Safety of biologic agents for the management of rheumatic diseases during pregnancy.

Purpose Of Review: To discuss the current understanding regarding the use of biologic therapeutics in pregnancy.

Recent Findings: Our understanding of the mechanisms underlying the potential fetal and infant exposure to biologics as well as a growing body of empirical evidence from real world use of biologics in pregnancy have demonstrated that biologics are generally compatible preconception and during pregnancy. Long-term effects of exposure to biologic agents in utero are not known, but will be uncovered in time.

An Oral Inoculation Infant Rabbit Model for Infection.

species cause diarrheal disease globally. Shigellosis is typically characterized by bloody stools and colitis with mucosal damage and is the leading bacterial cause of diarrheal death worldwide. After the pathogen is orally ingested, it invades and replicates within the colonic epithelium through mechanisms that rely on its type III secretion system (T3SS).

A Conserved Streptococcal Virulence Regulator Controls the Expression of a Distinct Class of M-Like Proteins.

subspecies (SEZ) are group C streptococci that are important pathogens of economically valuable animals such as horses and pigs. Here, we found that many SEZ isolates bind to a monoclonal antibody that recognizes poly-acetylglucosamine (PNAG), a polymer that is found as a surface capsule-like structure on diverse microbes. A fluorescence-activated cell sorting-based transposon insertion sequencing (Tn-seq) screen, coupled with whole-genome sequencing, was used to search for genes for PNAG biosynthesis.

Unsupervised Learning Approach for Comparing Multiple Transposon Insertion Sequencing Studies.

Transposon insertion sequencing (TIS) is a widely used technique for conducting genome-scale forward genetic screens in bacteria. However, few methods enable comparison of TIS data across multiple replicates of a screen or across independent screens, including screens performed in different organisms. Here, we introduce a analytic framework, comparative TIS (CompTIS), which utilizes unsupervised learning to enable meta-analysis of multiple TIS data sets.