Identify gestational diabetes mellitus by deep learning model from cell-free DNA at the early gestation stage.

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.

Textured anti-reflection and down-conversion composite functional films for high-efficiency solar cells.

Advances in high-efficiency solar cells introduce photon management challenges, including the difficult texturization of flat surfaces and low photon utilization at short wavelengths. While bifacial crystalline silicon solar cells have a front pyramid structure and SiN x layers reduce reflections, managing photons on the flat backside remains a challenge. To enhance light utilization, a soft nanoimprint technique was utilized to create pyramid micro-structured polyurethane films doped with europium (Eu3+) complex. These films, which possess anti-reflection and down-conversion properties, can be applied externally to various high-efficiency solar cells without compromising electrical performance. Research on the backside of bifacial PERC solar cells revealed that the optimal composite functional film increases the integrated current by 5.70%, with a 1.27% gain from down-conversion effects. This specialized film presents a novel approach to interface matching for different types of solar cells.