The gut microbiota-bile acid axis in cholestatic liver disease.

Cholestatic liver diseases (CLD) are characterized by impaired normal bile flow, culminating in excessive accumulation of toxic bile acids. The majority of patients with CLD ultimately progress to liver cirrhosis and hepatic failure, necessitating liver transplantation due to the lack of effective treatment. Recent investigations have underscored the pivotal role of the gut microbiota-bile acid axis in the progression of hepatic fibrosis via various pathways. The obstruction of bile drainage can induce gut microbiota dysbiosis and disrupt the intestinal mucosal barrier, leading to bacteria translocation. The microbial translocation activates the immune response and promotes liver fibrosis progression. The identification of therapeutic targets for modulating the gut microbiota-bile acid axis represents a promising strategy to ameliorate or perhaps reverse liver fibrosis in CLD. This review focuses on the mechanisms in the gut microbiota-bile acids axis in CLD and highlights potential therapeutic targets, aiming to lay a foundation for innovative treatment approaches.

Development of a diagnostic model for biliary atresia based on MMP7 and serological tests using machine learning.

OBJECTIVE: To develop a machine learning diagnostic model based on MMP7 and other serological testing indicators for early and efficient diagnosis of biliary atresia (BA). METHODS: A retrospective analysis was conducted on patient information from those hospitalized for pathological jaundice at Beijing Children's Hospital between January 1, 2019, and December 31, 2023. Patients with serum MMP7, liver stiffness measurements, and other routine serological tests were included in the study. Six machine learning models were constructed, including logistic regression (LR), random forest (RF), decision tree (DET), support vector machine classifier (SVC), neural network (MLP), and extreme gradient boosting (XGBoost), to diagnose BA. The area under the receiver operating characteristic curve was used to evaluate the diagnostic efficacy of the various models. RESULTS: A total of 98 patients were included in the study, comprising 64 BA patients and 34 patients with other cholestatic liver diseases. Among the six machine learning models, the XGBoost algorithm model and RF algorithm model achieved the best predictive performance, with an AUROC of nearly 100% in both the training and validation sets. In the training set, these two algorithm models achieved an accuracy, precision, recall, F1 score, and AUROC of 1. Through model interpretation analysis, serum MMP7 levels, serum GGT levels, and acholic stools were identified as the most important indicators for diagnosing BA. The nomogram constructed based on the XGBoost algorithm model also demonstrated convenient and efficient diagnostic efficacy. CONCLUSION: Machine learning models, especially the XGBoost algorithm and RF algorithm models, constructed based on preoperative serum MMP7 and serological tests can diagnose BA more efficiently and accurately. The most important influencing factors for diagnosis are serum MMP7, serum GGT, and acholic stools.

Diagnose and treatment for Type D congenital esophageal atresia with tracheoesophageal fistula.

Importance: Type D esophageal atresia (EA) with tracheoesophageal fistula (TEF) is characterized by EA with both proximal and distal TEFs. It is a rare congenital anomaly with a very low incidence. Objective: To investigate diagnostic and treatment strategies for this rare condition. Methods: We retrospectively reviewed the clinicopathological features of patients with EA/TEF treated at our institution between January 2007 and September 2021. Results: Among 386 patients with EA/TEF, 14 (3.6%) had type D EA/TEF. Only two patients were diagnosed with proximal TEF preoperatively. Seven patients were diagnosed intraoperatively. Five patients were missed for diagnosis during the initial surgery but was later confirmed by bronchoscopy. During the neonatal period, seven patients underwent a one-stage repair of proximal and distal TEF via thoracoscopy or thoracotomy. Due to missed diagnosis and other reasons, the other 7 patients underwent two-stage surgery for repair of the proximal TEF, including cervical incision and thoracoscopy. Ten of the 14 patients experienced postoperative complications including anastomotic leakage, pneumothorax, esophageal stricture, and recurrence. Patients who underwent one-stage repair of distal and proximal TEF during the neonatal period showed a higher incidence of anastomotic leak (4/7). In contrast, only one of seven patients with two-stage repair of the proximal TEF developed an anastomotic leak. Interpretation: Type D EA/TEF is a rare condition, and proximal TEFs are easily missed. Bronchoscopy may aim to diagnose and determine the correct surgical approach. A cervical approach may be more suitable for repairing the proximal TEF.