A novel copper/copper(i) oxide@carbon (Cu/Cu2O@C) core-shell nanocomposite monodispersed on the surface of reduced graphene oxide (defined as Cu/Cu2O@C-rGO) was synthesized using a one-step calcination of Cu-based metal organic frameworks (MOFs)/graphene oxide (GO) composite structure. The size of the resultant Cu/Cu2O@C core-shell nanoparticles (NPs) on the rGO layers were mainly distributed in range of about 90-100 nm with good dispersion, which was characterized using transmission electron microscopy. X-ray diffraction, Raman spectroscopy, Fourier transform infrared spectroscopy, nitrogen adsorption-desorption isotherms, X-ray photoelectron spectrometry and thermogravimetric analyses were also carried out to characterize the structure and composition of the Cu/Cu2O@C-rGO. The catalytic performance of the Cu/Cu2O@C-rGO composite was evaluated using a reduction reaction from 4-nitrophenol to 4-aminophenol. It was demonstrated that the Cu/Cu2O@C-rGO composite exhibited an excellent catalytic performance, which can reach a nearly 100% conversion rate in 90 s with 0.1 mg of catalyst. This catalyst also showed a good performance for the degradation of other organic dyes such as methylene blue, methyl orange and rhodamine B. Additionally, the core-shell structure exhibited good stability and reusability for the reduction reaction, which was because of its relatively better oxidation resistance than that of the bare Cu NPs. This research provided an efficient and stable non-noble metal-based catalyst derived from the MOF/GO structure.

Ractopamine (RAC) is a feed additive that regulates protein synthesis. In this study, an indirect enzyme-linked immunosorbent assay (icELISA) for the detection of RAC residues in the porcine liver was developed based on our specific immunomagnetic bead (IMB) coated with ractopamine-single chain variable fragment (RAC-scFv). The intact RAC-scFv was constructed, and its key amino acid sites were analyzed. Finally, an icELISA based on immunomagnetic beads separation techniques (IMBs) of RAC from the porcine liver was established with an IC50 of 9.15 ng/mL and a linear range from 4.8 to 17.4 ng / mL. The IC50 of the prepared RAC-scFv was 8.7 ng/mL with a linear range of 3.8 to 19.6 ng/mL. The limit of detection (LOD) in the porcine liver of RAC was 4.57 μg/kg, and the recoveries ranged from 72.65 to 89.41% (CV < 15%).

Atherosclerosis is a risk factor for various cardiovascular diseases, and is linked to high rates of morbidity and mortality across the globe. Although numerous complex processes are involved in the development and progression of atherosclerosis, the exact mechanisms behind its pathogenesis remain unclear. Inflammation and endothelial cell damage exert a lasting effect on atherosclerosis, causing lipid and fibrous tissue accumulation in the intima of the artery to form plaques, and subsequently promoting atherosclerosis. Nod-like receptor protein 3 (NLRP3) inflammatory corpuscle is thought to be the link between lipid metabolism and inflammation. Long Potassium outflow is a vital activator of NLRP3, with a simultaneous effect as a start-up and adjustment. The majority of existing drugs for atherosclerosis targeting the NLRP3 signaling pathway target IL-1, whereas drugs targeting the critical link of potassium efflux are relatively new. This review discusses the NLRP3 inflammatory corpuscle as a critical regulator of the immunological inflammatory pathway in atherosclerosis. Moreover, current knowledge on NLRP3 inflammatory corpuscle start and activation pathways were integrated, emphasizing potassiuminvolved outflow-related proteins. We highlight potential treatment approaches for NLRP3 inflammatory corpuscle pathways, specifically targeting potassium outflow channels of targeted drugs. Collectively, these insights indicate that targeting the NLRP3 inflammatory corpuscle is a vital anti-inflammatory therapy for treating atherosclerosis.

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Extensive studies have been conducted on multi-view stereo and stereo matching for 3D reconstruction, whereas relatively few methods have been proposed for a large-scale environment. The difficulty of producing high-resolution depth/disparity maps is one of the main reasons. In this paper, we propose a dual attention-guided self-adaptive aware cascade network (DAscNet) that achieves state-of-the-art results for generating high-resolution depth/disparity maps of complex scenes by introducing a cascade inference strategy using a set of input views. A pyramid cost volume fusion and a self-adaptive cost volume cascade are built upon a dual attention-guided context multi-scale feature extraction encoding geometric, spatial and contextual information at gradually finer scales to achieve robust structural representation for predictions. The dual attentionguided context multi-scale feature extraction is made up of two distinct modules that are both based on the attention mechanism. In the pyramid cost volume fusion, an inter-cost attention aggregation module fuses multiple low-resolution dense cost volumes to achieve a robust structural representation for initial predictions. In the self-adaptive cost volume cascade, a changeable depth/disparity range estimation module is employed to alter the depth/disparity searching range interval of following stage based on the prediction information from the previous stage. This module can drive the network to gradually deal with complicated matching ambiguities and make better the accuracy of depth/disparity searching range interval prediction. Experiments on two publicly available datasets, the Tanks and Temples dataset and the DTU dataset, show that DAscNet outperforms prior work. The effectiveness of our proposed method is also supported by statistics on the accuracy, runtime, and GPU memory of other representative methods.