Clusterin deficiency exacerbates cholestatic liver disease through ER stress and NLRP3 inflammasome activation.

Background: Cholestatic liver disease, characterized by impaired bile flow, leads to the accumulation of harmful metabolites and toxins, resulting in liver damage. Inflammatory cytokines are crucial for the progression of this condition. Clusterin is a glycoprotein with roles in cell death, lipid transport, and cellular protection.

Effect of hydrophilic polymers on the formation of size-controllable aqueous droplets in water-in-oil emulsion and the fabrication of porous micro-silica particles therefrom.

Size-controllable droplets were formed in a water in oil (W/O) emulsion using only hydrophilic polymers without a surfactant to fabricate porous micro-silica particles larger than 20 μm. Droplets of various size ranging from 1 to 30 μm were prepared by emulsifying aqueous solutions containing four types of polymers, namely polyethylene glycol (PEG), polyvinyl alcohol (PVA), polyvinylpyrrolidone (PVP), and polypropylene glycol (PPG), in a pentanol oil phase. Following the addition of tetraethyl orthosilicate (TEOS) as a silica precursor, silica particles were grown via hydrolysis and condensation reactions.

β-Ionone suppresses colorectal tumorigenesis by activating OR51E2, a potential tumor suppressor.

Background: Olfactory receptors (ORs) are present in non-olfactory tissues and contribute to diverse biological roles beyond smell perception. Among them, OR51E2 has been associated with cancer biology, and its activator, β-ionone, a natural terpenoid, is known to have anticancer effects.

Purpose: This study aimed to clarify the tumor-suppressive role of OR51E2 in colorectal cancer (CRC), unravel the regulatory mechanism underlying its downregulation, and evaluate the therapeutic potential of β-ionone, an OR51E2 ligand, in CRC progression.

ASiDentify (ASiD): A Machine Learning Model to Predict New Autism Spectrum Disorder Risk Genes.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects nearly 3% of children and has a strong genetic component. While hundreds of ASD risk genes have been identified through sequencing studies, the genetic heterogeneity of ASD makes identifying additional risk genes using these methods challenging. To predict candidate ASD risk genes, we developed a simple machine learning model, ASiDentify (ASiD), using human genomic, RNA- and protein-based features.