Gestational diabetes mellitus (GDM) leads to poor pregnancy outcomes and fetoplacental endothelial dysfunction; however, the underlying mechanisms remain unknown. This study aimed to investigate the effect of placenta-derived exosomal miRNAs on fetoplacental endothelial dysfunction in GDM, as well as to further explore the role of chemerin to this end. Placenta-derived exosomal miR-140-3p and miR-574-3p expression (next-generation sequencing, quantitative real-time PCR), its interactions with cell function (Cell Counting Kit-8, Transwell, tube formation assay), chemerin interactions (Western blotting), and placental inflammation (immunofluorescence staining, enzyme-linked immunosorbent assay) were investigated. Placenta-derived exosomal miR-140-3p and miR-574-3p were downregulated in GDM. Additionally, miR-140-3p and miR-574-3p inhibited the proliferation, migration, and tube formation ability of umbilical vein endothelial cells by targeting vascular endothelial growth factor. Interestingly, miR-140-3p and miR-574-3p expression levels were negatively correlated with chemerin, which induced placental inflammation through the recruitment of macrophage cells and release of IL-18 and IL-1β. These findings indicate that chemerin reduces placenta-derived exosomal miR-140-3p and miR-574-3p levels by inducing placental inflammation, thereby promoting the proliferation, migration, and tube formation of umbilical vein endothelial cells in GDM, providing a novel perspective on the underlying pathogenesis and therapeutic targets for GDM and its offspring complications.

Water is one of the largest resources on earth. People need water to sustain life, including drinking water. It is important to know whether drinking water - human life resource - is enough for everyone now and in the future. However, water resources are not evenly distributed everywhere on the planet. While the water resource is rich in some countries and regions, it is not enough for some other regions. The analysis of different region’s water resources should be done individually. In this paper, the authors analyze the potability of water by using an Indian water potability dataset from Kaggle. More specifically, this paper talks about each factor of water that influences water potability through statistical methods - binomial distribution and the k-nearest neighbor algorithm. Also, the authors build a model that allows people to predict the potability of a water resource by the data of each factor of that resource. According to the research, the features of water are not related to each other. All the features should meet a specific standard in order to get potable water.