Presbyphonia, or "aging voice", is one of the most common voice concerns, with prevalence ranging from 19-40% in the older adult (60+) population. Common symptoms of presbyphonia are a strained, weak voice and reduced loudness, which causes communication difficulties that negatively affect mental and social wellbeing. A key factor contributing to presbyphonia is vocal fold atrophy, which is deterioration of the muscle and tissue in the vocal folds. This causes weakness and incomplete vocal fold closure during voice production. Treatment usually consists of voice therapy with a speech-language pathologist (SLP) or vocal fold injections to bulk up vocal fold volume from an otolaryngologist. Currently, the standard for viewing the vocal folds to assess post-treatment change in vocal function is via an endoscope. However, endoscopic examination of the vocal folds relies on perceptual assessment in two dimensions, restricting analysis of vocal fold volume. In this research project, I am using magnetic resonance imaging (MRI) to view the vocal folds in 3D, allowing for a complete analysis of volume in mm3. My goal is to use this novel method to provide new information on which treatments create the best outcomes for patients in terms of vocal fold volume and voice quality. Participants undergo a comprehensive voice assessment conducted by me and a mentor SLP, an initial MRI scan, and then complete one of two treatment pathways: either 4-6 weeks of voice therapy or vocal fold injections. After treatment, they return for another voice assessment and MRI to evaluate the effects. This research is ongoing, but I anticipate that both treatment groups will experience improvement in voice outcomes, consistent with the literature. However, it is unknown if vocal fold volume will increase in both treatment groups, as MRI has not been used to assess post-treatment outcomes in this patient population.  

Developmental language disorder (DLD) is a prevalent lifelong communication disorder that encompasses challenges in learning, understanding, and using language not attributed to other bodily or environmental conditions. It is heritable, but its exact cause is unknown. Understanding why a specific population has language difficulties is essential to clinical communication support. This research aimed to establish a protocol for using a novel method of neuroimaging, magnetic resonance spectroscopy (MRspec), to investigate the brains of adults with DLD. We used MRspec to measure the neurotransmitter levels in regions associated with language guided by existing functional and structural findings about the DLD brain. Adult participants were recruited via survey and identified using the Fidler test. We scanned the head of the caudate nucleus and the inferior frontal gyrus in both hemispheres. I identified metabolites in those regions and am testing their possible language skill correlations. We expect, even with minimal data, to find lower concentrations of choline and glutamate and elevated concentrations of GABA in individuals with DLD compared to TD participants. Additionally, because choline chloride is linked to memory and poorer verbal and nonverbal working memory is associated with DLD, we anticipate a lower level in the caudate head. GABA may be at a higher level because it is inhibitory, which means it slows down messages in the nervous system, which may lead to difficulty processing and producing language. Inversely, we expect lower levels of glutamate as an excitatory neurotransmitter. I selected our software, TARQUIN, for processing and conducted analyses on MR data throughout the project. Ongoing analysis includes a visual reference and quantitative data to compare between participants. This study is the baseline for future research exploring neurotransmitters in adult individuals with DLD. Our results help better understand why specific language difficulties exist and how clinicians can help.

Nanoparticles are drug delivery carriers on the nanometer-length scale, and are promising targeted drug delivery solutions due to their small size and tailorability. However, current materials used to produce nanoparticles are synthetic and typically lead to large amounts of chemical waste and high costs. To explore more sustainable technologies, the Nance and Roumeli labs established a novel bacterial cellulose nanoparticle (BCNP) platform. BCNPs are formulated with a bacteria that produces cellulose and no byproducts when cultured, allowing for less reagents required and non-toxic biodegradable wastes. To be comparable to synthetic nanoparticles as a drug delivery platform, BCNPs must load and release drugs and be biocompatible with mammalian cells. In this project, I explored the tunability of BCNPs through size modification, performed cytotoxicity studies on a microglial cell line, and carried out drug loading studies. I found that higher mixing speeds during BC culturing led to a smaller BCNP size and variable particle concentration. Through cytotoxicity analysis in cell culture, I showed BCNPs were not toxic. Ongoing studies are assessing BCNP cytotoxicity as a function of BCNP dose. To demonstrate drug loading, I am incorporating catalase, an enzyme with the ability to mitigate oxidative stress markers, into BCNPs to analyze their efficacy in an in vitro model of oxidative injury. These results show BCNPs have the potential to become a sustainable nanomedicine platform and provide an important step towards reducing the environmental impact of synthetic nanoparticles.

Type 1 Diabetes is characterized by a dysfunctional response of the immune system, with our project focusing on CD8+ T-cells. Studying epigenomics provides us with information about differential gene expression, as well as distal enhancers and their targets. Understanding this genetic background enables more efficient means of treatment. In this paper, we look at three different kinds of sequencing: ATACseq, RNAseq, and Hi-C. Our focus up until this point has primarily rested upon the first, as we have used it to analyze chromatin accessibility across the genome in patients with T1D and healthy controls. To do so, we have found peaks within the reads for both demographics, which we then used to examine the individual peaks for each subject and the consensus peaks between each condition to see which are especially prominent. These peaks are then the focus of our differential expression analysis, which will allow us to understand the areas of significance and perform further exploration: variance calling and footprinting. As we continue with this project, we hope that RNAseq and Hi-C will provide us with information on gene expression levels and the physical structure of chromatin, respectively. The former was run and sequenced within our lab, but the latter is pre-existing data we will be drawing from for this analysis. Understanding the regulatory landscape allows for better informed treatments, not just for T1D but for autoimmune diseases as a whole.