Microplastic and nanoplastic exposure and risk of diabetes mellitus.

The issue of plastic pollutants has become a growing concern. Both microplastics (MPs) (particle size < 5 mm) and nanoplastics (NPs) (particle size < 1 µm) can cause DNA damage, cytotoxicity, and oxidative stress in various organisms. The primary known impacts of microplastic/nanoplastic are observed in the liver and respiratory system, leading to hepatotoxicity and chronic obstructive pulmonary disease.

Development and Validation of the Scale of Hypoglycemia Self-Care Behavior in Type 2 Diabetes.

Background: Inappropriate dietary, exercise, and medication self-care behaviors among persons with diabetes can easily trigger hypoglycemia. Clinically, it is necessary to quickly identify high-risk groups for hypoglycemic events to provide targeted hypoglycemia education. However, there is currently a lack of precise tools to assess self-care behaviors related to hypoglycemia.

Manipulating Mg/Ca ratios in MgO-CaO-SiO bioactive glass for achieving accelerated osteogenic differentiation of human adipose-derived stem cells.

Cell-containing biomaterial is a promising material for treating nonunion or critical bone defect. Human adipose-derived stem cells (hADSCs) are suitable for bone repair due to their abundance in the abdomen, thighs, and buttocks. However, the low osteogenic capacities of hADSCs hinder their extended development for bone regeneration application.

The Impact of Administrative Districts and Urban Landscape on the Dispersal of Aedes aegypti via Genetic Differentiation.

Mosquito-borne diseases affect millions and cause numerous deaths annually. Effective vector control, which hinges on understanding their dispersal, is vital for reducing infection rates. Given the variability in study results, likely due to environmental and human factors, gathering local dispersal data is critical for targeted disease control.

Comparison of algorithms using deep reinforcement learning for optimization of hyperbolic metamaterials.

A hyperbolic metamaterial absorber has great potential for improving the performance of photo-thermoelectric devices targeting heat sources owing to its broadband absorption. However, optimizing its geometry requires considering numerous parameters to achieve absorption that aligns with the radiation spectrum. Here, we compare three algorithms using deep reinforcement learning for the optimization of a hyperbolic metamaterial absorber.