Clinical characteristics and prognostic analysis of gastroenteropancreatic neuroendocrine neoplasm(GEP-NEN)

Objective： The aim of the study was to analyze the clinicopathologic characteristics of gastroenteropancreatic neuroendocrine neoplasm （GEP-NEN） and to explore the prognostic factors for patients and differences of immunohisto- chemical markers between neuroendocrine tumor （NET） and neuroendocrine carcinoma （NEC）. Methods： Retrospective reviews were conducted for the charts of 119 patients with GEP-NEN at the Affiliated Hospital of Qingdao University （China） from August 2003 to December 2013. Kaplan-Meier method was used to do the overall survivals analysis for the patients at different levels of predictive factors. Meanwhile, Cox proportional hazard model was used to select independent risk factors of surJival. Analysis of variance was used to compare the expression of immunohistochemical markers among different patho- logical grades. Results：Among 119 patients, pancreas （45/119, 37.82%） and rectum （33/119, 27.73%） were mostly involved. The onset age of GEP-NEN in female group was younger than that of the male group. There were 13 deaths （10.92%） during 18.9 （0.1-133.4） months follow-up period. Multivariate analysis indicated that neural invasion, gender and pathological grades of NET and NEC were independent risk factors. In neuroendocrine neoplasm （NEN）, Syn expression in G2 was higher than G1 and G3, while CgA showed no significant difference. All markers showed no significant differences between NET and NEC. Conclusion： GEP-NEN may occur at multiple sites of digestive system and lack specific clinical manifestations. Syn expression detected for the prognosis of G1, G2 and G3 tumors have clinical significance. Neural invasion, sex and patho- logical grades were independent prognostic factors for GEP-NEN patients. No significant difference was found in different pathological grades of NET and NEC.

Study on Two-Tier EV Charging Station Recommendation Strategy under Multi-Factor Influence

This article aims to address the clustering effect caused by unorganized charging of electric vehicles by adopting a two-tier recommendation method.The electric vehicles(EVs)are classified into high-level alerts and general alerts based on their state of charge(SOC).EVs with high-level alerts have the most urgent charging needs,so the distance to charging stations is set as the highest priority for recommendations.For users with general alerts,a comprehensive EV charging station recommendation model is proposed,taking into account factors such as charging price,charging time,charging station preference,and distance to the charging station.Using real data from EV charging stations and ride-hailing vehicles in Xiamen City,Fujian Province,simulation analyses are conducted using Python for different periods of the day.The research results show that the stability of the multi-factor recommendation model in terms of service density variance,coverage rate,price cost,and distance cost outperform single-factor models.This indicates that our composite multi-factor recommendation model has significant practical value in resolving the clustering phenomenon caused by unorganized EV charging,optimizing the EV charging service system,and improving user satisfaction.

Correlation analysis of three influencing factors and the dust production rate for a free-falling particle stream

The effects of three factors （i.e., drop height h, hopper outlet diameter do, and material temperature T] on the dust generation rate derived from a free falling particle stream were investigated via filll factorial experiments. The correlation between the three factors and dust generation rate was also analysed. Results show that Tand h affect the first fugitive dust rate largely, whereas the second fugitive dust rate is mainly dominated by h and do. Through analysing the first fugitive dust percentage data, it is found that h and T should be considered first for higher temperatures and lower flow rates, whereas h and do can be considered under contrasting conditions, and h should be controlled in the remaining two sets of conditions. Relationships between the influencing factors and total and first fugitive dust rates were developed via multiple regression to quantify the dust emission rates for different contact surfaces （rigid or water）.

Risk factors for thepostoperative cerebral complicationin patients with acute type A aortic dissection

Background Patients with acute type A aortic dissection have a high incidence of postoperative cerebral complication. Better understanding of the risk factors could help to optimize prevention and treatment strategies. Method A total of 298 patients of acute type A aortic dissection operated in Guangdong Cardiac Institution from 2010.10 to 2014.12 were included, 253 of them were male and the other 45 were female. Their average age was 45.8__. 12.2y. All the patients were divided into cerebral complication and no cerebral complication groups. Data collectedfrom both groups were analyzed by univariate and multivariate analyses to identify the indepen- dent risk factors of postoperative cerebral complication. Result The incidence of postoperative cerebral compli- cation was 37.9% （113/298）. Among 113 patients, temporary nerve damage （TND） group and permanent nerve damage （PND） accounted for 93 cases（31.2%） and 20 cases （6.7%） respectively. Logistic Regress multivariate analysis showed that history of hypertension （OR=2.560, 95%CI, 1.397-4.692, P〈0.01）, total arch replacement （OR=2.315, 95%CI, 1.386-3.869, P〈0.01）, time of aortic arrest （OR=1.008, 95%CI, 1.004-1.012, P〈0.01） and postoperative hypoxemia （OR=1.858, 95%CI, 1.122-3.078, P〈0.05） were the independent risk factors of the post- operative cerebral complication in patients with acute type A aortic dissection. Conclusion Incidence of postop- erative cerebral complication is high in the acute type A aortic dissection patients itsmajor risk factors are hypertension history, total arch replacement, and long duration of aortic arrest or hypoxemia.