The neural crest (NC) is a multipotent and temporarily migratory cell population stemming from the dorsal neural tube during vertebrate embryogenesis. Cardiac neural crest cells (NCCs), a specified subpopulation of the NC, are vital for normal cardiovascular development, as they significantly contribute to the pharyngeal arch arteries, the developing cardiac outflow tract (OFT), cardiac valves, and interventricular septum. Various signaling pathways are shown to orchestrate the proper migration, compaction, and differentiation of cardiac NCCs during cardiovascular development. Any loss or dysregulation of signaling pathways in cardiac NCCs can lead to abnormal cardiovascular development during embryogenesis, resulting in abnormalities categorized as congenital heart defects (CHDs). This review focuses on the contributions of cardiac NCCs to cardiovascular formation, discusses cardiac defects caused by a disruption of various regulatory factors, and summarizes the role of multiple signaling pathways during embryonic development. A better understanding of the cardiac NC and its vast regulatory network will provide a deeper insight into the mechanisms of the associated abnormalities, leading to potential therapeutic advancements.

Bronchopulmonary dysplasia (BPD) is the most common lung disease of extreme prematurity, yet mechanisms that associate with or identify neonates with increased susceptibility for BPD are largely unknown. Combining artificial intelligence with gene expression data is a novel approach that may assist in better understanding mechanisms underpinning chronic lung disease and in stratifying patients at greater risk for BPD. The objective of this study is to develop an early peripheral blood transcriptomic signature that can predict preterm neonates at risk for developing BPD. Secondary analysis of whole blood microarray data from 97 very low birth weight neonates on day of life 5 was performed. BPD was defined as positive pressure ventilation or oxygen requirement at 28 days of age. Participants were randomly assigned to a training (70%) and testing cohort (30%). Four gene-centric machine learning models were built, and their discriminatory abilities were compared with gestational age or birth weight. This study adheres to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) statement. Neonates with BPD ( n = 62 subjects) exhibited a lower median gestational age (26.0 wk vs. 30.0 wk, P < 0.01) and birth weight (800 g vs. 1,280 g, P < 0.01) compared with non-BPD neonates. From an initial pool (33,252 genes/patient), 4,523 genes exhibited a false discovery rate (FDR) <1%. The area under the receiver operating characteristic curve (AUC) for predicting BPD utilizing gestational age or birth weight was 87.8% and 87.2%, respectively. The machine learning models, using a combination of five genes, revealed AUCs ranging between 85.8% and 96.1%. Pathways integral to T cell development and differentiation were associated with BPD. A derived five-gene whole blood signature can accurately predict BPD in the first week of life.

The current state of prenatal detection of genetic conditions in congenital heart defects Tina O. Findley, Hope Northrup