The trillions of commensal microorganisms living in the gut lumen profoundly influence the physiology and pathophysiology of the liver through a unique gut-liver axis. Disruptions in the gut microbial communities, arising from environmental and genetic factors, can lead to altered microbial metabolism, impaired intestinal barrier and translocation of microbial components to the liver. These alterations collaboratively contribute to the pathogenesis of liver disease, and their continuous impact throughout the disease course plays a critical role in hepatocarcinogenesis. Persistent inflammatory responses, metabolic rearrangements and suppressed immunosurveillance induced by microbial products underlie the pro-carcinogenic mechanisms of gut microbiota. Meanwhile, intrahepatic microbiota derived from the gut also emerges as a novel player in the development and progression of liver cancer. In this review, we first discuss the causes of gut dysbiosis in liver disease, and then specify the pivotal role of gut microbiota in the malignant progression from chronic liver diseases to hepatobiliary cancers. We also delve into the cellular and molecular interactions between microbes and liver cancer microenvironment, aiming to decipher the underlying mechanism for the malignant transition processes. At last, we summarize the current progress in the clinical implications of gut microbiota for liver cancer, shedding light on microbiota-based strategies for liver cancer prevention, diagnosis and therapy.

Due to its irregular shape and varying contour, pancreas segmentation is a recognized challenge in medical image segmentation. Convolutional neural network (CNN) and Transformer-based networks perform well but have limitations: CNN have constrained receptive fields, and Transformer underutilize image features. This work proposes an improved pancreas segmentation method by combining CNN and Transformer. Point-wise separable convolution was introduced in a stage-wise encoder to extract more features with fewer parameters. A densely connected ensemble decoder enabled multi-scale feature fusion, addressing the structural constraints of skip connections. Consistency terms and contrastive loss were integrated into deep supervision to ensure model accuracy. Extensive experiments on the Changhai and National Institute of Health (NIH) pancreas datasets achieved the highest Dice similarity coefficient (DSC) values of 76.32% and 86.78%, with superiority in other metrics. Ablation studies validated each component's contributions to performance and parameter reduction. Results demonstrate that the proposed loss function smooths training and optimizes performance. Overall, the method outperforms other advanced methods, enhances pancreas segmentation performance, supports physician diagnosis, and provides a reliable reference for future research.
Humans ; Neural Networks, Computer ; Pancreas/diagnostic imaging* ; Image Processing, Computer-Assisted/methods* ; Algorithms ; Deep Learning

OBJECTIVE: To accurately measure human energy metabolism with high temporal resolution, a respiratory gas analysis system was designed using a breath-by-breath approach. METHODS: Firstly, indirect calorimetry was employed in respiratory gas analysis to measure the respiratory flow and concentration signals in real-time. Secondly, oxygen consumption QO2 and carbon dioxide production QCO2 were calculated through respiratory characteristic alignment and respiratory signal segmentation. Finally, metabolic indexes were calculated according to the Weir formula. Furthermore, a controlled trial was formulated for validation comparisons with the MGC ULTIMA SYSTEM PFX CARDIO2. RESULTS: The results indicate that the data points of metabolic indexes measured by this system all fall within the confidence interval when compared with those of the MGC. CONCLUSION The system has high consistency with the MGC measurement results. Thus, the system can accurately measure metabolism in real-time and provide data support for clinical nutrition assessment and therapy.
Humans ; Energy Metabolism ; Breath Tests/instrumentation* ; Calorimetry, Indirect/instrumentation* ; Equipment Design

The trillions of commensal microorganisms living in the gut lumen profoundly influence the physiology and pathophysiology of the liver through a unique gut-liver axis. Disruptions in the gut microbial communities, arising from environmental and genetic factors, can lead to altered microbial metabolism, impaired intestinal barrier and translocation of microbial components to the liver. These alterations collaboratively contribute to the pathogenesis of liver disease, and their continuous impact throughout the disease course plays a critical role in hepatocarcinogenesis. Persistent inflammatory responses, metabolic rearrangements and suppressed immunosurveillance induced by microbial products underlie the pro-carcinogenic mechanisms of gut microbiota. Meanwhile, intrahepatic microbiota derived from the gut also emerges as a novel player in the development and progression of liver cancer. In this review, we first discuss the causes of gut dysbiosis in liver disease, and then specify the pivotal role of gut microbiota in the malignant progression from chronic liver diseases to hepatobiliary cancers. We also delve into the cellular and molecular interactions between microbes and liver cancer microenvironment, aiming to decipher the underlying mechanism for the malignant transition processes. At last, we summarize the current progress in the clinical implications of gut microbiota for liver cancer, shedding light on microbiota-based strategies for liver cancer prevention, diagnosis and therapy.

The trillions of commensal microorganisms living in the gut lumen profoundly influence the physiology and pathophysiology of the liver through a unique gut-liver axis. Disruptions in the gut microbial communities, arising from environmental and genetic factors, can lead to altered microbial metabolism, impaired intestinal barrier and translocation of microbial components to the liver. These alterations collaboratively contribute to the pathogenesis of liver disease, and their continuous impact throughout the disease course plays a critical role in hepatocarcinogenesis. Persistent inflammatory responses, metabolic rearrangements and suppressed immunosurveillance induced by microbial products underlie the pro-carcinogenic mechanisms of gut microbiota. Meanwhile, intrahepatic microbiota derived from the gut also emerges as a novel player in the development and progression of liver cancer. In this review, we first discuss the causes of gut dysbiosis in liver disease, and then specify the pivotal role of gut microbiota in the malignant progression from chronic liver diseases to hepatobiliary cancers. We also delve into the cellular and molecular interactions between microbes and liver cancer microenvironment, aiming to decipher the underlying mechanism for the malignant transition processes. At last, we summarize the current progress in the clinical implications of gut microbiota for liver cancer, shedding light on microbiota-based strategies for liver cancer prevention, diagnosis and therapy.

Arginine methylation is a critical post-translational modification that plays multifaceted biological functions. However, the manipulation of protein arginine methylation largely depends on genetic or pharmaceutic inhibition of the regulatory enzymes, protein arginine methyltransferases (PRMTs), or non-methylation substitution of corresponding arginine residue to lysine or alanine of protein of interest (POI), which inevitably affects other substrates, or disrupts the structure of POI. Thus, it urges an approach to specifically modulate the arginine methylation of a POI under physiological conditions. To this end, we report the discovery of a methylation tagging system (MeTAG), that enables targeted modification of protein arginine methylation. Through bridging the methyltransferase PRMT5 proximity to a POI, MeTAG facilitates the arginine methylation of POIs, including known arginine methylated proteins, androgen receptor (AR) and protein kinase B (AKT), as well as a neo-substrate E1A binding protein (p300), in a reversible and PRMT5-dependent manner. Moreover, MeTAG can regulate downstream signaling in a methylation dependent manner, leading to downregulation of PSMA mRNA level and activation of AKT. Therefore, MeTAG represents a feasible approach to modulate protein methylation and thereby perturbs protein function in biological and therapeutic contexts.

Acute liver failure (ALF) is a life-threatening condition associated with macrophage-mediated inflammatory responses. Effective therapies and drugs are still lacking to date. Here, we reveal that a derivative of xanthohumol, CAM12203, alleviates lipopolysaccharide (LPS) + d-galactosamine (D-GalN)-induced ALF through limiting macrophage-mediated inflammation, with the most significant impact on interleukin-1β (IL-1β) transcription. Through biotin labeling-mediated pull-down and LC-MS/MS analysis, diacylglycerol kinase ζ (DGKζ), a lipid-metabolizing kinase, is identified as the direct target of CAM12203. Mechanistically, DGKζ is induced in macrophages upon inflammatory stimuli and is upregulated observed on clinical liver failure samples. Its product phosphatidic acid (PA) boosts phospholipase C (PLC)-inositol 1,4,5-trisphosphate (IP₃)-Ca²⁺ signaling and subsequent janus kinase 2 (JAK2)-signal transducer and activator of transcription 3 (STAT3) cascade, ultimately promoting IL-1β production and liver failure. DGKζ knockdown/ablation or inhibition significantly impairs the DGKζ-STAT3-IL-1β pathway along with ALF progression. Finally, CAM12203 is confirmed to be a new DGKζ inhibitor and acts against inflammation in a DGKζ-reliant manner. Taken together, CAM12203 inhibits IL-1β transcription in macrophages by binding to DGKζ and blocking the DGKζ-STAT3 axis, thereby exerting an ameliorative effect on ALF. These results not only highlight CAM12203 as a promising lead compound for ALF treatment, but also define DGKζ as a novel therapeutic target.

Objective:To design an inspection system for quality of metabolism tester based on alcohol combustion mode for human body,so as to realize assessment for accuracy degree of human metabolism tester.Methods:According to the specific indicators that were measured by the metabolism tester for human body,a simulation of bidirectional controllable flow and a simulation of component concentration of exhaled breath were designed and realized.Then,the ratio of alcohol consumption to oxygen consumption and carbon dioxide production were calculated by the formula of alcohol combustion.Finally,the metabolic indexes(volume of gas flow,oxygen consumption,dioxide carbon production,rate of energy metabolism,respiratory quotient)that were outputted by the inspection system for quality were calculated according to Weir formula.Furthermore,the verification for precision of simulation modules of exhale and inhalation,and weighing device module was carried out,and an application test of inspection system for quality was also carried out.Results:The errors of the volume of gas flow and alcohol consumption that were outputted by simulation modules of exhale and inhalation,and weighing device module were all within±1%,and the errors between the reference value of the metabolic index of the inspection system for quality and the actually measured value of the metabolic index of the hood calorimeter were all within 10%.Conclusion:There was higher consistency between inspection system for quality of metabolism tester for human body and the actual measurement data of calorimeter,which can better simulate the process of respiratory metabolism of human body,and provide standardized testing means for the performance verification of metabolic tester.

Objective:To design an inspection system for quality of metabolism tester based on alcohol combustion mode for human body,so as to realize assessment for accuracy degree of human metabolism tester.Methods:According to the specific indicators that were measured by the metabolism tester for human body,a simulation of bidirectional controllable flow and a simulation of component concentration of exhaled breath were designed and realized.Then,the ratio of alcohol consumption to oxygen consumption and carbon dioxide production were calculated by the formula of alcohol combustion.Finally,the metabolic indexes(volume of gas flow,oxygen consumption,dioxide carbon production,rate of energy metabolism,respiratory quotient)that were outputted by the inspection system for quality were calculated according to Weir formula.Furthermore,the verification for precision of simulation modules of exhale and inhalation,and weighing device module was carried out,and an application test of inspection system for quality was also carried out.Results:The errors of the volume of gas flow and alcohol consumption that were outputted by simulation modules of exhale and inhalation,and weighing device module were all within±1%,and the errors between the reference value of the metabolic index of the inspection system for quality and the actually measured value of the metabolic index of the hood calorimeter were all within 10%.Conclusion:There was higher consistency between inspection system for quality of metabolism tester for human body and the actual measurement data of calorimeter,which can better simulate the process of respiratory metabolism of human body,and provide standardized testing means for the performance verification of metabolic tester.

Objective To evaluate the efficacy and safety of fluorouracil and leucovorin and oxaliplatin(FOLFOX)regimen hepatic artery infusion chemotherapy(HAIC)combined with lenvatinib(LEN)and immune checkpoint inhibitors(ICIs)in treating patients with hepatocellular carcinoma(HCC)after the occurrence of transcatheter arterial chemoembolization(TACE)refractoriness.Methods The clinical data of 54 HCC patients who developed TACE refractoriness,were admitted to the Jiangsu Provincial Cancer Hospital of China to receive FOLFOX-HAIC combined with LEN and ICIs therapy between January 2019 and December 2022,were retrospectively analyzed.The modified Response Evaluation Criteria in Solid Tumors(mRECIST)was used to statistically analyze the clinical efficacy,the Common Terminology Criteria For Adverse Events version 5.0(CTCAE 5.0)was adopted to record and evaluate the treatment-related adverse events(TRAEs).The primary endpoints were progression-free survival(PFS)and overall survival(OS),the secondary endpoints were objective response rate(ORR),disease control rate(DCR),and safety.Results The median PFS was 11.7 months(95％CI:8.124-15.276 months),the median OS was 23.1 months(95％CI:19.508-26.692 months),the ORR was 46.3％,and the DCR was 87.0％.The most common TRAE at all levels was elevated alanine aminotransferase(51.9％),and the most common TRAE of grade 3/4 was hypertension(9.3％).No treatment-related death occurred.Conclusion For the treatment of HCC patients who developed TACE refractoriness,FOLFOX-HAIC combined LEN and ICIs is clinically safe and effective.(J Intervent Radiol,2024,33:610-615)