Introduction: Extracellular vesicles could serve as a non-invasive biomarker for early cancer detection. However, limited methods to quantitate cancer-derived vesicles in the native state remain a significant barrier to clinical translation.
Aim: This research aims to develop a rapid, one-step immunoaffinity approach to quantify HCC exosomes directly from a small serum volume.
The epidemiology, natural history, and therapeutic responses of chronic liver diseases and liver lesions often vary by sex. In this review, we summarize available clinical and translational data on these aspects of the most common liver conditions encountered in clinical practice, including the potential contributions of sex hormones to the underlying pathophysiology of observed differences. We also highlight areas of notable knowledge gaps and discuss sex disparities in access to liver transplant and potential strategies to address these barriers.
View Article and Find Full Text PDFImportance: Large language models (LLMs) have proven useful for extracting data from publicly available sources, but their uses in clinical settings and with clinical data are unknown.
Objective: To determine the accuracy of data extraction using "Versa Chat," a chat implementation of the general-purpose OpenAI gpt-35-turbo LLM model, versus manual chart review for hepatocellular carcinoma (HCC) imaging reports.
Design: We engineered a prompt for the data extraction task of six distinct data elements and input 182 abdominal imaging reports that were also manually tagged.
Background And Aim: HCC development in liver cirrhosis is associated with impaired autophagy leading to increased production of extracellular vesicles (EVs) including exosomes and microvesicles. The goal of the study is to determine which of these particles is primarily involved in releasing of HCC-specific biomarker glypican-3 (GPC3) when autophagy is impaired.
Methods: Streptavidin-coated magnetic beads were coupled with either biotinylated CD63 or Annexin A1 antibodies.