One of the largest problems facing modern medicine is drug resistance. Many classes of drugs can be rendered ineffective if their target is able to acquire beneficial mutations. While this is an excellent showcase of the power of evolution, it necessitates the development of increasingly stronger drugs to combat resistant pathogens. Not only is this strategy costly and time consuming, it is also unsustainable. To contend with this problem, many multi-drug treatment strategies are being explored. Previous studies have shown that resistance to some drug combinations is not possible, for example, resistance to a common antifungal drug, fluconazole, seems impossible in the presence of radicicol. We believe that in order to understand the viability of multi-drug strategies in combating drug resistance, we must understand the full spectrum of resistance mutations that an organism can develop, not just the most common ones. It is possible that rare mutations exist that are resistant to both drugs. Knowing the frequency of such mutations is important for making predictions about how problematic they will be when multi-drug strategies are used to treat human disease. This experiment aims to expand on previous research on the evolution of drug resistance in S. cerevisiae by using molecular barcodes to track ~100,000 evolving lineages simultaneously. The barcoded cells were evolved with serial transfers for seven weeks (200 generations) in three concentrations of the antifungal Fluconazole, three concentrations of the Hsp90 inhibitor Radicicol, and in four combinations of Fluconazole and Radicicol. Sequencing data was used to track barcode frequencies over the course of the evolution, allowing us to observe resistant lineages as they rise and quantify differences in resistance evolution across the different conditions. We were able to successfully observe over 100,000 replicates simultaneously, revealing many adaptive lineages in all conditions. Our results also show clear differences across drug concentrations and combinations, with the highest drug concentrations exhibiting distinct behaviors.

Seven human subjects with body mass indices (BMIs) ranging from 19.4 kg/ m2 to 26.7 kg/ m2 and six human subjects with BMIs ranging from 32.1 kg/ m2 to 37.6 kg/ m2 were recruited and subjected to 45-minute bouts of acute exercise to look at the changes in the plasma concentration of the dopamine metabolite homovanillic acid (HVA) in response to acute physical activity. Plasma HVA concentration was measured before exercise and during the last 10 minutes of the exercise bout via competitive ELISA. On average the optical density (OD) of the samples taken from lean subjects decreased from 0.203 before exercise to 0.192 during exercise, indicating increased plasma HVA concentration. In subjects with obesity OD increased from 0.210 before exercise to 0.219 during exercise, indicating reduced plasma HVA concentration. These differences in OD were not statistically significant. Between the lean group and the group with obesity no significant difference was observed between the OD of the plasma samples taken before exercise, but a significant difference (p = 0.0209) was observed between the ODs of the samples taken after exercise. This indicated that there was a significant difference between the percent changes in OD between the lean group and the group with obesity, which suggested that there may be a body weight-dependent difference in the amount of dopamine released in response to exercise. Because of the lack of significance in the changes in OD within the lean group and the group with obesity the results of this study were insufficient to conclude that this difference is not due to chance, but further investigation is warranted.

The field of biomedical research relies on the knowledge of binding interactions between various proteins of interest to create novel molecular targets for therapeutic purposes. While many of these interactions remain a mystery, knowledge of these properties and interactions could have significant medical applications in terms of understanding cell signaling and immunological defenses. Furthermore, there is evidence that machine learning and peptide microarrays can be used to make reliable predictions of where proteins could interact with each other without the definitive knowledge of the interactions. In this case, a neural network was used to predict the unknown binding interactions of TNFR2 onto LT-ɑ and TRAF2, and PD-L1 onto CD80, based off of the binding data from a sampling of protein-peptide interactions on a microarray. The accuracy and reliability of these predictions would rely on future research to confirm the interactions of these proteins, but the knowledge from these methods and predictions could have a future impact with regards to rational and structure-based drug design.

Language has a critical role as a social determinant of health and a source of healthcare disparities. Rhetorical devices are ubiquitous in medicine and are often used to persuade or inform care team members. Rhetorical devices help a healthcare team acknowledge and interpret narratives. For example, metaphors are frequently used as rhetorical devices by patients to describe cancer, including winning or losing a battle, surviving a fight, war, potentially implying that the patient feels helpless like a pawn fighting in a struggle directed by the physician, thus reducing patient autonomy and agency. However, this occidental approach is flawed because it excessively focuses on the individual's agency and marginalizes external factors, such as cultural beliefs and social support (Sontag, 1989). Although there is a large body of research about how the rhetoric of medicine affects patients in the United States, there is a lack of such research about how patient experiences' rhetoric can help increase the understanding of Latino populations' unique social determinants. This creative project aims to analyze the rhetorical differences in the description of disease amongst Latino and American communities, translating to creating an educational module for a Spanish for biomedical sciences class. The objective is to increase future healthcare professionals' ability to understand how the composition of descriptions and medical rhetoric in different mediums of humanities can serve as critical tools to analyze social determinants in Latino healthcare delivery.

CD47 is a cell surface receptor expressed on many cells in the body. It has many immune functions such as marking host cells as “self” and the activation of apoptosis through phagocytosis. Mac-1 is a major integrin on myeloid cells and has been implicated in several different macrophage immune functions. Previous studies from Dr. Ugarova’s lab demonstrated CD47 may form a complex with Mac-1 through the cis-interaction and could regulate Mac-1-dependent macrophage functions. To localize the binding site for Mac-1 in CD47, the extracellular domain of CD47 IgV was isolated as GST-fusion protein from E. coli cells. The recombinant fusion protein is being used in current studies with cell adhesion assays and immunoprecipitation to determine the complementary binding site in Mac-1.

Melanoma is one of the most severe forms of skin cancer and can be life-threatening due to metastasis if not caught early on in its development. Over the past decade, the U.S. Government added a Healthy People 2020 objective to reduce the melanoma skin cancer rate in the U.S. population. Now that the decade has come to a close, this research investigates possible large-scale risk factors that could lead to incidence of melanoma in the population using logistic regression and propensity score matching. Logistic regression results showed that Caucasians are 14.765 times more likely to get melanoma compared to non-Caucasians; however, after adjustment using propensity scoring, this value was adjusted to 11.605 times more likely for Caucasians than non-Caucasians. Cholesterol, Chronic Obstructive Pulmonary Disease, and Hypertension predictors also showed significance in the initial logistic regression. By using the results found in this experiment, the door has been opened for further analysis of larger-scale predictors and gives public health programs the initial information needed to create successful skin safety advocacy plans.