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Background:Autophagy is a lysosomal degradation pathway that can provide energy through its recycling mechanism to act as a cytoprotective adaptive response mediating treatment resistance in cancer cells. We investigated the autophagy-inducing effects of ZD6474, a small-molecule inhibitor that blocks activities of vascular endothelial growth factor receptor (VEGFR), epidermal growth factor receptor (EGFR), and RET tyrosine kinases.Methods:We investigated the effects of ZD6474 on autophagy in glioblastomas cells. The ZD6474 mechanism of action was determined by western blot. We then examined the impacts of the inhibition of autophagy in combination with ZD6474 on cell apoptosis in vitro. Furthermore, we evaluated the synergistic anticancer activity of combination treatment with an autophagy inhibitor (chloroquine) and ZD6474 in U251 glioblastoma cells xenograft model.Results:ZD6474-induced autophagy was dependent on signalling through the phosphoinositide 3-kinase/Akt/mammalian target of rapamycin (PI3K/Akt/mTOR) pathway. ZD6474-induced autophagy was inhibited by both knockdown of the ATG7 and Beclin 1 gene, essential autophagy genes, and pharmacologic agents (chloroquine and 3-methyalanine) treatment. Both treatments also dramatically sensitised glioblastoma cells to ZD6474-induced apoptosis, decreasing cell viability in vitro. Furthermore, in a xenograft mouse model, combined treatment with ZD6474 and chloroquine significantly inhibited U251 tumour growth, and increased the numbers of apoptotic cells compared with treatment with either agent alone.Conclusion:Autophagy protects glioblastoma cells from the proapoptotic effects of ZD6474, which might contribute to tumour resistance against ZD6474 treatment.

As a novel computing technology closer to business ends, edge computing has become an effective solution for delay sensitive business of power Internet of Things (IoT) and promotes the application and development of the IoT technology in smart grids. However, the inherent characteristics of a single edge node with limited resources may fail to meet the delay requirements for access ubiquitous IoT businesses of massive access. Multiple edge nodes are needed to cooperate with each other to optimize workload allocation to provide lower delay services. To this end, this paper proposes a workload allocation mechanism, orienting edge computing-based power IoT, which minimizes service delay. The workload optimization allocation model is established, and the optimal workload allocation oriented on delay among multiple edge nodes is further realized on the basis of computing resource optimization within the single edge node. The balanced initialization, resource allocation, and task allocation (BRT) algorithm are proposed. Based on the balanced initialization of workload within edge nodes, the particle swarm algorithm modified by the pheromone strategy is used to solve the problem of the computing resources' allocation inside edge nodes. Finally, the task allocation among multiple edge servers is converted into a semi-definite programming problem. The simulation results show that the proposed BRT algorithm reduces the service delay by 9.1%, 16.9%, and 26.4%, and the service delay growth rate by 24.6%, 34.5%, and 38.7%, respectively, compared with the simulated annealing algorithm (SAA), LoAd Balancing (LAB), and Latency-awarE workloAd offloaDing (LEAD) algorithms. INDEX TERMS Edge computing, multiple business, power Internet of Things, service delay, workload allocation.

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