal growth method and commercial nanoparticles. Such differences may influence cytotoxicity, skewing the results of antibacterial studies. To do so, both Scanning Electron Microscopy (SEM) and Fourier Transform Infrared (FTIR) spectroscopy were used to probe the quality and cleanliness of the ZnO crystalline free surface of the microparticles and nanoparticles.

In this work we detected similarities in the vibrational modes at the surface stemming from ZnO growth precursors. These are seen to be similar across all samples investigated, however, a weak O-H bending is found in the home-grown microparticles. We demonstrate that these results justifies our low-cost hydrothermally lab-grown specimen as a suitable platform for future surface-specific antibacterial studies.

Syncytia formation is the fusion of cells by a virus to create a multinucleated cell (syncytium) that shields the virus from outer factors in the extracellular space, such as antibodies. However, this process is much more energy intensive for a virus than tunneling between cells, which also shelters the virus. Why would a virus fuse cells together rather than save energy and tunnel? In order to determine what the benefits of syncytia formation are for viruses, a mathematical model including syncytia formation and antibodies was developed to simulate viral dynamics. Characteristics like viral duration, viral titer peak, and time of peak were measured while changing parameters such as fusion rate, which allowed comparison of infections with and without syncytia formation. Mathematically modeling and analyzing these comparisons and changes helps us understand whether syncytia formation helps protect viruses from the effect of antibodies.

To track drug delivery within the body, the vehicle must be biocompatible, soluble, and transparent in the human body. Being transparent in the human body means the vehicle exhibits fluorescence in the near-infrared (NIR) III biological transparency window (1500 – 1800 nm). These traits will respectively not oppose health defects in the subjects, will be stable within the blood and cells of the body, and be able to be found within the body through the means of infrared detectors. This is where graphene quantum dots (GQDs) come into the picture. GQDs prepared by a one-step hydrothermal method from glucosamine and ascorbic acid precursors are biocompatible and soluble in water. On their own, they do not demonstrate fluorescence in the NIR-III. To add this capability, we dope GQDs with erbium ions (Er-GQDs) as they demonstrate a fluorescence peak at 1550nm followed by excitation at 980nm laser. Fluorescence light coming from erbium ions at 1550 nm covers the NIR-III biological window, which is the last specification needed to have an eligible vehicle. In our work, we synthesized Er-GQDs at 200℃ for 8 h and 17 h in deuterium oxide. The fluorescence of erbium ions is known to be quenched by OH functional groups. The average size of Er-GQDs is growing from 3 to 5 nm after 8 h and 17 h treatment times, respectively, and exhibit fluorescence with 1550 nm emission peak in deuterium oxide. All aforementioned results make Er-GQDs a potential imaging agent for bioimaging.

COVID-19 now has antiviral treatments to help prevent hospitalization. Paxlovid is the most prevalent and effective of these medications. Paxlovid consists of two medications taken twice daily for five days, however, there have been anecdotal reports of rebound infection after a course of Paxlovid. This project aims to use mathematical models to investigate the infection conditions that result in rebound cases. Stochastic modeling is used to simulate the time course of infections with different doses and durations of Paxlovid to determine when rebound will occur. These findings could help physicians develop more consistent treatment regimens for Paxlovid.

Graphene quantum dots (GQDs) are a frontier of research in the interdisciplinary world of biology and medicine. They have been hallmarked for their remarkable applications, from cellular imaging to drug delivery. Due to their unique physicochemical and optical properties, there is a strong desire to bring them to clinical application. However, prior to any therapeutic and bioimaging studies comprehensive analysis of GQDs cytotoxicity has to be done in vitro. In our research, we assess the biocompatibility of a variety GQDs synthesized from different carbon-based precursors in non-cancerous cells through cell viability assay. Our results show that GQDs prepared from chitosan and glucosamine demonstrate 80% cell availability at 1.2 and 2.2 mg/mL concentrations, respectively, making them the most promising candidates for further therapeutic applications among over 15 GQD candidates tested.

The SARS-CoV-2 virus, which induced a global pandemic in 2020, is a serious pathogen that can cause acute respiratory distress in infected individuals. In order to garner a greater understanding of the SARS-CoV-2 virus and attenuate its effects, researchers have aimed to estimate key viral kinetic parameters. In this study, data from a previously published challenge study on the impacts of SARS-CoV-2 on young adults, including viral load, upsit score, and symptom score, was used to calibrate a system of ordinary differential equations, generating pathogenic parameters. In addition, Pearson covariance values and the Lyapunov exponents were calculated for each participant from the challenge study. For a majority of participants, the Lyapunov exponents were positive and finite, indicating chaotic behavior in vector space. Similarly, for most participants, there was a weak positive correlation between upsit/symptom scores and viral load. Future research will consist of implementing a newer system of ordinary differential equations that may be a better fit for the data

The SARS-CoV-2 pandemic initially made landfall in the United States in early 2020, and at that point in the pandemic, few developed treatments left the initial prevention of the disease largely up to preventative measures like mask mandates, quarantines for infected individuals, and social distancing policies. As a result, we must understand how preventative measures affect the transmission of infectious diseases to prepare us to fight the future spread of similar diseases. To accomplish this, we used a SEIR model with a variable transmission rate and fit SARS-CoV-2 case data to it. Principally, we used four models for the change in transmission rate: instant, linear, exponential, and logistic. Then using these models for the decay of transmission rate, we obtained SSR and parameter values that allowed us to compare models for each state. After comparing models between the four states we fit, there was no evident best-fit model for the decay in transmission. These results may suggest that regional differences like behavior, socioeconomic status, and exact preventative measures enforced could be responsible for the disparity in how the transmission rate decayed.

The most common immunological models for analyzing viral infections assume even spatial distribution between virus particles and healthy target cells. However, throughout an infection, the spatial distribution of virus and cells changes. Initially, virus and infected cells are localized so that a target cell in an area with lower virus presence will be less likely to be infected than a cell close to a location of viral production. A density-dependent rate has the potential to improve models that treat cellular infection probability as constant. A Beddington-DeAngelis model was used to understand how density dependent parameters could impact the severity of an influenza infection. Parameter values were varied to understand implications of density constraints. For low density dependence, a steeper increase in number of virus and greater viral peak was predicted. Higher density dependence predicted a longer time to viral load maximum and a greater infection duration. Initial localization of infected cells likely slows the progression of infection. The model demonstrates that accounting for density dependence when analyzing influenza infection severity can result in an altered expectation for viral progression. A density-dependent infection rate may provide a more complete view of the interaction between infected and healthy cells.

Mathematical models of cancer cells can be used by researchers to study the use of oncolytic viruses to treat tumors. With these models, we are able to help predict the viral characteristics needed in order for a virus to effectively kill a tumor. Our approach uses non-cancerous cells in addition to the tumor to determine when the virus will spread to non-cancerous cells. However, there are several models used to describe cancer growth, including the exponential, Mendelsohn, logistic, linear, surface, Gompertz, and Bertalanffy. We study how the choice of a particular model affects the predicted outcome of treatment.