Recombinant human chorionic gonadotropin and gonadotropin-releasing hormone agonist differently affect the profile of extracellular vesicle microRNAs in human follicular fluid.

PURPOSE: To compare the expression profile of extracellular vesicle microRNAs (EV-miRNAs) derived from follicular fluid after a trigger with recombinant human chorionic gonadotropin (r-hCG) or with a gonadotropin-releasing hormone GnRH agonist (GnRH-a) for final oocyte maturation. METHODS: A retrospective analysis of a prospective cohort. Women undergoing in vitro fertilization at a tertiary university-affiliated hospital were recruited between 2014 and 2016. EV-miRNAs were extracted from the follicular fluid of a single follicle, and their expression was assessed using TaqMan Open Array®. Genes regulated by EV-miRNAs were analyzed using miRWalk2.0 Targetscan database, DAVID Bioinformatics Resources, Kyoto-Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO). RESULTS: Eighty-two women were included in the r-hCG trigger group and 9 in the GnRH-a group. Of 754 EV-miRNAs screened, 135 were detected in at least 50% of the samples and expressed in both groups and were further analyzed. After adjusting for multiple testing, 41 EV-miRNAs whose expression levels significantly differed between the two trigger groups were identified. Bioinformatics analysis of the genes regulated by these EV-miRNAs showed distinct pathways between the two triggers, including TGF-beta signaling, cell cycle, and Wnt signaling pathways. Most of these pathways regulate cascades associated with apoptosis, embryo development, implantation, decidualization, and placental development. CONCLUSIONS: Trigger with GnRH-a or r-hCG leads to distinct EV-miRNAs expression profiles and to downstream biological effects in ovarian follicles. These findings may provide an insight for the increased apoptosis and the lower implantation rates following GnRH-a trigger vs. r-hCG in cases lacking intensive luteal phase support.

Prediction of Shigellosis outcomes in Israel using machine learning classifiers.

Shigellosis causes significant morbidity and mortality in developing and developed countries, mostly among infants and young children. The World Health Organization estimates that more than one million people die from Shigellosis every year. In order to evaluate trends in Shigellosis in Israel in the years 2002-2015, we analysed national notifiable disease reporting data. Shigella sonnei was the most commonly identified Shigella species in Israel. Hospitalisation rates due to Shigella flexenri were higher in comparison with other Shigella species. Shigella morbidity was higher among infants and young children (age 0-5 years old). Incidence of Shigella species differed among various ethnic groups, with significantly high rates of S. flexenri among Muslims, in comparison with Jews, Druze and Christians. In order to improve the current Shigellosis clinical diagnosis, we developed machine learning algorithms to predict the Shigella species and whether a patient will be hospitalised or not, based on available demographic and clinical data. The algorithms' performances yielded an accuracy of 93.2% (Shigella species) and 94.9% (hospitalisation) and may consequently improve the diagnosis and treatment of the disease.