Integrating molecular, biochemical, and immunohistochemical features as predictors of hepatocellular carcinoma drug response using machine-learning algorithms.

Introduction: Liver cancer, particularly Hepatocellular carcinoma (HCC), remains a significant global health concern due to its high prevalence and heterogeneous nature. Despite the existence of approved drugs for HCC treatment, the scarcity of predictive biomarkers limits their effective utilization. Integrating diverse data types to revolutionize drug response prediction, ultimately enabling personalized HCC management.

Evaluating the therapeutic potential of genetically engineered probiotic Zbiotics (ZB183) for non-alcoholic steatohepatitis (NASH) management modulation of the cGAS-STING pathway.

NAFLD/NASH has emerged as a global health concern with no FDA-approved treatment, necessitating the exploration of novel therapeutic elements for NASH. Probiotics are known as an important adjunct therapy in NASH. Zbiotics (ZB183) is the first commercially available genetically engineered probiotic.

Correction: Rosavin improves insulin resistance and alleviates hepatic and kidney damage modulating the cGAS-STING pathway and autophagy signaling in HFD/STZ-induced T2DM animals.

[This corrects the article DOI: 10.1039/D4MD00023D.].

Machine-Learning-Based Identification of Key Feature RNA-Signature Linked to Diagnosis of Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) is the third prime cause of malignancy-related mortality worldwide. Early and accurate identification of HCC is crucial for good prognosis, efficacy of therapy, and survival rates of the patients. We aimed to develop a machine-learning model incorporating differentially expressed RNA signatures with laboratory parameters to construct an RNA signature-based diagnostic model for HCC.