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.

Antibiotics have contributed to the decline in mortality and morbidity caused by infections, but overuse may weaken effectiveness resulting in a worldwide threat. Antibiotic overuse is correlated with adverse events like Clostridium difficile infection, antimicrobial resistance, unnecessary healthcare utilization and poor health outcomes. Long term care facility (LTCF) residents are vulnerable targets for this phenomenon as antibiotics are one of the most commonly prescribed medications in this setting. Consequently, multiple organizations mandate strategies to promote antibiotic stewardship in all healthcare sites particularly LTCFs.

To address this global issue, this doctoral project utilized the Outcomes-Focused Knowledge Translation intervention framework to provide sepsis education, promoted use of an established clinical algorithm, and engaged a communication tool for nurses and the certified nursing assistants (CNAs) thus, improving antibiotic stewardship. The project was conducted in a 5-star Medicare-rated LTCF in Mesa, AZ with a convenience sample of 22 participants. The participants received a knowledge questionnaire and Work Relationship Scale pre- and post- intervention to determine improvement.

The results show that the education provided did not improve their knowledge with a p = 0.317 for nurses while p = 0.863 for CNAs over 8 weeks. Lastly, education provided did not improve the nurses’ Work Relationship p = 0.230 or for the CNAs p = 0.689. Though not statistically significant, the intervention tools are clinically significant. Additional research is needed to identify ways to determine barriers in implementing an antibiotic stewardship program.