Development and validation of a risk prediction model for gestational diabetes mellitus in women of advanced maternal age during the first trimester.

With the global rise in advanced maternal age (AMA) pregnancies, the risk of gestational diabetes mellitus (GDM) increases. However, few GDM prediction models are tailored for AMA women. This study aims to develop a practical risk prediction model for GDM in AMA women.

Inducible engineering precursor metabolic flux for synthesizing hyaluronic acid of customized molecular weight in Streptococcus zooepidemicus.

Background: Hyaluronic acid (HA) is extensively employed in various fields such as medicine, cosmetics, food, etc. The molecular weight (MW) of HA is crucial for its biological functions. Streptococcus zooepidemicus, a prominent HA industrial producer, naturally synthetizes HA with high MW.

Sources identification and health risk assessment of heavy metals in total suspended particulates (TSP) in a geochemical anomaly area influenced by historical indigenous zinc smelting activities.

The superposition of heavy metals (HMs) from multiple anthropogenic sources in geochemical anomaly areas makes it difficult to discriminate prime sources in atmospheric HMs. This study utilized a combination of microscopic features, positive matrix factorisation, and Pb isotope fingerprints to trace the main sources of HMs bound to total suspended particulates (TSP) at a pollution site (Msoshui: MS) and control site (Lushan: LS) in northwestern Guizhou. The results reveal that the concentrations of Cd, Pb, Cr, As, Cu, Ni, and Zn in the TSP of LS are 3.

Abnormal sensory processing cortex in insomnia disorder: a degree centrality study.

Insomnia disorder is a significant global health concern. This research aimed to explore the pathogenesis of insomnia disorder using static and dynamic degree centrality methods at the voxel level. A total of 29 patients diagnosed with insomnia disorder and 28 healthy controls were ultimately included to examine differences in degree centrality between the two groups.

The association between metabolomic profiles of lifestyle and the latent phase of incident chronic kidney disease in the UK Population.

Chronic kidney disease (CKD) is a global health challenge associated with lifestyle factors such as diet, alcohol, BMI, smoking, sleep, and physical activity. Metabolomics, especially nuclear magnetic resonance(NMR), offers insights into metabolic profiles' role in diseases, but more research is needed on its connection to CKD and lifestyle factors. Therefore, we utilized the latest metabolomics data from the UK Biobank to explore the relationship between plasma metabolites and lifestyle factors, as well as to investigate the associations between various factors, including lifestyle-related metabolites, and the latent phase of CKD onset.