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IntroductionMacranthoidin B is one of the primary and unique triterpenoid saponin metabolites from Lonicera macranthoides Hand. –Mazz, which is used to treat endometriosis (EMS) in traditional Chinese medicine. However, the effect of macranthoidin B remains unknown in EMS. This study aimed to elucidate the effect and mechanism of macranthoidin B in EMS.MethodsUsing rat autograft EMS model, the volume of ectopic endothelium, the histopathology, serum E2 and PROG were evaluated after macranthoidin B’s treatment. In primary endometriotic stromal and HEC1-B cells, the invasion and metastasis were assessed by scratch wound and Transwell tests. The epithelial-mesenchymal transition and COX-2/PGE2 pathway were examined in vivo and in vitro. Macranthoidin B were combined with LPS or celecoxib.ResultsIn a rat autograft EMS model, macranthoidin B suppressed ectopic lesion volume, improved histopathological morphology, and regulated serum estradiol (E2) and progesterone (PROG) levels. Additionally, macranthoidin B inhibited invasion and metastasis of primary endometriotic stromal cells and HEC1-B cells. Mechanistically, macranthoidin B suppressed COX-2/PGE2 pathway and epithelial-mesenchymal transition both in vivo and in vitro. LPS, the COX-2/PGE2 pathway activator, showed the promotion of epithelial-mesenchymal transition, invasion and metastasis. Macranthoidin B exhibited the antagonistic effects against LPS. Celecoxib, the COX-2/PGE2 pathway inhibitor, restrained the epithelial-mesenchymal transition, invasion and metastasis. This effect of celecoxib was enhanced by macranthoidin B.Discussion Macranthoidin B prevents epithelial-mesenchymal transition through COX-2/PGE2 pathway in EMS. It will facilitate the macranthoidin B’s development and broaden its potential application.

Abstract To support multi-source data stream generated from Internet of Things devices, edge computing emerges as a promising computing pattern with low latency and high bandwidth compared to cloud computing. To enhance the performance of edge computing within limited communication and computation resources, we study a cloud-edge-end computing architecture, where one cloud server and multiple computational access points can collaboratively process the compute-intensive data streams that come from multiple sources. Moreover, a multi-source environment is considered, in which the wireless channel and the characteristic of the data stream are time-varying. To adapt to the dynamic network environment, we first formulate the optimization problem as a markov decision process and then decompose it into a data stream offloading ratio assignment sub-problem and a resource allocation sub-problem. Meanwhile, in order to reduce the action space, we further design a novel approach that combines the proximal policy optimization (PPO) scheme with convex optimization, where the PPO is used for the data stream offloading assignment, while the convex optimization is employed for the resource allocation. The simulated outcomes in this work can help the development of the application of the multi-source data stream.