Expression of ATP7B in human gastric cardiac carcinomas in comparison with distal gastric carcinomas

AIM： To analyze expression of ATP7B in gastric cardiac adenocarcinomas, its clinicopathologic significance, in comparison with distal gastric adenocarcinomas. METHODS： Immunohistochemical avidin-biotin peroxidase complex method was applied to detect the expression of ATP7B in 49 cases of cardiac carcinomas, the corresponding adjacent non-neoplastic epithelium and 55 cases of distal gastric carcinomas. RESULTS： The proportion of ATP7B positive samples in gastric cardiac carcinomas （51.0%, 25 of 49） was significantly higher than that in the corresponding adjacent non-neoplastic epithelium （22.4%, 11 of 49） （P = 0.003）. ATP7B expression in poorly differentiated gastric cardiac carcinomas was significantly higher than that in well/moderately differentiated gastric cardiac carcinomas （P = 0.030）. ATP7B expression in gastric cardiac carcinomas was independent of age, tumor size, nodal stage and metastasis status. ATP7B protein was detected in 30.9% （17/55 cases） of distal gastric carcinomas, markedly lower than that in gastric cardiac carcinomas （P = 0.037）. CONCLUSION： ATP7B protein is frequently overexpressecl in gastric cardiac carcinomas, and correlated with the differentiation of cardiac carcinoma. ATP7B expression in gastric cardiac carcinomas is significantly higher than that in distal gastric carcinomas, which might partially explain the difference of chemotherapy response and prognosis between these two gastric carcinomas.

A traffic-aware Q-network enhanced routing protocol based on GPSR for unmanned aerial vehicle ad-hoc networks

In dense traffic unmanned aerial vehicle(UAV)ad-hoc networks,traffic congestion can cause increased delay and packet loss,which limit the performance of the networks;therefore,a traffic balancing strategy is required to control the traffic.In this study,we propose TQNGPSR,a traffic-aware Q-network enhanced geographic routing protocol based on greedy perimeter stateless routing(GPSR),for UAV ad-hoc networks.The protocol enforces a traffic balancing strategy using the congestion information of neighbors,and evaluates the quality of a wireless link by the Q-network algorithm,which is a reinforcement learning algorithm.Based on the evaluation of each wireless link,the protocol makes routing decisions in multiple available choices to reduce delay and decrease packet loss.We simulate the performance of TQNGPSR and compare it with AODV,OLSR,GPSR,and QNGPSR.Simulation results show that TQNGPSR obtains higher packet delivery ratios and lower end-to-end delays than GPSR and QNGPSR.In high node density scenarios,it also outperforms AODV and OLSR in terms of the packet delivery ratio,end-to-end delay,and throughput.