The effects of temperature variability on ischemic heart disease mortality in Hangzhou, China.

Ischemic heart disease (IHD) is a leading cause of death in cardiovascular patients. In China, the disease burden of IHD deaths has significantly increased. One of the main influencing factors of IHD is changing climates, and temperature and diurnal temperature range (DTR) are important indicators of climate change.

Impact of Microplastic Exposure on Blood Glucose Levels and Gut Microbiota: Differential Effects under Normal or High-Fat Diet Conditions.

Microplastics are emerging pollutants that have garnered significant attention, with evidence suggesting their association with the pathogenesis of type 2 diabetes mellitus. In order to assess the impact of polystyrene microplastic exposure on alterations in the gut microbiota and the subsequent implications for glucose dysregulation under different dietary conditions in mice, we investigated the effects and disparities in the blood glucose levels induced by polystyrene microplastic exposure in mice fed a high-fat diet versus those fed a normal diet. Using 16S rRNA sequencing and bioinformatics analyses, we explored the dynamic changes and discrepancies in the gut microbiota stability induced by polystyrene microplastic exposure under varied dietary conditions, and we screened for gut genera associated with the potential of polystyrene microplastics to disrupt glucose homeostasis.

Emerging Contaminants: An Emerging Risk Factor for Diabetes Mellitus.

Emerging contaminants have been increasingly recognized as critical determinants in global public health outcomes. However, the intricate relationship between these contaminants and glucose metabolism remains to be fully elucidated. The paucity of comprehensive clinical data, coupled with the need for in-depth mechanistic investigations, underscores the urgency to decipher the precise molecular and cellular pathways through which these contaminants potentially mediate the initiation and progression of diabetes mellitus.

A Novel EMG-Based Hand Gesture Recognition Framework Based on Multivariate Variational Mode Decomposition.

Surface electromyography (sEMG) is a kind of biological signal that records muscle activity noninvasively, which is of great significance in advanced human-computer interaction, prosthetic control, clinical therapy, and biomechanics. However, the number of hand gestures that can be recognized is limited and the recognition accuracy needs to be further improved. These factors lead to the fact that sEMG products are not widely used in practice.