Background and Aims. A retrospective cross-sectional study was conducted to evaluate the role of hyaluronic acid (HA), laminin (LN), amino-terminal propeptide of type III procollagen (PIIINP), and collagen IV (CIV) in predicting the presence of gastroesophageal varices (GEVs) in patients with liver cirrhosis. Methods. We enrolled 118 patients with liver cirrhosis who underwent the tests for the four serum liver fibrosis markers and upper gastrointestinal endoscopy at the same admissions. The predictive values of the four serum liver fibrosis markers were evaluated by the areas under the receiving operator characteristics curves (AUROCs) with 95% confidence intervals (CIs). Results. The prevalence of GEVs was 88% (104/118). The AUROCs for HA, LN, PIIINP, and CIV levels in predicting the presence of GEVs were 0.553 (95% CI: 0.458 to 0.644, P = 0.5668), 0.490 (95% CI: 0.397 to 0.584, P = 0.9065), 0.622 (95% CI: 0.528 to 0.710, P = 0.1099), and 0.560 (95% CI: 0.466 to 0.652, P = 0.4909). The PIIINP level at a cut-off value of 31.25 had a sensitivity of 73.1% and a specificity of 57.1%. Conclusions. The present study did not recommend HA, LN, PIIINP, and CIV levels to evaluate the presence of GEVs in liver cirrhosis.

BackgroundThe prognostic role of serum liver fibrosis markers in cirrhotic patients remains unclear. We performed a prospective observational study to evaluate the effect of amino-terminal pro-peptide of type III pro-collagen (PIIINP), collagen IV (CIV), laminin (LN), and hyaluronic acid (HA) on the prognosis of liver cirrhosis.Material/MethodsAll patients who were diagnosed with liver cirrhosis and admitted to our department were prospectively enrolled. PIIINP, CIV, LN, and HA levels were tested.ResultsOverall, 108 cirrhotic patients were included. Correlation analysis demonstrated that CIV (coefficient r: 0.658, p<0.001; coefficient r: 0.368, p<0.001), LN (coefficient r: 0.450, p<0.001; coefficient r: 0.343, p<0.001), and HA (coefficient r: 0.325, p=0.001; coefficient r: 0.282, p=0.004) levels, but not PIIINP level (coefficient r: 0.081, p=0.414; coefficient r: 0.090, p=0.363), significantly correlated with Child-Pugh and MELD scores. Logistic regression analysis demonstrated that HA (odds ratio=1.00003, 95% confidence interval [CI]=1.000004–1.000056, p=0.022) was significantly associated with the 6-month mortality. Receiver operating characteristics analysis demonstrated that the area under the curve (AUC) of HA for predicting the 6-month mortality was 0.612 (95%CI=0.508–0.709, p=0.1531).ConclusionsCIV, LN, and HA levels were significantly associated with the severity of liver dysfunction, but might be inappropriate for the prognostic assessment of liver cirrhosis.

The high explanatory power of the first-order lag term of inflation in the inflation explanatory factor is that, on the one hand, the calculation of annual inflation indicators makes the inflation values of adjacent months cover the high correlation caused by the common price increase, and on the other hand, it also shows that people’s perception of inflation is high. Some adaptability is expected. Although there are many Bayesian models available, due to the limitation of high-dimensional characteristics of the economy, most of the current inflation forecasting researches focus on a variety of generalized naive Bayesian models. By summarizing and analyzing the structural characteristics, learning methods, and classification principles of different Bayesian models, this paper finds out the important factors that affect the performance of the models and provides a theoretical basis for further improving the performance of Bayesian inflation forecasting. In this paper, the empirical mode decomposition method is introduced into inflation forecasting, and EEMD has obvious advantages in dealing with nonstationary and nonlinear time series and can decompose the signal according to the time scale characteristics of the data itself. Decompose the original time series step by step to generate eigenmode functions with different time scales. It is divided into high-frequency sequence and low-frequency sequence. Use rolling method and iterative method to construct subsamples for the sample data in this sample interval, forecast the inflation rate of each subsample interval in the next 12 months, and then compare the predicted value with the actual value to obtain a certain constructive conclusion. The predicted value is relatively close to the real value, which has theoretical and practical significance, and the predicted results obtained have no obvious regularity, but the root mean square error can be kept within 60%. By comparing the predicted value and the actual value, it can be seen that the prediction effect of the EEMD model is better.