Long-term influence of chemotherapy on steatosis-associated advanced hepatic fibrosis

Exploring the mechanism and drug candidates of alveolar echinococcosis affecting liver fibrosis through analysis of existing microarray data

Echinococcosis, a zoonotic disease, significantly impacts the liver, with alveolar echinococcosis (AE) often leading to liver fibrosis and, in severe cases, cirrhosis. However, the molecular mechanisms by which AE infection promotes liver fibrosis remain incompletely understood. This study utilized bioinformatic analysis of existing microarray data to explore the shared mechanisms between AE and liver fibrosis and to identify potential therapeutic drug candidates. We analyzed gene expression datasets to identify common differentially expressed genes (DEGs), followed by enrichment analyses using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes databases to determine biological functions and pathways. A protein-protein interaction network was constructed, and key hub genes were identified using Cytoscape software. Immune cell infiltration was evaluated and correlated with hub gene expression. Transcription factors regulating DEGs were predicted using the TRRUST database, and drug-target interactions were explored using DrugBank. A total of 260 DEGs were identified, primarily associated with cell cycle regulation and immune response pathways. Ten hub genes (DLGAP5, AURKA, MELK, CCNB2, CCNA2, NUF2, BUB1B, BUB1, TOP2A, and CCNB1) were highlighted for their significant interconnectivity and functional relevance. Immune infiltration analysis revealed dysregulation in immune responses, and transcription factor analysis identified E2F3 as a key regulatory factor with decreased expression in both AE and liver fibrosis. Finally, 135 candidate drugs targeting these hub genes were identified, offering new insights into therapeutic strategies. This study provides a foundation for understanding the molecular mechanisms underlying AE-related liver fibrosis and highlights potential drug candidates for clinical exploration.

An Overview of Artificial Intelligence Applications in Liver and Pancreatic Imaging

Artificial intelligence (AI) is one of the most promising fields of research in medical imaging so far. By means of specific algorithms, it can be used to help radiologists in their routine workflow. There are several papers that describe AI approaches to solve different problems in liver and pancreatic imaging. These problems may be summarized in four different categories: segmentation, quantification, characterization and image quality improvement. Segmentation is usually the first step of successive elaborations. If done manually, it is a time-consuming process. Therefore, the semi-automatic and automatic creation of a liver or a pancreatic mask may save time for other evaluations, such as quantification of various parameters, from organs volume to their textural features. The alterations of normal liver and pancreas structure may give a clue to the presence of a diffuse or focal pathology. AI can be trained to recognize these alterations and propose a diagnosis, which may then be confirmed or not by radiologists. Finally, AI may be applied in medical image reconstruction in order to increase image quality, decrease dose administration (referring to computed tomography) and reduce scan times. In this article, we report the state of the art of AI applications in these four main categories.

Analysis of molecular mechanisms of 5-fluorouracil-induced steatosis and inflammation in vitro and in mice

Chemotherapy-associated steatohepatitis is attracting increasing attention because it heralds an increased risk of morbidity and mortality in patients undergoing surgery because of liver metastases. The aim of this study was to develop in vitro and in vivo models to analyze the pathogenesis of 5-fluorouracil (5-FU)-induced steatohepatitis.

Therefore, primary human hepatocytes and HepG2 hepatoma cells were incubated with 5-FU at non-toxic concentrations up to 24 h. Furthermore, hepatic tissue of C57BL/6N mice was analyzed 24 h after application of a single 5-FU dose (200 mg/kg body weight). In vitro, incubation with 5-FU induced a significant increase of hepatocellular triglyceride levels. This was paralleled by an impairment of mitochondrial function and a dose- and time-dependently increased expression of fatty acid acyl-CoA oxidase 1 (ACOX1), which catalyzes the initial step for peroxisomal β-oxidation. The latter is known to generate reactive oxygen species, and consequently, expression of the antioxidant enzyme heme oxygenase 1 (HMOX1) was significantly upregulated in 5-FU-treated cells, indicative for oxidative stress. Furthermore, 5-FU significantly induced c-Jun N-terminal kinase (JNK) activation and the expression of pro-inflammatory genes IL-8 and ICAM-1. Also in vivo, 5-FU significantly induced hepatic ACOX1 and HMOX1 expression as well as JNK-activation, pro-inflammatory gene expression and immune cell infiltration. In summary, we identified molecular mechanisms by which 5-FU induces hepatocellular lipid accumulation and inflammation. Our newly developed models can be used to gain further insight into the pathogenesis of 5-FU-induced steatohepatitis and to develop therapeutic strategies to inhibit its development and progression.

State-of-the-art cross-sectional liver imaging: beyond lesion detection and characterization

Cross-sectional imaging with computed tomography or magnetic resonance imaging is routinely used to detect and diagnose liver lesions; however, these examinations can provide additional important information. The improvement of equipment and techniques has allowed outstanding evaluation of the vascular and biliary anatomy, which is practicable in most routine examinations. Anatomical variants may exclude patients from certain therapeutic options and may be the cause of morbidity or mortality after surgery or interventional procedures. Diffuse liver disease, such as steatosis, hemochromatosis, or fibrosis, must be diagnosed and quantified. Usually these conditions are silent until the late stages, and imaging plays an important role in detecting them early. Additionally, a background of diffuse disease may interfere in a focal lesion systematic reasoning. The diagnostic probability of a particular nodule varies according to the background liver disease. Nowadays, most diffuse liver diseases can be easily and accurately quantified by imaging, which has allowed better understanding of these diseases and improved patient management. Finally, cross-sectional imaging can calculate total and partial liver volumes and estimate the future liver remnant after hepatectomy. This information helps to select patients for portal vein embolization and reduces postoperative complications. Use of a specific hepatic contrast agent on magnetic resonance imaging, in addition to improving detection and characterization of focal lesions, provides functional global and segmental information about the liver parenchyma.