Over the last decades, the incidence of type 2 diabetes (T2D) is increasing substantially. Emerging evidences from epidemiological studies have shown the association between higher intake of soy isoflavones and reduced risk of T2D and its associated health risks. Daidzein, a soy isoflavone, has been found to have a promising therapeutic potential in managing T2D pathophysiology. Fermented soybean is the major source of daidzein; however, it can also be formed via the consumption of its glycosylated moiety, daidzin with subsequent hydrolysis by intestinal bacterial enzyme. Many studies reported the prophylactic effect of daidzein on the improvement of hyperglycemia, insulin resistance, dislipidemia, obesity, inflammation, and other complications associated with T2D. The molecular mechanisms underlying the action of daidzein include diverged pathways where daidzein has been shown to interact with several signaling molecules and receptors to achieve desirable effect. Although the specific molecular mechanism is still elusive, further studies are thus needed to understand it in detail. In this review, we discuss the antidiabetic potential of daidzein with respect to the evidences from various clinical, preclinical, and cell culture studies and the underlying molecular mechanism in a precise way to have a comprehensive account on this isoflavone with promising therapeutic potential. © 2018 BioFactors, 44(5):407-417, 2018.

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A new technique for diagnosis in a scan-based BIST environment is presented.i t allows non-adaptive identijkation of both the scan cells that capture errors (space information) as well as a subset of the failing test vectors (time information). Having both space and time information allows a faster and more precise diagnosis. Previous techniques for identifying the failing test vectors during BIST have been limited in the multiplicity of errors that can be handled andlor require a very large hardware overhead. The proposed approach, however, uses only two cycling registers at the output of the scan chain to accurately identify a subset of the failing BIST test vectors. This is accomplished using some novel pruning techniques that efficiently extract information ffom the signatures of the cycling registers. While not all the failing BIST test vectors can be ident$ed, results indicate that a significant number of them can be. This additional information can save a lot of time in failure analysis. r

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The purpose of this paper is to find out an efficient decision support method for non-traditional machine selection. It seeks to analyze potential non-traditional machine selection attributes with a relatively new MCDM approach of MOORA and MOOSRA method. The use of MOORA and MOOSRA method has been adopted to tackle subjective evaluation of information collected from an expert group. An example case study is shown here for better understanding of the said selection module which can be effectively applied to any other decision-making scenario. The method is not only computationally very simple, easily comprehensible, and robust, but also believed to have numerous subjective attributes. The rankings are expected to provide good guidance to the managers of an organization to select a feasible non-traditional machine. It shall also provide a good insight for the non-traditional machine manufacturer who might encourage research work concerning non-traditional machine selection.

Glucose-responsive delivery of antidiabetic molecules is gaining scientific attention as one of the promising strategies for maintaining normoglycemia and reducing the risk of overdose associated with the molecule. However, the water-insoluble antidiabetic molecules are not studied yet to deliver in a glucose-sensitive way. Herein, we examined the glucose-responsive delivery of vitamin K (VK) by using dextran-capped mesoporous silica nanoparticles (MSNs) functionalized with 3-carboxyphenylboronic acid (CPBA). The VK was loaded inside the pores of MSN-CPBA by physical adsorption, and the loading capacity of MSN-CPBA was 206.7 μg/mg (w/w). The pores of the nanoparticles were capped by using dextran via binding with CPBA, and the dextran-caps were untethered in the presence of glucose by competing with dextran to CPBA allowing the release of VK. The release of VK was 19.48 μg/mL in a cell-free system and 1.5 and 0.99 μg/mL in muscle and liver cells, respectively, in exposure to 25 mM glucose. The system not only delivered the VK in a glucose-dependent manner but also significantly improved the bioavailability thereof. The finding showed the promising therapeutic potential of MSN-CPBA-VK-Dex in reducing hyperglycemia in both in vitro and in vivo systems. The MSN-CPBA-based dextran-gated delivery system would be a promising therapeutic strategy to increase the bioavailability and stimuli-responsive delivery of antidiabetic molecules for managing diabetes mellitus over conventional approaches.

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An FPGA-based