Graphene Quantum Dots (GQDs) are highly perspective bioimaging agents due to a plethora of advantageous properties making them superior to conventional fluorophores. Those properties include stability to photobleaching, large Stokes shifts circumventing biological autofluorescence, and a capability of functionalization for drug delivery. In this work, a variety of GQD structures are imaged via visible fluorescence microscopy in order to evaluate the optimal GQD structures for bioimaging and bioengineering in vitro.

Infections deriving from the highly pathogenic avian H5N1 influenza virus often result in severe respiratory diseases with a high mortality rate. Although rarely transmissible to humans, recent events such as the SARS-CoV-2 pandemic have shown that a proper understanding of the life cycles of deadly viruses like H5N1 and any variables that affect its terminality are vital. One such variable could be the method of entry, and its impact on the progression of H5N1 is the focus of the study. Utilizing previous data on cynomolgus macaques subject to samples of H5N1, we study how entry via a combined intrabronchial, oral, and nasal pathway affect disease progression. We fit the data using a viral kinetics model, which allows us to estimate parameters describing the H5N1 life cycle. This allows us to better understand the life cycle of H5N1 in vivo.

Everyone gets sick and illness negatively affects all aspects of life. One major cause of illness is viral infections. Some viral infections can last for weeks; others, like influenza (the flu), can resolve quickly. During infections, healthy cells can grow in order to replenish the cells that have died from the virus. Past viral models, especially those for short-lived infections like influenza, tend to ignore cellular regeneration – since many think that uncomplicated influenza resolves much faster than cells regenerate. This research accounts for cellular regeneration, using an agent-based framework, and varies the regeneration rate in order to understand how cell regeneration affects viral infections. The model used represents virus infections and spread in a two-dimensional layer of cells in order to generate graphs of virus over time for corresponding regeneration rates. We find that the effect of cell regeneration depends on the mode of transmission of the infection.

Photothermal Therapy (PTT) provides a promising new method of radiative therapy cancer, using infrared wavelengths. In my project, the ability of these materials to heat up when shone with near infrared light, or the photothermal effect, of various nanomaterials—including reduced graphene oxide, reduced graphene quantum dots , and copper sulfide nanoparticles—is characterized by irradiation of the aqueous materials with near-infrared radiation.

With the onset of the SARS-CoV-2 pandemic in the U.S. in early 2020, much of the early response in the U.S. was made on a state level with varying levels of effectiveness. To characterize the effects of early preventative measures by state legislatures we can use a SEIR model and data gathered to analyze the effectiveness of lockdown measures from state to state. Using the data collected we can model the effect of lockdown measures on the infection rate to characterize the effect preventative measures had on case numbers. We chiefly used 4 models to simulate the change in infection rate: instantaneous, linear, exponential, and logarithmic. Then using these models, we fit each model to the case data and compared the relative accuracy of each model to the data to determine which model most accurately represented the change in infection rate within the first months of the pandemic. Following this, we used the fits obtained to create a possible distribution for each parameter, which helps accurately predict the actual number of cases and how it was affected by preventative measures.