Molecular mimicry, where foreign and self-peptides contain similar epitopes, can induce autoimmune responses. Identifying potential molecular mimics and studying their properties is key to understanding the onset of autoimmune diseases such as type 1 diabetes mellitus (T1DM). Previous work identified pairs of infectious epitopes (EINF) and T1DM epitopes (ET1D) that demonstrated sequence homology; however, structural homology was not considered. Correlating sequence homology with structural properties is important for streamlining translational investigation of potential molecular mimics. Therefore, the purpose of this work is to compare sequence homology with structural homology by calculating the structures and electrostatic potentials of 35 pairs of epitopes identified in previous work from our laboratory. For each epitope pair the root mean square deviation (RMSD) was calculated between their predicted structures and their electrostatic potentials were compared. Structures were predicted using the AlphaFold and I-TASSER software programs. We considered a structural match of EINF and ET1D pairs successful if the RMSD wasINF/ET1Dstructurally unmatched pairs. Despite structural differences, these four EINF/ET1Dpairs show similar electrostatic distributions, indicating that they may still bind to the same protein targets, major histocompatibility complex molecules, for T1DM. These findings suggest that searching for epitope pairs using sequence homology, a much less computationally demanding approach, leads to strong candidates for further study.

OBJECTIVE: The Recruitment Innovation Center (RIC), partnering with the Trial Innovation Network and institutions in the National Institutes of Health-sponsored Clinical and Translational Science Awards (CTSA) Program, aimed to develop a service line to retrieve study population estimates from electronic health record (EHR) systems for use in selecting enrollment sites for multicenter clinical trials. Our goal was to create and field-test a low burden, low tech, and high-yield method. MATERIALS AND METHODS: In building this service line, the RIC strove to complement, rather than replace, CTSA hubs' existing cohort assessment tools. For each new EHR cohort request, we work with the investigator to develop a computable phenotype algorithm that targets the desired population. CTSA hubs run the phenotype query and return results using a standardized survey. We provide a comprehensive report to the investigator to assist in study site selection. RESULTS: From 2017 to 2020, the RIC developed and socialized 36 phenotype-dependent cohort requests on behalf of investigators. The average response rate to these requests was 73%. DISCUSSION: Achieving enrollment goals in a multicenter clinical trial requires that researchers identify study sites that will provide sufficient enrollment. The fast and flexible method the RIC has developed, with CTSA feedback, allows hubs to query their EHR using a generalizable, vetted phenotype algorithm to produce reliable counts of potentially eligible study participants. CONCLUSION: The RIC's EHR cohort assessment process for evaluating sites for multicenter trials has been shown to be efficient and helpful. The model may be replicated for use by other programs.

Drug Eluting Stents (DES) have distinct advantages over other Percutaneous Coronary Intervention procedures, but have recently been associated with the development of serious complications after the procedure. There is a growing need for understanding the risk of these complications, which has led to the development of simple statistical models. In this work, we have developed a predictive model based on Support Vector Machines on a real world live dataset consisting of clinical variables of patients being treated at a cardiac care facility to predict the risk of complications at 12 months following a DES procedure. A significant challenge in this work, common to most clinical machine learning datasets, was imbalanced data, and our results showed the effectiveness of the Synthetic Minority Over-sampling Technique (SMOTE) to address this issue. The developed predictive model provided an accuracy of 94% with a 0.97 AUC (Area under ROC curve), indicating high potential to be used as a decision support for management of patients following a DES procedure in real-world cardiac care facilities.