Single-nuclei RNA-sequencing fails to detect molecular dysregulation in the preeclamptic placenta.

Introduction: Single-cell RNA-seq (scRNA-seq) revolutionized our understanding of tissue complexity in health and disease and revealed massive transcriptional dysregulation across placental cell classes in early-onset, but not late-onset preeclampsia (PE). However, the multinucleated syncytium is largely inaccessible to cell dissociation. Nuclei isolation and single-nuclei RNA-seq may be preferable in the placenta; not least considering compatibility with long-term tissue storage.

Early childhood respiratory morbidity according to gestational age at birth: A nationwide cohort study.

Background: Preterm birth survivors are at risk for short- and long-term respiratory morbidity. This includes increased rates of chronic obstructive pulmonary disease and infectious morbidity. Previous studies showed increased utilization of healthcare services throughout early childhood.

Machine Learning in Electronic Health Records: Identifying High-Risk Obstetric Patients Pre and During Labor.

Our goal is to apply artificial intelligence (AI) and statistical analysis to understand the relationship between various factors and outcomes during pregnancy and labor and delivery, in order to personalize birth management and reduce complications for both mothers and newborns. We use a structured electronic health records database with data from approximately 130,000 births to train, test and validate our models. We apply machine learning (ML) methods to predict various obstetrical outcomes before and during labor, with the aim of improving patient care management in the delivery ward.