Bile acid synthesis impedes tumor-specific T cell responses during liver cancer.

The metabolic landscape of cancer greatly influences antitumor immunity, yet it remains unclear how organ-specific metabolites in the tumor microenvironment influence immunosurveillance. We found that accumulation of primary conjugated and secondary bile acids (BAs) are metabolic features of human hepatocellular carcinoma and experimental liver cancer models. Inhibiting conjugated BA synthesis in hepatocytes through deletion of the BA-conjugating enzyme bile acid-CoA:amino acid -acyltransferase (BAAT) enhanced tumor-specific T cell responses, reduced tumor growth, and sensitized tumors to anti-programmed cell death protein 1 (anti-PD-1) immunotherapy.

Assessing the Effectiveness of Student-generated Scenario-based Questions as a Tool for Active Learning.

Background: Active learning is not new as an educational philosophy and its benefits over passive learning modes are well known. In a competency-based framework, active learning is one of the key thrust areas. However, across the globe studies have shown that its implementation is wrought with challenges and limitations.

Highly Efficient JR Optimization Technique for Solving Prediction Problem of Soil Organic Carbon on Large Scale.

We utilized remote sensing and ground cover data to predict soil organic carbon (SOC) content across a vast geographic region. Employing a combination of machine learning and deep learning techniques, we developed a novel data fusion approach that integrated Digital Elevation Model (DEM) data, MODIS satellite imagery, WOSIS soil profile data, and CHELSA environmental data. This combined dataset, named GeoBlendMDWC, was specifically designed for SOC prediction.