Objective:To establish a noninvasive prediction model for liver fibrosis in chronic hepatitis B (CHB) virus infection patients with alanine aminotransferase (ALT) less than upper limit of normal level.Methods:A total of 183 CHB patients with ALT <40 U/L admitted in the First Affiliated of Wenzhou Medical University from January 2014 to September 2017 were enrolled. There were 149 cases of non-marked liver fibrosis (F0-F2) and 34 cases of marked liver fibrosis (F3-F6) according to the Ishak scoring system. The clinical data, liver stiffness measurement (LSM), liver ultrasound imaging, serum biochemical features and hepatitis B virus related indexes of patients were retrospectively analyzed. The independent predictors of liver fibrosis were screened and a prediction model was constructed based on the results of multivariate logistic regression analysis. The receiver operating characteristic (ROC) curve was applied to evaluate the established model and other indicators in predicting liver fibrosis.Results:Multivariate logistic regression analysis showed that LSM ( OR=1.327, 95% CI 1.149-1.531), liver ultrasound scores ( OR=6.610, 95% CI 2.704-16.156) were independent predictors of liver fibrosis. The area under ROC curve(AUC)of the established model (LU) in predicting liver fibrosis was 0.873(95% CI 0.799-0.947), which was significantly greater than that of the ultrasound score, LSM, FIB-4, APRI and GPR (AUC=0.790, 0.804, 0.654, 0.673 and 0.770; Z=3.394, 1.982, 2.077, 3.168 and 2.165, all P<0.05 or <0.01). With the cut-off value of -1.787, the sensitivity and specificity of LU model were 85.3% and 77.2%, respectively. Conclusion:The established model (LU) can effectively predict the liver fibrosis in CHB patients with ALT less than upper limit of normal.

Objective:To investigate the expression characteristics of mucin 5B (MUC5B) protein and programmed cell death factor 4 (PDCD4) protein in patients with intrahepatic cholangiocarcinoma (ICC), and to construct a nomogram model for prognosis prediction.Methods:Clinical data of 100 patients who underwent radical surgical resection and were diagnosed as ICC by postoperative pathology from September 2009 to September 2020 in the Third Affiliated Hospital of Wenzhou Medical University were retrospectively selected, including 46 males and 54 females, aged (56.9±12.2) years old. Immunohistochemistry was used to detect the expression of MUC5B and PDCD4 protein in 100 cases of ICC and corresponding adjacent tissues respectively, and the relationship between them and clinicopathological factors of ICC patients was analyzed. Univariate and multivariate Cox regression analysis were performed to analyze the influencing factors on postoperative prognosis of ICC patients. The nomogram model was constructed using rms package and performed internal verification.Results:The positive expression rate of MUC5B protein in ICC was 76.0% (76/100), which was higher than that in para-cancer tissues 27.0%(27/100), and the difference was statistically significant ( χ2=11.33, P=0.015). While the positive expression rate of PDCD4 protein in ICC was 21.0%(21/100), which was lower than that in normal tissues 73.0% (73/100), and the difference was statistically significant ( χ2=15.57, P=0.007). Multivariate Cox regression analysis showed that ICC patients with carbohydrate antigen 19-9>37 kU/L, tumor length>5 cm, tumor TNM stage Ⅱ/Ⅲ, tumor medium/low differentiation, MUC5B positive expression, and PDCD4 negative expression had a high risk of short survival after resection (all P<0.05). The nomogram model was constructed based on the above indicators, and the C-index was 0.801. The postoperative survival calibration curve showed that the high predictive survival fit of the nomogram model, and the area under the receiver operating characteristic curve was 0.862. Conclusions:Positive expression of MUC5B protein and negative expression of PDCD4 protein in ICC tissue suggest poor prognosis of ICC patients. The nomogram model constructed on the basis of MUC5B and PDCD4 protein is well distinguished and has ideal predictive efficacy.