Bevacizumab attenuates osteosarcoma angiogenesis by suppressing MIAT encapsulated by serum-derived extracellular vesicles and facilitating miR-613-mediated GPR158 inhibition.

Targeting angiogenesis has been considered a promising treatment for a large number of malignancies, including osteosarcoma. Bevacizumab (Bev) is an anti-vascular endothelial growth factor being used for this purpose. We herein investigate the therapeutic potential of Bev in angiogenesis during osteosarcoma and the related mechanisms. Bioinformatics were performed for identification of osteosarcoma-related microarray dataset to collect related lncRNA and miRNA, with MIAT and miR-613 obtained. The predicted binding site between miR-613 and GPR158 3'UTR region was further confirmed by luciferase assay. Then, their effects combined with treatment with Bev on osteosarcoma cells were explored by the gain- and loss-of-function. After extraction from osteosarcoma patients' serum (serum-EVs) and identification, EVs were co-cultured with osteosarcoma cells, the biological behaviors of which were detected by CCK-8 assay and microtubule formation in vitro. A mouse tumor xenograft model was used to determine the effect of Bev on tumor angiogenesis in vivo. Bev inhibited osteosarcoma cell proliferation and angiogenesis in vivo and in vitro. Besides, serum-EVs could transfer MIAT (EV-MIAT) into osteosarcoma cells, where it is competitively bound to miR-613 to elevate GPR158, thus promoting osteosarcoma cell proliferation and angiogenesis. Furthermore, Bev arrested osteosarcoma cell proliferation and angiogenesis by inhibiting EV-MIAT and inducing miR-613-mediated GPR158 inhibition. In conclusion, the Bev-mediated MIAT/miR-613/GPR158 regulatory feedback revealed a new molecular mechanism in the pathogenesis of osteosarcoma angiogenesis.

Cylinder detection in large-scale point cloud of pipeline plant.

The huge number of points scanned from pipeline plants make the plant reconstruction very difficult. Traditional cylinder detection methods cannot be applied directly due to the high computational complexity. In this paper, we explore the structural characteristics of point cloud in pipeline plants and define a structure feature. Based on the structure feature, we propose a hierarchical structure detection and decomposition method that reduces the difficult pipeline-plant reconstruction problem in IR³ into a set of simple circle detection problems in IR². Experiments with industrial applications are presented, which demonstrate the efficiency of the proposed structure detection method.