BackgroundA great number of studies reported that 12/15-lipoxygenase (12/15-LO) played an important role in atherosclerosis. And its arachidonic acid(AA) metabolite, 15(S)-hydroperoxy-5,8,11,13-(Z,Z,Z,E)-eicosatetraenoic acid (15(S)-HETE), is demonstrated to mediate endothelial dysfunction. 15-oxo-5,8,11,13-(Z,Z,Z,E)-eicosatetraenoic acid (15-oxo-ETE) was formed from 15-hydroxyprostaglandin dehydrogenase (PGDH)-mediated oxidation of 15(S)-HETE. However, relatively little is known about the biological effects of 15-oxo-ETE in cardiovascular disease. Here, we explore the likely role of 15-lipoxygenase (LO)-1-mediated AA metabolism,15-oxo-ETE, in the early pathogenesis of atherosclerosis.MethodsThe 15-oxo-ETE level in serum was detected by means of liquid chromatography and online tandem mass spectrometry (LC-MS/MS). And the underlying mechanisms were illuminated by molecular techniques, including immunoblotting, MTT assay, immunocytochemistry and Immunohistochemistry. ResultsIncreased 15-oxo-ETE level is found in in patients with acute myocardial infarction (AMI). After 15-oxo-ETE treatment, Human umbilical vein endothelial cells (HUVECs) showed more attractive to monocytes, whereas monocyte adhesion is suppressed when treated with PKC inhibitor. In ex vivo study, exposure of arteries from C57 mice and ApoE−/−mice to 15-oxo-ETE led to significantly increased E-selectin expression and monocyte adhesion.ConclusionsThis is the first report that 15-oxo-ETE promotes early pathological process of atherosclerosis by accelerating E-selectin expression and monocyte adhesion. 15-oxo-ETE -induced monocyte adhesion is partly attributable to activation of PKC.Electronic supplementary materialThe online version of this article (doi:10.1186/s12944-017-0518-2) contains supplementary material, which is available to authorized users.
Fiber Bragg grating (FBG) sensors have been widely applied in various applications, especially for structural health monitoring. Low cost, wide range, and low error are necessary for an excellent performance FBG sensor signal demodulation system. Yet the improvement of performance is commonly accompanied by costly and complex systems. A high-performance, low-cost wavelength interrogation method for FBG sensors was introduced in this paper. The information from the FBG sensor signal was extracted by the array waveguide grating (AWG) and fed into the proposed cascaded neural network. The proposed network was constructed by cascading a convolutional neural network and a residual backpropagation neural network. We demonstrate that our network yields a vastly significant performance improvement in AWG-based wavelength interrogation over that given by other machine learning models and validate it in experiments. The proposed network cost-effectively widens the wavelength interrogation range of the demodulation system and optimizes the wavelength interrogation error substantially, also making the system scalable.
For FPI sensor demodulation systems to be used in actual engineering measurement, they must have high performance, low cost, stability, and scalability. Excellent performance, however, necessitates expensive equipment and advanced algorithms. This research provides a new absolute demodulation system for FPI sensors that is high-performance and cost-effective. The reflected light from the sensor was demultiplexed into distinct channels using an array waveguide grating (AWG), with the interference spectrum features change translated as the variation of the transmitted intensity in each AWG channel. This data was fed into an end-to-end neural network model, which was utilized to interrogate multiple interference peaks’ absolute peak wavelengths simultaneously. This architecturally simple network model can achieve remarkable generalization capabilities without training large-scale datasets using an appropriate data augmentation strategy. Experiments show that in simultaneous multi-wavelength and cavity length interrogations, the proposed system has the precision of up to ± 14 pm and ± 0.07 µm, respectively. The interrogation resolution can theoretically reach the pm level benefit from the neural network method. Furthermore, the system’s outstanding demodulation repeatability and suitability were demonstrated. The system is expected to provide a high-performance and cost-effective, reliable solution for practical engineering applications.
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