Browse Articles

In this study, the authors investigate associations between use of histamine H₂ receptor antagonists and suicidal ideation in different age groups.

This study investigates how caffeine and melatonin affect learning in adolescent zebrafish, serving as a model for human teens. Using an automated system to track behavior, we found that melatonin slowed learning while caffeine caused erratic, inconsistent responses, suggesting both substances can negatively impact adolescent learning patterns. These findings highlight the need for further research into their physiological effects and potential implications for human adolescents.

This study examined whether findings from laboratory studies of baseball cognition also apply during real games. Using 2024 MLB data from 30 players, researchers analyzed batting performance under different levels of feedback and anxiety.

The authors use computational methods to compare tau acetylation to the better studied tau phosphorylation in Alzheimer's disease and then design and computationally test a new drug to prevent abnormal post-translational modifications of tau.

The authors looked at different strategies to extract oil from almonds to determine which gave the best results.

The authors looked at how activation of the sympathetic nervous system impacts short-term memory.

The study explores how music and sports impact cognitive development in young children, particularly in relation to learning disorders like ADHD and dyslexia.

Coronary artery disease, the leading cause of death worldwide, results from cholesterol build-up in coronary arteries, limiting blood and oxygen flow to the heart. This study investigated the use of gold and silver nanoparticles coated with aspirin and activated by near-infrared light to improve blood flow in a clogged artery model. The nanoparticles increased simulated blood flow rates, demonstrating potential as a less invasive and more targeted treatment for cardiovascular disease.

Here, seeking to identify a possible explanation for the more frequent diagnosis of autism spectrum disorder (ASD) in males than females, they sought to investigate a potential sex bias in the expression of ASD-associated genes. Based on their analysis, they identified 17 ASD-associated candidate genes that showed stronger collective sex-dependent expression.

Here, recognizing the difficulty associated with tracking the progression of dementia, the authors used machine learning models to predict between the presence of cognitive normalcy, mild cognitive impairment, and Alzheimer's Disease, based on blood DNA methylation levels, sex, and age. With four machine learning models and two dataset dimensionality reduction methods they achieved an accuracy of 53.33%.