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Objective To analyze the serological markers and surgical indicators associated with biliary complications after orthotopic liver transplantation, explore their influencing factors and predictive indicators. Methods A retrospective analysis was performed for the clinical data of 101 patients who underwent orthotopic liver transplantation in Renmin Hospital of Wuhan University from January 2016 to June 2022, according to the presence or absence of biliary complication (BC) at 6 months after surgery, they were divided into BC group with 21 patients and non-BC group with 80 patients.The t-test or the Mann-Whitney U test was used for comparison of continuous data between groups, and the chi-square test was used for comparison of categorical data between groups.Univariate and multivariate Logistic regression analyses were performed, and the receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of combined indicators. Results Among the 101 patients, 21(20.8%) experienced BC.The multivariate Logistic regression analysis showed that MELD score (odds ratio[OR]=0.134, 95% confidence interval[CI]: 0.031-0.590, P=0.008), SⅡ/Alb (OR=1.415, 95%CI: 1.181-1.696, P=0.001), and plasma transfusion volume (OR=1.001, 95%CI: 1.000-1.002, P=0.032) were independent risk factors for the development of BC in patients after liver transplantation.MELD score, SⅡ/Alb, plasma transfusion volume, MELD+SⅡ/Alb, and MELD+SⅡ/Alb+plasma transfusion volume had an area under the ROC curve of 0.712, 0.870, 0.712, 0.900, and 0.918, respectively, in predicting BC after liver transplantation. Conclusion SⅡ/Alb, plasma transfusion volume and MELD score are independent risk fators for BC after liver transplantation.The combination of three indicators has good predictive value and clinical guiding significance for BC after liver transplantation. [ABSTRACT FROM AUTHOR]

Objective To investigate the risk factors of infection after hepatectomy for liver cancer, and to establish and validate a risk prediction model. Methods The clinical data of 167 patients with primary liver cancer who underwent hepatectomy in People's Hospital of Wuhan University from January 2020 to March 2022 were retrospectively collected. All patients were divided into postoperative infection group (n=28) and non-infection group (n=139) according to whether postoperative infection complications occurred. The t-test or Mann-Whitney U test was used for comparison of continuous data between two groups and the chi-square test was used for comparison of categorical data between two groups. Univariate analysis and logistic regression analysis were used to screen the risk factors of infection after hepatectomy for hepatocellular carcinoma, and a nomogram risk prediction model for postoperative infection was established. All patients were randomly divided into training cohort (n=119) and the validation cohort (n=48) according to the ratio of 7∶ 3, the Bootstrap method was used for internal validation of the model, and the model calibration curve and ROC curve were used to evaluate the calibration and discrimination of the nomogram model. Results Postoperative infection occurred in 28 of 167 patients (16.8%). Logistic regression analysis showed that diabetes, CONUT score ≥4 points, preoperative NLR, operation time, intraoperative blood loss, and drainage tube placement time > 7 d were independent risk factors for infection after hepatectomy for liver cancer (all P < 0.05). Based on the nomogram constructed from the above six risk factors, the area under the ROC curve of the training cohort and the validation cohort was 0.848, and 0.853, respectively. The calibration curve of the nomogram model shows that the predicted value is basically consistent with the actual observed value, indicating that the accuracy of the nomogram model prediction is better. Conclusion The individualized nomogram risk prediction model based on diabetes, CONUT score ≥4 points, preoperative NLR, operation time, intraoperative blood loss, and drainage tube placement time > 7 d has good predictive performance and has high predictive value for high-risk patients. [ABSTRACT FROM AUTHOR]

Tumor microenvironment is composed of tumor cells and their internal and external environment, and the components vary slightly between different types of tumor. Liver cancer microenvironment is a tumor-promoting microenvironment constructed by hepatoma cells and immune cells and can lead to the development, invasion, and metastasis of liver cancer by recruiting inflammatory cells, inhibiting antitumor immune response, promoting angiogenesis, and promoting drug resistance. This article discusses the characteristics of liver cancer microenvironment, the composition and role of liver cancer microenvironment, and the new advances in microenvironment-targeting therapy for liver cancer. [ABSTRACT FROM AUTHOR]