Genome-Wide Identification of Molecular Pathways and Biomarkers in Response to Arsenic Exposure in Zebrafish Liver

10.1371/journal.pone.0068737PLoS ONE87-POLN

Interferon-gamma-stimulated genes, but not USP18, are expressed in livers of patients with acute hepatitis C

BACKGROUND & AIMS: Approximately 50% of patients with chronic hepatitis C (CHC) have a sustained virologic response to treatment with pegylated interferon (pegIFN)-alpha and ribavirin. Nonresponse to treatment is associated with constitutively increased expression of IFN-stimulated genes (ISGs) in the liver. Treatment of patients with acute hepatitis C (AHC) is more effective, with sustained virologic response rates greater than 90%. We investigated mechanisms of the different responses of patients with CHC and AHC to pegIFN-alpha therapy. METHODS: We analyzed IFN signaling and ISG expression in liver samples from patients with AHC, patients with CHC, and individuals without hepatitis C (controls) using microarray, immunohistochemical, and protein analyses. Findings were compared with those from primary human hepatocytes stimulated with IFN-alpha or IFN-gamma, as reference sets. RESULTS: Expression levels of hundreds of genes, primarily those regulated by IFN-gamma, were altered in liver samples from patients with AHC compared with controls. Expression of IFN-gamma-stimulated genes was induced in liver samples from patients with AHC, whereas expression of IFN-alpha-stimulated genes was induced in samples from patients with CHC. In an expression analysis of negative regulators of IFN-alpha signaling, we did not observe differences in expression of suppresor of cytokine signaling 1 or SOCS3 between liver samples from patients with AHC and those with CHC. However, USP18 (another negative regulator of IFN-alpha signaling), was up-regulated in liver samples of patients with CHC that did not respond to therapy, but not in AHC. CONCLUSIONS: Differences in expression of ISGs might account for the greater response of patients with AHC, compared with those with CHC, to treatment with pegIFN-alpha and ribavirin. Specifically, USP18 is up-regulated in liver samples of patients with CHC that did not respond to therapy, but not in patients with AHC

A Review of Machine Learning Applications for the Proton Magnetic Resonance Spectroscopy Workflow

This literature review presents a comprehensive overview of machine learning (ML) applications in proton magnetic resonance spectroscopy (MRS). As the use of ML techniques in MRS continues to grow, this review aims to provide the MRS community with a structured overview of the state-of-the-art methods. Specifically, we examine and summarize studies published between 2017 and 2023 from major journals in the magnetic resonance field. We categorize these studies based on a typical MRS workflow, including data acquisition, processing, analysis, and artificial data generation. Our review reveals that ML in MRS is still in its early stages, with a primary focus on processing and analysis techniques, and less attention given to data acquisition. We also found that many studies use similar model architectures, with little comparison to alternative architectures. Additionally, the generation of artificial data is a crucial topic, with no consistent method for its generation. Furthermore, many studies demonstrate that artificial data suffers from generalization issues when tested on in-vivo data. We also conclude that risks related to ML models should be addressed, particularly for clinical applications. Therefore, output uncertainty measures and model biases are critical to investigate. Nonetheless, the rapid development of ML in MRS and the promising results from the reviewed studies justify further research in this field