Gestational diabetes mellitus (GDM) is a common complication of pregnancy, which has significant adverse effects on both the mother and fetus. The incidence of GDM is increasing globally, and early diagnosis is critical for timely treatment and reducing the risk of poor pregnancy outcomes. GDM is usually diagnosed and detected after 24 weeks of gestation, while complications due to GDM can occur much earlier. Copy number variations (CNVs) can be a possible biomarker for GDM diagnosis and screening in the early gestation stage. In this study, we proposed a machine-learning method to screen GDM in the early stage of gestation using cell-free DNA (cfDNA) sequencing data from maternal plasma. Five thousand and eighty-five patients from north regions of Mainland China, including 1942 GDM, were recruited. A non-overlapping sliding window method was applied for CNV coverage screening on low-coverage (~0.2×) sequencing data. The CNV coverage was fed to a convolutional neural network with attention architecture for the binary classification. The model achieved a classification accuracy of 88.14%, precision of 84.07%, recall of 93.04%, F1-score of 88.33% and AUC of 96.49%. The model identified 2190 genes associated with GDM, including DEFA1, DEFA3 and DEFB1. The enriched gene ontology (GO) terms and KEGG pathways showed that many identified genes are associated with diabetes-related pathways. Our study demonstrates the feasibility of using cfDNA sequencing data and machine-learning methods for early diagnosis of GDM, which may aid in early intervention and prevention of adverse pregnancy outcomes.

Bisphenol A (BPA), a common endocrine-disrupting chemical (EDC), has been revealed to be closely associated with the induction of abnormal heart development, obesity, prediabetes, and other metabolic disorders. However, the underlying mechanism of maternal BPA exposure on fetal heart development abnormalities is not clear. To explore the adverse effects of BPA and its potential mechanism on heart development, C57BL/6 J mice and human cardiac AC-16 cells were used to conduct in vivo and in vitro studies. For the in vivo study, mice were exposed to low-dose BPA (40 mg/(kg·bw)) and high-dose BPA (120 mg/(kg·bw)) for 18 d during pregnancy. In vitro study, human cardiac AC-16 cells were exposed to BPA of various concentrations (0.01, 0.1, 1, 10, and 100 µM) for 24 h. Cell viability and ferroptosis were evaluated using 2,5-diphenyl-2 H-tetrazolium bromide (MTT), immunofluorescence staining, and western blotting. In BPA-treated mice, the alterations of fetal cardiac structure were observed. Increased NK2 homeobox 5(Nkx2.5) was detected in vivo with the induction of ferroptosis, revealing that BPA induced abnormal fetal heart development. Furthermore, the results showed that SLC7A11 and SLC3A2 decreased in low- and high-dose BPA-treated groups, suggesting that system Xc- mediated BPA-induced abnormal fetal heart development via inhibiting the expression of GPX4. Observing AC-16 cells confirmed that cell viability declined significantly with various concentrations of BPA. Moreover, BPA exposure suppressed GPX4 expression by inhibiting System Xc- (the decrease of SLC3A2 and SLC7A11). Collectively, system Xc- modulating cell ferroptosis might play important in abnormal fetal heart development induced by BPA exposure.

Dystrophic epidermolysis bullosa (DEB) is a series of severe genetic conditions affecting skin and nails caused by mutations in the COL7A1 gene. DEB has a strong phenotypic variability. In the present study, we recruited a case with a boy exhibiting typical DEB indication, and performed a clinical, genetic, and experimental investigation, followed by a prenatal diagnosis on their current pregnancy. Whole exome sequencing identified a novel compound heterozygous variation in COL7A1, consisting of two variants, namely c.191T>C (p.Leu64Pro) and c.5124G>A (p.Leu1708=) in the proband. In vitro study by minigene system indicated that c.5124G>A would result in an increased ratio of a transcript with exon‐skipping, which supported its pathogenicity. Further prenatal detection confirmed the genotype–phenotye co‐separation in this family. In conclusion, the findings in our study expanded the mutation spectrum of DEB, and emphasized the importance of paying attention to specific synonymous variants in the filtering process.