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.

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.

Among the most life-threatening diseases, cancer poses a major issue and affects over fifty million people worldwide. To overcome the challenges associated with conventional chemotherapy, affecting both cancerous and normal cells, here we develop folic acid-functionalized Graphene Quantum Dots (GQDs) targeted to folate receptors overexpressed in a variety of cancer cell lines. GQDs due to their high biocompatibility and intrinsic fluorescence-based imaging capabilities have recently emerged as promising theragnostic agents. In this project, we synthesized GQDs utilizing the bottom up synthesis method and functionalized them with folic acid. The efficacy of the Folic acid functionalized GQDs (FAGQDs) is evaluated through their internalization study in cancerous (HeLa) and normal (HEK-293) cells by utilizing the intrinsic fluorescence of FAGQDs.