This study is the first to confirm, in a large-scale prospective cohort, that fish oil supplementation can reduce the risk of developing type 2 diabetes (T2D) in individuals with prediabetes. It systematically reveals that DHA and EPA improve glucose homeostasis in prediabetic individuals by enhancing aerobic glucose metabolism, glycogen synthesis, and GLUT4 vesicle translocation in skeletal muscle, thereby lowering the risk of T2D onset.

A groundbreaking study titled "An Engineered Intravitreal Injection Retinal-Pigment-Epithelium-Tropic Adeno-Associated Virus Vector Expressing a Bispecific Antibody Binding VEGF-A and ANG-2 Rescues Neovascular Age-Related Macular Degeneration in Animal Models and Patients" has unveiled a promising new gene therapy approach for treating nAMD. 

The authors comprehensively elaborate on the diversity and complexity of ILs' roles in AKI pathogenesis.  The review also describes how ILs coordinate immune cell activation and mediate crosstalk between these cells and renal parenchymal cells, establishing a balance between pro-inflammatory/anti-inflammatory responses and injury/repair processes.  

Self-driving cars know their own way in unpredictable traffic, thanks to path planning technology. Among current AI-driven efforts to make path planning more efficient and reliable, a research team has developed an optimization framework proven especially effective in uncertain environments. The results were published June 3 under the title “Action-Curiosity-Based Deep Reinforcement Learning Algorithm for Path Planning in a Nondeterministic Environment” in Intelligent Computing, a Science Partner Journal.

Decoding cosmic evolution depends on accurately predicting the complex chemical reactions in the harsh environment of space. Traditional methods for such predictions rely heavily on costly laboratory experiments or expert knowledge, both of which are resource-intensive and limited in scope. Recently, a research team developed an innovative AI tool that predicts astrochemical reactions with high accuracy and efficiency, demonstrating that deep learning techniques can successfully address data limitations in astrochemistry. Titled “A Two-Stage End-to-End Deep Learning Approach for Predicting Astrochemical Reactions,” this research was published May 15 in Intelligent Computing, a Science Partner Journal.