The role of the secretin/secretin receptor axis in inflammatory cholangiocyte communication via extracellular vesicles

Abstract Small and large intrahepatic bile ducts consist of small and large cholangiocytes, respectively, and these cholangiocytes have different morphology and functions. The gastrointestinal peptide hormone, secretin (SCT) that binds to secretin receptor (SR), is a key mediator in cholangiocyte pathophysiology. Extracellular vesicles (EVs) are membrane-bound vesicles and cell-cell EV communication is recognized as an important factor in liver pathology, although EV communication between cholangiocytes is not identified to date. Cholangiocytes secrete proinflammatory cytokines during bacterial infection leading to biliary inflammation and hyperplasia. We demonstrate that cholangiocytes stimulated with lipopolysaccharide (LPS), which is a membrane component of gram-negative bacteria, secrete more EVs than cholangiocytes incubated with vehicle. These LPS-derived EVs induce inflammatory responses in other cholangiocytes including elevated cytokine production and cell proliferation. Large but not small cholangiocytes show inflammatory responses against large but not small cholangiocyte-derived EVs. Large cholangiocytes with knocked down either SCT or SR by short hairpin RNAs show reduced EV secretion during LPS stimulation, and EVs isolated from SCT or SR knocked down cholangiocytes fail to induce inflammatory reactions in control large cholangiocytes. This study identifies cholangiocyte EV communication during LPS stimulation, and demonstrates that the SCT/SR axis may be important for this event

Machine Learning-Based Classification of Abnormal Liver Tissues Using Relative Permittivity

The search for non-invasive, fast, and low-cost diagnostic tools has gained significant traction among many researchers worldwide. Dielectric properties calculated from microwave signals offer unique insights into biological tissue. Material properties, such as relative permittivity (εr) and conductivity (σ), can vary significantly between healthy and unhealthy tissue types at a given frequency. Understanding this difference in properties is key for identifying the disease state. The frequency-dependent nature of the dielectric measurements results in large datasets, which can be postprocessed using artificial intelligence (AI) methods. In this work, the dielectric properties of liver tissues in three mouse models of liver disease are characterized using dielectric spectroscopy. The measurements are grouped into four categories based on the diets or disease state of the mice, i.e., healthy mice, mice with non-alcoholic steatohepatitis (NASH) induced by choline-deficient high-fat diet, mice with NASH induced by western diet, and mice with liver fibrosis. Multi-class classification machine learning (ML) models are then explored to differentiate the liver tissue groups based on dielectric measurements. The results show that the support vector machine (SVM) model was able to differentiate the tissue groups with an accuracy up to 90%. This technology pipeline, thus, shows great potential for developing the next generation non-invasive diagnostic tools