DGCL: dual-graph neural networks contrastive learning for molecular property prediction.

In this paper, we propose DGCL, a dual-graph neural networks (GNNs)-based contrastive learning (CL) integrated with mixed molecular fingerprints (MFPs) for molecular property prediction. The DGCL-MFP method contains two stages. In the first pretraining stage, we utilize two different GNNs as encoders to construct CL, rather than using the method of generating enhanced graphs as before.

Using machine learning to develop a five-item short form of the children's depression inventory.

Background: Many adolescents experience depression that often goes undetected and untreated. Identifying children and adolescents at a high risk of depression in a timely manner is an urgent concern. While the Children's Depression Inventory (CDI) is widely utilized in China, it lacks a localized revision or simplified version.

The dynamic nexus: exploring the interplay of BMI before, during, and after pregnancy with Metabolic Syndrome (MetS) risk in Chinese lactating women.

Background And Aim: The health implications of BMI and MetS in lactating women are significant. This study aims to investigate the relationship between risk of Mets in lactation and BMI in four stages: pre-pregnancy, prenatal period, 42 days postpartum, and current lactation.

Methods And Results: A total of 1870 Lactating Women within 2 years after delivery were included from "China Child and Lactating Mother Nutrition Health Surveillance (2016-2017)".

Heat shock protein B7 (HSPB7) inhibits lung adenocarcinoma progression by inhibiting glycolysis.

In the present study, it was aimed to investigate the effects and potential mechanisms of heat shock protein B7 (HSPB7) on lung adenocarcinoma (LUAD). Bioinformatic analysis was performed to explore the association between HSPB7 expression and patients with LUAD. MTT, colony formation, wound healing and Transwell assays were performed to examine the proliferative, migratory and invasive abilities of H1975 and A549 cells.