Humans are exposed to a multitude of environmental chemical mixtures (ECMs) in daily life that may influence depression risk. While prior studies have shown individual ECM exposures to depression, the cumulative and interactive effects of multiple co...
Accurate detection of somatic variants in tumors is of critical importance and remains challenging. Current methods typically require matched normal samples for reliable detection, which are often unavailable in real-world research and clinical scena...
Depression, which is increasingly prevalent among older adults, has traditionally been diagnosed through symptom-based questionnaires. However, emerging evidence suggests that retinal changes could serve as objective biomarkers for depression. In thi...
While previous studies have reported functional abnormalities in the prefrontal-limbic-subcortical circuit, the treatment effects on this activity remain unclear. This longitudinal study aimed to investigate spontaneous brain activity in bipolar diso...
BACKGROUND: Coronary artery disease (CAD) is one of the biggest causes of mortality worldwide. Risk stratification for early detection is essential for the primary prevention of CAD. QRISK3 is known to overestimate future CAD risk in some populations...
BACKGROUND: Atrial fibrillation (AF) is a prevalent arrhythmia associated with adverse outcomes, often presenting paroxysmally. The lack of an efficient method to promptly detect paroxysmal AF and the absence of a unified screening approach necessita...
PURPOSE: This study evaluated the dynamic changes in the tumor microenvironment (TME) in patients with non-small cell lung cancer (NSCLC) and acquired resistance to epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) using an ar...
BACKGROUND: Epilepsy affects approximately 50 million people globally and imposes a substantial clinical and societal burden, requiring continuous and personalized monitoring for effective management. Wearable artificial intelligence (AI) technologie...
BACKGROUND: Mental health-related artificial intelligence (MH-AI) systems are proliferating across consumer and clinical contexts, outpacing regulatory frameworks and raising urgent questions about safety, accountability, and clinical integration. Re...
BACKGROUND: The ability to perform complex tasks has seen artificial intelligence (AI) used to support radiology in clinical settings, including lung cancer detection and diagnosis. Evidence suggests that AI can contribute to accurate diagnosis, redu...
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