Background: Hepatocellular Carcinoma (HCC) is a highly heterogeneous malignant tumor, and its prognostic prediction is extremely challenging. Ferroptosis is a cell mechanism dependent on iron, which is very significant for HCC development. Long non-coding RNA (lncRNA) is also linked to HCC progression. This work aimed to establish a prognosis risk model for HCC and to discover a possible biomarker and therapeutic target. Methods: The Cancer Genome Atlas (TCGA) database was used to obtain RNA-seq transcriptome data and clinic information of HCC patients. Firstly, univariate Cox was utilized to identify 66 prognostic ferroptosis-related lncRNAs. Then, the identified lncRNAs were further included in the multivariate Cox analysis to construct the prognostic model. Eventually, we performed quantitative polymerase chain reaction (q-PCR) to validate the risk model. Results: We established a prognostic seventeen-ferroptosis-related lncRNA signature model. The signature could categorize patients into two risk subgroups, with the low-risk subgroup associated with a better prognosis. Additionally, the area under the curve (AUC) of the lncRNAs signature was 0.801, indicating their reliability in forecasting HCC prognosis. Risk score was an independent prognostic factor by regression analyses. Gene set enrichment analysis (GSEA) analyses demonstrated a remarkable enrichment of cancer-related and immune-related pathways in the high-risk group. Besides, the immune status was decreased in the high-risk group. Eventually, three prognostic lncRNAs were validated in human HCCLM3 cell lines. Conclusions: The risk model based on seventeen-ferroptosis-related lncRNA has significant prognostic value for HCC and may be therapeutic targets associated with ferroptosis in clinical ways.

Hepatic sinusoidal obstruction syndrome (HSOS) is a life-threatening syndrome, and a cause is exposure to pyrrolizidine alkaloid (PA)-containing products. It is well-established that retrorsine (RTS), a representative Pas, insults hepatic sinusoidal endothelial cells and ensues congestion of hepatic sinusoids. However, little known about the impact of Pas on gut microbiota and intestinal barrier and inflammation in HSOS.

With an increasing understanding of hepatitis B, the antiviral indications have been broadening gradually. To evaluate the effectiveness of tenofovir alafenamide (TAF) in chronic hepatitis B (CHB) patients with normal alanine aminotransferase (ALT) and detectable hepatitis B virus (HBV) DNA, those who are ineligible for broader antiviral criteria from the Chinese CHB prevention guide (2019).

Background: There are challenges for beginners to identify standard biliopancreatic system anatomical sites on endoscopic ultrasonography (EUS) images. Therefore, the authors aimed to develop a convolutional neural network (CNN)-based model to identify standard biliopancreatic system anatomical sites on EUS images. Methods: The standard anatomical structures of the gastric and duodenal regions observed by EUS was divided into 14 sites. The authors used 6230 EUS images with standard anatomical sites selected from 1812 patients to train the CNN model, and then tested its diagnostic performance both in internal and external validations. Internal validation set tests were performed on 1569 EUS images of 47 patients from two centers. Externally validated datasets were retrospectively collected from 16 centers, and finally 131 patients with 85 322 EUS images were included. In the external validation, all EUS images were read by CNN model, beginners, and experts, respectively. The final decision made by the experts was considered as the gold standard, and the diagnostic performance between CNN model and beginners were compared. Results: In the internal test cohort, the accuracy of CNN model was 92.1–100.0% for 14 standard anatomical sites. In the external test cohort, the sensitivity and specificity of CNN model were 89.45–99.92% and 93.35–99.79%, respectively. Compared with beginners, CNN model had higher sensitivity and specificity for 11 sites, and was in good agreement with the experts (Kappa values 0.84–0.98). Conclusions: The authors developed a CNN-based model to automatically identify standard anatomical sites on EUS images with excellent diagnostic performance, which may serve as a potentially powerful auxiliary tool in future clinical practice.

Although ursodeoxycholic acid (UDCA) treatment in primary biliary cholangitis is effective in many patients, there are still many people who respond poorly to it. Identifying and intervening these patients early is important. Therefore, exploring the risk factors and proposing a predictor index to predict the UDCA treatment nonresponse earlier among primary biliary cholangitis patients were the aims of this research.

The effect of tenofovir amibufenamide (TMF) on blood lipid profiles in patients with chronic hepatitis B (CHB) remains unclear. This study aimed to explore whether TMF affects blood lipids during 48 weeks in patients with CHB.