The TICE Pathway: Mechanisms and Potential Clinical Applications.

PURPOSE OF REVIEW: Transintestinal cholesterol excretion (TICE) is a non-biliary pathway that excretes excess cholesterol from the body through feces. This article focuses on the research progress of the TICE pathway in the last few years, including the discovery process of the TICE pathway, its molecular mechanism, and potential clinical applications. RECENT FINDINGS: Cholesterol homeostasis is vital for cardiovascular diseases, stroke, and neurodegenerative diseases. Beyond the cholesterol excretion via hepatobiliary pathway, TICE contributes significantly to reverse cholesterol transport ex vivo and in vivo. Nuclear receptors are ligand-activated transcription factors that regulate cholesterol metabolism. The farnesoid X receptor (FXR) and liver X receptor (LXR) activated, respectively, by oxysterols and bile acids promote intestinal cholesterol secretion through ABCG5/G8. Nutrient regulators and intestinal flora also modulate cholesterol secretion through the TICE pathway. TICE allows direct elimination of plasma cholesterol, which may provide an attractive therapeutic targets. TICE pathway may provide a potential target to stimulate cholesterol elimination and reduce the risk of cardiovascular diseases.

Functional division of the dorsal striatum based on a graph neural network.

The dorsal striatum, an essential nucleus in subcortical areas, has a crucial role in controlling a variety of complex cognitive behaviors; however, few studies have been conducted in recent years to explore the functional subregions of the dorsal striatum that are significantly activated when performing multiple tasks. To explore the differences and connections between the functional subregions of the dorsal striatum that are significantly activated when performing different tasks, we propose a framework for functional division of the dorsal striatum based on a graph neural network model. First, time series information for each voxel in the dorsal striatum is extracted from acquired functional magnetic resonance imaging data and used to calculate the connection strength between voxels. Then, a graph is constructed using the voxels as nodes and the connection strengths between voxels as edges. Finally, the graph data are analyzed using the graph neural network model to functionally divide the dorsal striatum. The framework was used to divide functional subregions related to the four tasks including olfactory reward, "0-back" working memory, emotional picture stimulation, and capital investment decision-making. The results were further subjected to conjunction analysis to obtain 15 functional subregions in the dorsal striatum. The 15 different functional subregions divided based on the graph neural network model indicate that there is functional differentiation in the dorsal striatum when the brain performs different cognitive tasks. The spatial localization of the functional subregions contributes to a clear understanding of the differences and connections between functional subregions.