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The picture of chronic liver diseases (CLDs) has changed considerably in recent years. One of them is the increase of non-alcoholic fatty liver disease. More and more CLD patients, even those with liver cirrhosis (LC), tend to be presenting with obesity these days. The annual rate of muscle loss increases with worsening liver reserve, and thus LC patients are more likely to complicate with sarcopenia. LC is also characterized by protein-energy malnutrition (PEM). Since the PEM in LC can be invariable, the patients probably present with sarcopenic obesity (Sa-O), which involves both sarcopenia and obesity. Currently, there is no mention of Sa-O in the guidelines; however, the rapidly increasing prevalence and poorer clinical consequences of Sa-O are recognized as an important public health problem, and the diagnostic value of Sa-O is expected to increase in the future. Sa-O involves a complex interplay of physiological mechanisms, including increased inflammatory cytokines, oxidative stress, insulin resistance, hormonal disorders, and decline of physical activity. The pathogenesis of Sa-O in LC is diverse, with a lot of perturbations in the muscle–liver–adipose tissue axis. Here, we overview the current knowledge of Sa-O, especially focusing on LC.

BackgroundUltrasonography (US) is widely used for the diagnosis of liver tumors. However, the accuracy of the diagnosis largely depends on the visual perception of humans. Hence, we aimed to construct artificial intelligence (AI) models for the diagnosis of liver tumors in US.MethodsWe constructed three AI models based on still B-mode images: model-1 using 24,675 images, model-2 using 57,145 images, and model-3 using 70,950 images. A convolutional neural network was used to train the US images. The four-class liver tumor discrimination by AI, namely, cysts, hemangiomas, hepatocellular carcinoma, and metastatic tumors, was examined. The accuracy of the AI diagnosis was evaluated using tenfold cross-validation. The diagnostic performances of the AI models and human experts were also compared using an independent test cohort of video images.ResultsThe diagnostic accuracies of model-1, model-2, and model-3 in the four tumor types are 86.8%, 91.0%, and 91.1%, whereas those for malignant tumor are 91.3%, 94.3%, and 94.3%, respectively. In the independent comparison of the AIs and physicians, the percentages of correct diagnoses (accuracies) by the AIs are 80.0%, 81.8%, and 89.1% in model-1, model-2, and model-3, respectively. Meanwhile, the median percentages of correct diagnoses are 67.3% (range 63.6%–69.1%) and 47.3% (45.5%–47.3%) by human experts and non-experts, respectively.Conclusion The performance of the AI models surpassed that of human experts in the four-class discrimination and benign and malignant discrimination of liver tumors. Thus, the AI models can help prevent human errors in US diagnosis.

Sarcopenia in patients with liver cirrhosis (LC) has been attracting much attention these days because of the close linkage to adverse outcomes. LC can be related to secondary sarcopenia due to protein metabolic disorders and energy metabolic disorders. LC is associated with profound alterations in gut microbiota and injuries at the different levels of defensive mechanisms of the intestinal barrier. Dysbiosis refers to a state in which the diversity of gut microbiota is decreased by decreasing the bacterial species and the number of bacteria that compose the gut microbiota. The severe disturbance of intestinal barrier in LC can result in dysbiosis, several bacterial infections, LC-related complications, and sarcopenia. Here in this review, we will summarize the current knowledge of the relationship between sarcopenia and dysbiosis in patients with LC.

BackgroundNot only obesity but also sarcopenia is associated with NAFLD. The influence of altered body composition on the pathophysiology of NAFLD has not been fully elucidated. The aim of this study is to determine whether skeletal muscle mass to visceral fat area ratio (SV ratio) affects NAFLD pathophysiology.MethodsA total of 472 subjects were enrolled. The association between SV ratio and NAFLD pathophysiological factors was assessed in a cross-sectional nature by stratification analysis.ResultsWhen the SV ratio was stratified by quartiles (Q1–Q4), the SV ratio showed a negative relationship with the degree of body mass index, HOMA-IR, and liver stiffness (Q1, 8.9 ± 7.5 kPa, mean ± standard deviation; Q2, 7.5 ± 6.2; Q3, 5.8 ± 3.7; Q4, 5.0 ± 1.9) and steatosis (Q1, 282 ± 57 dB/m; Q2, 278 ± 58; Q3, 253 ± 57; Q4, 200 ± 42) measured by transient elastography. Levels of leptin and biochemical markers of liver cell damage, liver fibrosis, inflammation and oxidative stress, and hepatocyte apoptosis were significantly higher in subjects in Q1 than in those in Q2, Q3, or Q4. Moreover, fat contents in femoral muscles were significantly higher in subjects in Q1 and the change was associated with weakened muscle strength. In logistic regression analysis, NAFLD subjects with the decreased SV ratio were likely to have an increased risk of moderate-to-severe steatosis and that of advanced fibrosis.ConclusionsDecreased muscle mass coupled with increased visceral fat mass is closely associated with an increased risk for exacerbating NAFLD pathophysiology.

Hepatoma-derived growth factor (HDGF) was identified in research seeking to find a novel growth factor for hepatoma cells. Subsequently, four HDGF-related proteins were identified, and these proteins are considered to be members of a new gene family. HDGF has a growth-stimulating role, an angiogenesis-inducing role, and a probable anti-apoptotic role. HDGF is ubiquitously expressed in non-cancerous tissues, and participates in organ development and in the healing of damaged tissues. In addition, the high expression of HDGF was reported to be closely associated with unfavorable clinical outcomes in several malignant diseases. Thus, HDGF is considered to contribute to the development and progression of malignant disease. We herein provide a brief overview of the factor and its functions in relation to benign and malignant cells. We also describe its possible role as a target molecule for digestive malignancies.

BackgroundThis study aimed to evaluate the quantitative measurement of Mac-2 binding protein glycosylation isomer (M2BPGi) levels using the new chemiluminescent enzyme immunoassay.MethodsThe data of a total of 347 patients with hepatitis C virus (HCV) infection and 150 health volunteers from 13 locations in Japan were evaluated. The quantitative system for measuring M2BPGi-Qt levels was based on a new chemiluminescent enzyme immunoassay. We evaluated the reproducibility and quantitation range in quantitative M2BPGi-Qt measurement. We also investigated the confidence ratio of M2BPGi-Qt levels measured by the new quantitative system to M2BPGi levels measured by the current semi-quantitative system for validating the clinical utility of the new method.ResultsThe reproducibility of M2BPGi-Qt in HCV samples with negative, positive 1+, and positive 2+ was 0.77 ± 0.02 AU/mL, 2.25 ± 0.03 AU/mL, and 6.55 ± 0.21 AU/mL, respectively, and the corresponding coefficient of variation (CV)s were 2.1%, 1.3%, and 3.2%, respectively. The range of quantification assessment resulted that all CVs showed less than 5% in investigated range. Sample stability testing found that the mean percentage difference between the pre- and post-storage values of 6 samples ranged between 96.2 and 103.9%. The correlation coefficient between M2BPGi and M2BPGi-Qt in patients with HCV and the healthy volunteers was 0.986 and 0.991, respectively. M2BPGi-Qt could be quantitatively assessed in a patient with over 20 C.O.I.ConclusionCompared with qualitative methods, the M2BPGi quantitative measurement system could provide a numerical value unaffected by interpretation bias, and measurements are more precise at high M2BPGi levels.

Background/Aim: We identified a new growth factor, hepatoma-derived growth factor (HDGF), which is a presumed growth-stimulating factor of hepatocellular carcinoma (HCC). Recently, we identified two microRNAs (miR-6072 and miR-3137) induced by HDGF, which were also found to be associated with the prognosis of HCC patients. This study aimed to identify the target genes of these HDGF-related microRNAs. Materials and Methods: A public database was searched for candidate target genes of HDGF-related microRNAs. Using the microarray system, the genes whose expression changed in response to HDGF administration were determined. Finally, a public cancer genomics database was searched for genes that were induced by HDGF and associated with the prognosis of HCC. Results: A total of 1,132 genes were identified as common target genes of the 2 HDGF-related microRNAs. Among these genes, a microarray system showed that the expression of 6 genes was increased (≥1.5-fold) or decreased (≤0.67-fold) after HDGF administration. Using a cancer genomics database, two of the six genes were found to be related to the prognosis of HCC. A high expression of alkylglycerone phosphate synthase (AGPS) was significantly associated with a poor survival (p=0.0025, 0.0063 and 0.0081 for the 1-, 3- and 5-year survival, respectively). A high expression of the shroom family member 4 (SHROOM4) gene was found to be significantly associated with a better survival (p=0.003, 0.0006 and 0.0006 for the 1-, 3- and 5-year survival, respectively). Conclusion: This study identified potential target genes of HDGF-related microRNAs that were associated with the prognosis of HCC.

The first edition of the guidelines for the use of ultrasound contrast agents was published in 2004, dealing with liver applications. The second edition of the guidelines in 2008 reflected changes in the available contrast agents and updated the guidelines for the liver, as well as implementing some nonliver applications. The third edition of the contrast-enhanced ultrasound (CEUS) guidelines was the joint World Federation for Ultrasound in Medicine and Biology-European Federation of Societies for Ultrasound in Medicine and Biology (WFUMB-EFSUMB) venture in conjunction with other regional US societies such as Asian Federation of Societies for Ultrasound in Medicine and Biology, resulting in a simultaneous duplicate on liver CEUS in the official journals of both WFUMB and EFSUMB in 2013. However, no guidelines were described mainly for Sonazoid due to limited clinical experience only in Japan and Korea. The new proposed consensus statements and recommendations provide general advice on the use of Sonazoid and are intended to create standard protocols for the use and administration of Sonazoid in hepatic and pancreatobiliary applications in Asian patients and to improve patient management.

Surveillance and diagnostic algorithms for hepatocellular carcinoma (HCC) have already been described in guidelines published by the American Association for the Study of Liver Diseases (AASLD), the European Association for the Study of the Liver and the European Organisation for Research and Treatment of Cancer (EASL-EORTC), and the Japan Society of Hepatology (JSH), but the content of these algorithms differs slightly. The JSH algorithm mainly differs from the other two algorithms in that it is highly sophisticated and considers the functional imaging techniques of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid-enhanced MRI (EOB-MRI) and Sonazoid contrast-enhanced ultrasound (CEUS) to be very important diagnostic modalities. In contrast, the AASLD and EASL-EORTC algorithms are less advanced and suggest that a diagnosis be made based solely on hemodynamic findings using dynamic CT/MRI and biopsy findings. A consensus meeting regarding the JSH surveillance and diagnostic algorithm was held at the 50th Liver Cancer Study Group of Japan Congress, and a 2014 update of the algorithm was completed. The new algorithm reaffirms the very important role of EOB-MRI and Sonazoid CEUS in the surveillance and diagnosis of liver cancer and is more sophisticated than those currently used in the United States and Europe. This is now an optimized algorithm that can be used to diagnose early-stage to classical HCC easily and highly accurately.