Chronic liver diseases pose a substantial health burden worldwide, with approximately two million deaths each year. Branched-chain amino acids (BCAAs)—valine, leucine, and isoleucine—are a group of essential amino acids that are essential for human health. Despite the necessity of a dietary intake of BCAA, emerging data indicate the undeniable correlation between elevated circulating BCAA levels and chronic liver diseases, including non-alcoholic fatty liver diseases (NAFLD), cirrhosis, and hepatocellular carcinoma (HCC). Moreover, circulatory BCAAs were positively associated with a higher cholesterol level, liver fat content, and insulin resistance (IR). However, BCAA supplementation was found to provide positive outcomes in cirrhosis and HCC patients. This review will attempt to address the contradictory claims found in the literature, with a special focus on BCAAs’ distribution, key signaling pathways, and the modulation of gut microbiota. This should provide a better understanding of BCAAs’ possible contribution to liver health.

Liver cancer, predominantly hepatocellular carcinoma (HCC), is the third leading cause of cancer-related deaths worldwide. Emerging data highlight the importance of gut homeostasis in the pathogenesis of HCC. Clinical and translational studies revealed the patterns of dysbiosis in HCC patients and their potential role for HCC diagnosis. Research on underlying mechanisms of dysbiosis in HCC development pointed out the direction for improving the treatment and prevention. Despite missing clinical studies, animal models showed that modulation of the gut microbiota by probiotics may become a new way to treat or prevent HCC development.

Antibodies in response to antigens are related to the immune repertoire of T- and B-cell receptors. However, some patients with chronic hepatitis B (CHB) have coexisting HBsAg and anti-HBsAg antibodies (anti-HBs) that cannot neutralize HBV. We attempted to investigate the repertoires that produce this response in CHB patients. The T-cell receptor β chain (TRB) and B-cell receptor (BCR) repertoires of peripheral blood genomic DNA were analyzed using MiXCR. T-cell receptor (TCR) cluster analysis was carried out by clusTCR, and motifs prediction was selected by Multiple Em for Motif Elicitation (MEME). A total of 76 subjects were enrolled, including 26 HBsAg and anti-HBs coexisting patients with CHB (DP group), 25 anti-HBs single-positive healthy people (SP group), and 25 CHB patients (CHB group). The clone length of BCR in 39, 90 was significantly different among these groups (p = 0.005, 0.036). The motif “CASSLG” in the DP group was significantly higher than SP and CHB groups and may relate to coexistence, and the motif “GAGPLT” was only shown in the SP group and may relate to anti-HB expression. These provide important insights into vaccine development and CHB treatment.

Recently, deep learning has made an important contribution to the development of medical image segmentation, such as U-Net and its variants. However, the existing methods based deep learning perform poorly in the small structures, structure continuity and efficiency. In this paper, we present EU-Net, an efficient and more powerful U-Net-like architecture for medical image segmentation, which consists of a lightweight encoder and decoder that are connected through dense skip-connection. To further improve the robustness of the EU-Net, chain EU-Net is proposed. Chain EU-Net is based on a streamlined architecture that uses multiple EU-Net to build light weight deep neural networks by dense skip-connection. We evaluate the EU-Net and chain EU-Net on three typical medical image segmentation tasks: GLAS (Gland segmentation) dataset, RITE (Retinal Images Vessel Tree Extraction) dataset and LiTS (Liver Tumor Segmentation Challenge) dataset. In addition, we used PUFH (Peking University First Hospital) dataset. Experimental results show that the proposed methods achieve state-of-the-art performance with very few parameters.