Portal vein thrombosis (PVT) is a significant issue in cirrhotic patients, necessitating early detection. This study aims to develop a data-driven predictive model for PVT diagnosis in chronic hepatitis liver cirrhosis patients. We employed data from a total of 816 chronic cirrhosis patients with PVT, divided into the Lanzhou cohort (n = 468) for training and the Jilin cohort (n = 348) for validation. This dataset encompassed a wide range of variables, including general characteristics, blood parameters, ultrasonography findings and cirrhosis grading. To build our predictive model, we employed a sophisticated stacking approach, which included Support Vector Machine (SVM), Naïve Bayes and Quadratic Discriminant Analysis (QDA). In the Lanzhou cohort, SVM and Naïve Bayes classifiers effectively classified PVT cases from non-PVT cases, among the top features of which seven were shared: Portal Velocity (PV), Prothrombin Time (PT), Portal Vein Diameter (PVD), Prothrombin Time Activity (PTA), Activated Partial Thromboplastin Time (APTT), age and Child–Pugh score (CPS). The QDA model, trained based on the seven shared features on the Lanzhou cohort and validated on the Jilin cohort, demonstrated significant differentiation between PVT and non-PVT cases (AUROC = 0.73 and AUROC = 0.86, respectively). Subsequently, comparative analysis showed that our QDA model outperformed several other machine learning methods. Our study presents a comprehensive data-driven model for PVT diagnosis in cirrhotic patients, enhancing clinical decision-making. The SVM–Naïve Bayes–QDA model offers a precise approach to managing PVT in this population.

Umbilical cord mesenchymal stem cell (UC-MSC) therapy improves liver function in liver cirrhosis patients. This study aimed to elucidate the therapeutic mechanism underlying cell therapy by analyzing changes in the modification and expression of proteins 1 month post-treatment with UC-MSCs. This prospective study included 11 cirrhosis patients who received MSC injection. The laboratory indexes before and after treatment were collected to evaluate the clinical treatment effect of UC-MSCs, and the protein expression and lactylation modification in the liver were comprehensively revealed. Meanwhile, weighted gene co-expression network analysis was used to analyze the co-expression protein modules and their relationship with clinical features. The patients with liver cirrhosis showed an improvement trend after receiving UC-MSC treatment; specifically, the liver protein synthesis function was significantly improved and the coagulation function was also significantly improved. Proteomics combined with lactic acid proteomics revealed 160 lysine lactylation (Kla) sites of 119 proteins. Functional analysis showed that the lactylation-modified proteins were enriched in the pathway of glucose and other substances’ metabolism, and many key enzymes of glycolysis and gluconeogenesis were lactated. UC-MSC therapy has a certain clinical effect in the treatment of liver cirrhosis and may act by regulating material metabolism, because the lactylation protein points to energy metabolism.