Development of high-resolution melting (HRM) assay to differentiate the species of Shigella isolates from stool and food samples

Shigella species, a group of intracellular foodborne pathogens, are the main causes of bacillary dysentery and shigellosis in humans worldwide. It is essential to determine the species of Shigella in outbreaks and food safety surveillance systems. The available immunological and molecular methods for identifying Shigella species are relatively complicated, expensive and time-consuming. High resolution melting (HRM) assay is a rapid, cost-effective, and easy to perform PCR-based method that has recently been used for the differentiation of bacterial species. In this study, we designed and developed a PCR-HRM assay targeting rrsA gene to distinguish four species of 49 Shigella isolates from clinical and food samples and evaluated the sensitivity and specificity of the assay. The assay demonstrated a good analytical sensitivity with 0.01–0.1 ng of input DNA template and an analytical specificity of 100% to differentiate the Shigella species. The PCR-HRM assay also was able to identify the species of all 49 Shigella isolates from clinical and food samples correctly. Consequently, this rapid and user-friendly method demonstrated good sensitivity and specificity to differentiate species of the Shigella isolates from naturally contaminated samples and has the potential to be implemented in public health and food safety surveillance systems.

Broadening the reach of the FDA Sentinel system: A roadmap for integrating electronic health record data in a causal analysis framework

The Sentinel System is a major component of the United States Food and Drug Administration’s (FDA) approach to active medical product safety surveillance. While Sentinel has historically relied on large quantities of health insurance claims data, leveraging longitudinal electronic health records (EHRs) that contain more detailed clinical information, as structured and unstructured features, may address some of the current gaps in capabilities. We identify key challenges when using EHR data to investigate medical product safety in a scalable and accelerated way, outline potential solutions, and describe the Sentinel Innovation Center’s initiatives to put solutions into practice by expanding and strengthening the existing system with a query-ready, large-scale data infrastructure of linked EHR and claims data. We describe our initiatives in four strategic priority areas: (1) data infrastructure, (2) feature engineering, (3) causal inference, and (4) detection analytics, with the goal of incorporating emerging data science innovations to maximize the utility of EHR data for medical product safety surveillance.