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Resistance to chemotherapy leads to poor prognosis for osteosarcoma (OS) patients. However, due to the high metastasis of tumor and the decrease in sensitivity of tumor cells to cisplatin (DDP), the 5‐year survival rate of OS patients is still unsatisfactory. This study explored a mechanism for improving the sensitivity of OS cells to DDP. A DDP‐resistant OS cell model was established, and we have found that circORC2 and TRIM2 were upregulated in DDP‐resistant OS cells, but miR‐485‐3p was downregulated. The cell viability and proliferation of the OS cells decreased gradually with the increase of DDP dose, but a gradual increase in apoptosis was noted. CircORC2 promoted OS cell proliferation and DDP resistance and upregulated TRIM2 expression by targeting miR‐485‐3p. Functionally, circORC2 downregulated miR‐485‐3p to promote OS cell proliferation and inhibit DDP sensitivity. Additionally, it promoted cell proliferation and inhibited the sensitivity of DDP by regulating the miR‐485‐3p/TRIM2 axis. In conclusion, circORC2 promoted cell proliferation and inhibited the DDP sensitivity in OS cells via the miR‐485‐3p/TRIM2 axis. These findings indicated the role of circORC2 in regulating the sensitivity of OS cells to DDP. [ABSTRACT FROM AUTHOR]

For the ill-posed tensor completion (TC) problem, how to appropriately explore the hidden information is a pivotal yet challenging issue. Two well-known priors have been intensively studied, including the shallow priors such as low-rankness and local piecewise smoothness, and the deep priors using data-driven learning. However, their singleton usage or the simple combination hardly touches an acceptable performance. In this paper, we propose a hybrid shallow-and-deep priors model (HPM) to simultaneously exploit the respective strengths for a better performance. Specifically, based on the frontiers, i.e., tensor nuclear norm of discrete cosine transform (DCTNN) and enhanced 3D total variation (E3DTV), we propose a weighting scheme to characterize both the globally low-rank correlation and the locally smoothness. On that basis, a data-driven denoiser following the Plug-and-Play (PnP) framework is incorporated to provide the implicit information that cannot be captured by the shallow priors. Finally, the overall tensor completion model is optimized by the well-known alternating direction method of multiplier (ADMM). Numerical experiments show that the hybrid priors benefit from both types of visual properties and enable state-of-the-art quantitative and qualitative performance. [ABSTRACT FROM AUTHOR]

The aim of this study was to isolate, purify, and characterize a novel peptide with zinc-binding ability from mung bean protein hydrolysates obtained by alcalase. The peptide purification process was performed using ultrafiltration, TSK-GEL size-exclusion chromatography, and reversed-phase high-performance liquid chromatography (RP-HPLC). The molecular weight of the isolated peptide was determined by liquid chromatography–electrospray ionization mass spectrometry (LC–ESI/MS) and found to be 1192.56 Da with amino acid sequence SSEDQPFNLR. The identified peptide showed zinc-binding capacity of 80.82 mg/g, which is 55.09% higher than that of the original mung bean protein hydrolyzate. Moreover, UV–vis and FTIR spectra indicated that the zinc-binding sites on the peptide were nitrogen atoms of amide, terminal amino group, and oxygen atoms on the carboxyl group. Moreover, zinc-releasing experiments showed that the peptide–zinc chelate is highly soluble under both acidic and alkaline conditions. Furthermore, the identified peptide significantly promoted the transportation and absorption of zinc ions in Caco-2 cell model. These findings indicate a potential for production of zinc-binding peptide from mung bean protein for the utilization as ingredient in functional foods. [ABSTRACT FROM AUTHOR]

According to the characteristics of pesticide residue data and analyzing requirements in food safety fields, we presented a visual analyzing method for associated hierarchical data, called sunburst with ordered nodes based on hierarchical clustering (SONHC). SONHC arranged the leaf nodes in sunburst in order using hierarchical clustering algorithm, put the associated dataset as a node in center of the sunburst, and connected it with the associated leaf nodes in sunburst using colored lines. So, it can present not only two hierarchical structures but also the relationships between them. Based on SONHC and some interaction techniques (clicking, contraction and expansion, etc) we developed an associated visual analyzing system (AVAS) for pesticide residues detection results data, which can help users to inspect the hierarchical structure of pesticide and agricultural products and to explore the associations between pesticides and agricultural products, and associations between different pesticides. The results of user experience test showed that SONHC algorithm overperforms than SA and SR algorithm in ULE and ULE's variance. AVAS system is effective in helping users to analyze the pesticide residues data. Furthermore, SONHC algorithm can also be adopted to analyze associated hierarchical data in other fields, such as finance, insurance and e-commerce. Graphical Abstract: [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]