BackgroundThe M2 phenotype of tumor-associated macrophages (TAM) inhibits the anti-tumor inflammation, increases angiogenesis and promotes tumor progression. The transcription factor Nuclear Factor (erythroid-derived 2)-Like 2 (Nrf2) not only modulates the angiogenesis but also plays the anti-inflammatory role through inhibiting pro-inflammatory cytokines expression; however, the role of Nrf2 in the cancer cell and macrophages interaction is not clear.MethodsHepatocellular carcinoma cells (Hep G2 and Huh 7) and pancreatic cancer cells (SUIT2 and Panc-1) were co-cultured with monocytes cells (THP-1) or peripheral blood monocytes derived macrophages, then the phenotype changes of macrophages and epithelial-mesenchymal transition of cancer cells were detected. Also, the role of Nrf2 in cancer cells and macrophages interaction were investigated.ResultsIn this study, we found that cancer cells could induce an M2-like macrophage characterized by up-regulation of CD163 and Arg1, and down-regulation of IL-1b and IL-6 through Nrf2 activation. Also, Nrf2 activation of macrophages promoted VEGF expression. The Nrf2 activation of macrophages correlated with the reactive oxygen species induced by cancer cells derived lactate. Cancer cells educated macrophages could activate Nrf2 of the cancer cells, in turn, to increase cancer cells epithelial-mesenchymal transition (EMT) through paracrine VEGF. These findings suggested that Nrf2 played the important role in the cancer cells and macrophages interaction.ConclusionsMacrophage Nrf2 activation by cancer cell-derived lactate skews macrophages polarization towards an M2-like phenotype and educated macrophages activate Nrf2 of the cancer cells to promote EMT of cancer cells. This study provides a new understanding of the role of Nrf2 in the cancer cell and TAM interaction and suggests a potential therapeutic target.Electronic supplementary materialThe online version of this article (10.1186/s12964-018-0262-x) contains supplementary material, which is available to authorized users.

Stroke is a leading cause of long-term disability, and outcome is directly related to timely intervention. Not all patients benefit from rapid intervention, however. Thus a significant amount of attention has been paid to using neuroimaging to assess potential benefit by identifying areas of ischemia that have not yet experienced cellular death. The perfusion-diffusion mismatch, is used as a simple metric for potential benefit with timely intervention, yet penumbral patterns provide an inaccurate predictor of clinical outcome. Machine learning research in the form of deep learning (artificial intelligence) techniques using deep neural networks (DNNs) excel at working with complex inputs. The key areas where deep learning may be imminently applied to stroke management are image segmentation, automated featurization (radiomics), and multimodal prognostication. The application of convolutional neural networks, the family of DNN architectures designed to work with images, to stroke imaging data is a perfect match between a mature deep learning technique and a data type that is naturally suited to benefit from deep learning's strengths. These powerful tools have opened up exciting opportunities for data-driven stroke management for acute intervention and for guiding prognosis. Deep learning techniques are useful for the speed and power of results they can deliver and will become an increasingly standard tool in the modern stroke specialist's arsenal for delivering personalized medicine to patients with ischemic stroke.

Our results with the novel endovascular procedure appear acceptable. Additional evidence and studies with larger sample size and longer follow-up are needed to support the durability of this new technique.

The prevalence of arterial stiffness and hypertension increases with age. This study investigates the effect of induced pluripotent mesenchymal stem cell-derived extracellular vesicles (EVs) on ageing-associated arterial stiffness and hypertension. EVs were collected and purified from induced pluripotent stem cell-derived mesenchymal stem cells (iPS-MSCs). Young and old male C57BL/6 mice were used. Mice in the EVs group were injected via tail vein once a week for four weeks (18 x 10 6 EVs/mouse/injection). Blood pressure (BP) was measured using the tail-cuff method and validated by direct cannulation. Pulse wave velocity (PWV) was measured using a Doppler workstation. PWV and BP were increased significantly in the old mice, indicating arterial stiffness and hypertension. Intravenous administration of EVs significantly attenuated ageing-related arterial stiffness and hypertension, while enhancing endothelium-dependent vascular relaxation and arterial compliance in the old EVs mice. Elastin degradation and collagen I deposition (fibrosis) were increased in aortas of the old mice, but EVs substantially improved ageing-associated structural remodelling. Mechanistically, EVs abolished downregulation of sirtuin type 1 (SIRT1), and endothelial nitric oxide synthase (eNOS) protein expression in aortas of the older mice. In cultured human aortic endothelial cells, EVs promoted the expression of SIRT1, AMP-activated protein kinase alpha (AMPKα), and eNOS. In conclusion, iPS-MSC-derived EVs attenuated ageing-associated vascular endothelial dysfunction, arterial stiffness, and hypertension, likely via activation of the SIRT1-AMPKα-eNOS pathway and inhibition of MMPs and elastase. Thus, EVs mitigate arterial ageing. This finding also sheds light into the therapeutic potential of EVs for ageing-related vascular diseases.