The Redox-Sensing Regulator Rex Contributes to the Virulence and Oxidative Stress Response of Streptococcus suis Serotype 2

Streptococcus suis serotype 2 (SS2) is an important zoonotic pathogen responsible for septicemia and meningitis. The redox-sensing regulator Rex has been reported to play critical roles in the metabolism regulation, oxidative stress response, and virulence of various pathogens. In this study, we identified and characterized a Rex ortholog in the SS2 virulent strain SS2-1 that is involved in bacterial pathogenicity and stress environment susceptibility. Our data show that the Rex-knockout mutant strain Δrex exhibited impaired growth in medium with hydrogen peroxide or a low pH compared with the wildtype strain SS2-1 and the complementary strain CΔrex. In addition, Δrex showed a decreased level of survival in whole blood and in RAW264.7 macrophages. Further analyses revealed that Rex deficiency significantly attenuated bacterial virulence in an animal model. A comparative proteome analysis found that the expression levels of several proteins involved in virulence and oxidative stress were significantly different in Δrex compared with SS2-1. Electrophoretic mobility shift assays revealed that recombinant Rex specifically bound to the promoters of target genes in a manner that was modulated by NADH and NAD+. Taken together, our data suggest that Rex plays critical roles in the virulence and oxidative stress response of SS2

Extracting medications and associated adverse drug events using a natural language processing system combining knowledge base and deep learning

Abstract Objective Detecting adverse drug events (ADEs) and medications related information in clinical notes is important for both hospital medical care and medical research. We describe our clinical natural language processing (NLP) system to automatically extract medical concepts and relations related to ADEs and medications from clinical narratives. This work was part of the 2018 National NLP Clinical Challenges Shared Task and Workshop on Adverse Drug Events and Medication Extraction. Materials and Methods The authors developed a hybrid clinical NLP system that employs a knowledge-based general clinical NLP system for medical concepts extraction, and a task-specific deep learning system for relations identification using attention-based bidirectional long short-term memory networks. Results The systems were evaluated as part of the 2018 National NLP Clinical Challenges challenge, and our attention-based bidirectional long short-term memory networks based system obtained an F-measure of 0.9442 for relations identification task, ranking fifth at the challenge, and had &amp;lt;2% difference from the best system. Error analysis was also conducted targeting at figuring out the root causes and possible approaches for improvement. Conclusions We demonstrate the generic approaches and the practice of connecting general purposed clinical NLP system to task-specific requirements with deep learning methods. Our results indicate that a well-designed hybrid NLP system is capable of ADE and medication-related information extraction, which can be used in real-world applications to support ADE-related researches and medical decisions. </jats:sec