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Chronic liver injury leads to decreased liver function and increased fibrosis. Fibrosis is not only associated with the development of portal hypertension and carcinogenesis, but with the occurrence of events and a poor prognosis, highlighting the importance of non‐invasive fibrosis assessment in patients. In the present study, we searched for markers related to liver fibrosis via proteomic analysis of small extracellular vesicles (sEVs). In the discovery cohort, proteomic analysis was carried out in the sEVs extracted from the sera of 5 patients with decompensated cirrhosis, 5 patients with compensated cirrhosis, and 5 controls without liver disease. Interestingly, in this cohort, fibulin‐4 was significantly associated with cirrhosis while in the validation cohort [formed by 191 patients: 7 patients without disease, 16 patients without liver disease (other diseases), 38 patients with chronic liver disease (CLD), 75 patients with cirrhosis of Child–Pugh class A (36 without hepatocellular carcinoma [HCC], 29 with HCC), and 65 patients with cirrhosis of Child–Pugh class B–C (39 without HCC, 26 with HCC)], fibulin‐4/CD9 levels increased with cirrhosis progression. Furthermore, the fibulin‐4/CD9 ratio was significantly higher in patients with varices. Immunostaining also revealed strong fibulin‐4 expression in cholangiocytes within the fibrous areas and mesothelial cells in liver tissue blood vessels. Taken together, our results suggest that fibulin‐4, essential for lysyl oxidase activation, might be a new liver fibrosis marker found in the sEVs of patients with cirrhosis. [ABSTRACT FROM AUTHOR]

Background and Aim: Treatment response to ursodeoxycholic acid may predict the prognosis of patients with primary biliary cholangitis (PBC). Recent studies have suggested the benefits of using machine learning (ML) to forecast complex medical predictions. We aimed to predict treatment response in patients with PBC using ML and pretreatment data. Methods: We conducted a single‐center retrospective study and collected data from 194 patients with PBC who were followed up for at least 12 months after treatment initiation. Patient data were analyzed with five ML models, namely random forest, extreme gradient boosting (XGB), decision tree, naïve Bayes, or logistic regression, to predict treatment response using the Paris II criteria. The established models were assessed using an out‐of‐sample validation. The area under the curve (AUC) was used to evaluate the efficacy of each algorithm. Overall survival and liver‐related deaths were analyzed using Kaplan–Meier analysis. Results: Compared to logistic regression (AUC = 0.595, P = 0.0219, 0.031 models), ML analyses showed significantly high AUC in the random forest (AUC = 0.84) and XGB (AUC = 0.83) models; however, the AUC was not significantly high for decision tree (AUC = 0.633) or naïve Bayes (AUC = 0.584) models. Kaplan–Meier analysis showed significantly improved prognoses in patients predicted to achieve the Paris II criteria by XGB (log‐rank = 0.005 and 0.007). Conclusion: ML algorithms could improve treatment response prediction using pretreatment data, which could lead to better prognoses. In addition, the ML model using XGB could predict the prognosis of patients before treatment initiation. [ABSTRACT FROM AUTHOR]

The photosynthetic light-harvesting-reaction center core complex (LH1-RC) is a natural excitonic and photovoltaic device embedded in a lipid membrane. In order to apply LH1-RCs as a biohybrid energy-producing material, some important issues must be addressed, including how to make LH1-RCs function as efficiently as possible. In addition, they should be characterized to evaluate how many active LH1-RCs efficiently work in artificial systems. We report here that an anionic phospholipid, phosphatidylglycerol (PG), stabilizes the charge-separated state (a photooxidized electron donor and reduced quinone pair, P + Q B - ) of LH1-RC (from Rhodopseudomonas palustris) and enhances its activity in photocurrent generation. Steady-state fluorometric analysis demonstrated that PG enhances the formation of the P + Q B - state at lower irradiances. The photocurrent generation activity was analyzed via Michaelis-Menten kinetics, revealing that 38% of LH1-RCs reconstituted into the PG membrane generated photocurrent at a turnover frequency of 46 s -1 . PG molecules, which interact with LH1-RC in vivo, play the role of an active effector component for LH1-RC to enhance its function in the biohybrid system.