Objective: To observe the expression of the G-protein-coupled-receptor 14 (GPR14), urotensinⅡreceptor, in the cardiovascular system and brain of SD rats. Methods: Semi-quantitative reverse transcription-polymerase chain reaction (RT-PCR) was used to detect the GPR14 mRNA. Results: In cardiovascular system, GPR14 mRNA was detected in the left ventricle, left atrium, thoratic aorta and carotid aorta. The highest level of expression was found in the left ventricle. In the brain, GPR14 mRNA was detected in cortex, hippocampus, hypothalamus and cerebellum, and higher level of expression was found in the cerebellum. Conclusion: GPR14 mRNA expression is found in the cardiovascular and neural tissues of tested rat, suggesting that urotensinⅡ may play an important role in cardiovasculature and central nervous activity.

Objective To investigate the predictive value of systemic immune inflammation index(SII)scores in long-term survival of patients with stage Ⅲ squamous lung cancer treated with radical radiotherapy.Methods Clinical data of stage Ⅲ squamous lung cancer patients who underwent radical radiotherapy at the Radiotherapy Department of the Fourth Hospital of Hebei Medical University from January 2010 to December 2018 were retrospectively analyzed.The peripheral hematological indexes one week before radiotherapy were collected and recorded.X-Tile software was applied to determine the best cut-off values for continuous variables.Kaplan-Meier method was used to analyze overall survival(OS)and progression-free survival(PFS).Results A total of 453 patients were included in this study.There were 336 patients in the low SII group(＜1 277.3),and other 117 patients were in the high SII group(≥1 277.3).The median OS and median PFS in the high SII group were shorter than those in the low SII group(OS:20.8 months vs.31.0 months,Log-rank χ2=18.015,P＜0.01;PFS:13.0 months vs.21.0 months,Log-rank χ2=15.062,P＜0.01).Multivariate Cox regression analysis showed that high SII was associated with OS(HR=1.628,95%CI:1.294-2.047,P<0.001)and PFS(HR=1.559,95%CI:1.240-1.961,P<0.001).Other influencing factors included late TNM stage,poor radiotherapy efficacy and decreased HALP score.Conclusion SII can be used to evaluate the long-term survival of patients with stage Ⅲ lung squamous cell carcinoma receiving radical radiotherapy,and the increase of SII indicates a poor prognosis.

Objective:To examine a survival prognostic model applicable for patients with intrahepatic cholangiocarcinoma (ICC) based on Bayesian network.Methods:The clinical and pathological data of ICC patients who underwent curative intent resection in ten Chinese hepatobiliary surgery centers from January 2010 to December 2018 were collected.A total of 516 patients were included in the study. There were 266 males and 250 females.The median age( M( Q R)) was 58(14) years.One hundred and sixteen cases (22.5%) with intrahepatic bile duct stones,and 143 cases (27.7%) with chronic viral hepatitis.The Kaplan-Meier method was used for survival analysis.The univariate and multivariate analysis were implemented respectively using the Log-rank test and Cox proportional hazard model.One-year survival prediction models based on tree augmented naive Bayesian (TAN) and na?ve Bayesian algorithm were established by Bayesialab software according to different variables,a nomogram model was also developed based on the independent predictors.The receiver operating characteristic curve and the area under curve (AUC) were used to evaluate the prediction effect of the models. Results:The overall median survival time was 25.0 months,and the 1-,3-and 5-year cumulative survival rates was 76.6%,37.9%,and 21.0%,respectively.Univariate analysis showed that gender,preoperative jaundice,pathological differentiation,vascular invasion,microvascular invasion,liver capsule invasion,T staging,N staging,margin,intrahepatic bile duct stones,carcinoembryonic antigen,and CA19-9 affected the prognosis(χ 2=5.858-54.974, all P<0.05).The Cox multivariate model showed that gender,pathological differentiation,liver capsule invasion, T stage,N stage,intrahepatic bile duct stones,and CA19-9 were the independent predictive factors(all P<0.05). The AUC of the TAN model based on all 19 clinicopathological factors was 74.5%,and the AUC of the TAN model based on the 12 prognostic factors derived from univariate analysis was 74.0%,the AUC of the na?ve Bayesian model based on 7 independent prognostic risk factors was 79.5%,the AUC and C-index of the nomogram survival prediction model based on 7 independent prognostic risk factors were 78.8% and 0.73,respectively. Conclusion:The Bayesian network model may provide a relatively accurate prognostic prediction for ICC patients after curative intent resection and performed superior to the nomogram model.

Objective:To examine a survival prognostic model applicable for patients with intrahepatic cholangiocarcinoma (ICC) based on Bayesian network.Methods:The clinical and pathological data of ICC patients who underwent curative intent resection in ten Chinese hepatobiliary surgery centers from January 2010 to December 2018 were collected.A total of 516 patients were included in the study. There were 266 males and 250 females.The median age( M( Q R)) was 58(14) years.One hundred and sixteen cases (22.5%) with intrahepatic bile duct stones,and 143 cases (27.7%) with chronic viral hepatitis.The Kaplan-Meier method was used for survival analysis.The univariate and multivariate analysis were implemented respectively using the Log-rank test and Cox proportional hazard model.One-year survival prediction models based on tree augmented naive Bayesian (TAN) and na?ve Bayesian algorithm were established by Bayesialab software according to different variables,a nomogram model was also developed based on the independent predictors.The receiver operating characteristic curve and the area under curve (AUC) were used to evaluate the prediction effect of the models. Results:The overall median survival time was 25.0 months,and the 1-,3-and 5-year cumulative survival rates was 76.6%,37.9%,and 21.0%,respectively.Univariate analysis showed that gender,preoperative jaundice,pathological differentiation,vascular invasion,microvascular invasion,liver capsule invasion,T staging,N staging,margin,intrahepatic bile duct stones,carcinoembryonic antigen,and CA19-9 affected the prognosis(χ 2=5.858-54.974, all P<0.05).The Cox multivariate model showed that gender,pathological differentiation,liver capsule invasion, T stage,N stage,intrahepatic bile duct stones,and CA19-9 were the independent predictive factors(all P<0.05). The AUC of the TAN model based on all 19 clinicopathological factors was 74.5%,and the AUC of the TAN model based on the 12 prognostic factors derived from univariate analysis was 74.0%,the AUC of the na?ve Bayesian model based on 7 independent prognostic risk factors was 79.5%,the AUC and C-index of the nomogram survival prediction model based on 7 independent prognostic risk factors were 78.8% and 0.73,respectively. Conclusion:The Bayesian network model may provide a relatively accurate prognostic prediction for ICC patients after curative intent resection and performed superior to the nomogram model.