Effects of PCSK-9 Inhibition by Alirocumab Treatments on Biliary Cirrhotic Rats

Hyperlipidemia and oxidative stress with elevated oxidized low-density lipoprotein (ox-LDL) exacerbate hepatic inflammation and fibrosis. The plasma level of low-density lipoprotein (LDL) is controlled by proprotein convertase subtilisin/kexin 9 (PCSK9). Alirocumab is a monoclonal antibody that decreases LDL via inhibiting PCSK9 function. Apart from lipid-lowering effects, alirocumab exerts anti-inflammation, anti-angiogenesis and anti-oxidant effects. This study aims to investigate the impact of alirocumab treatment on common bile duct ligation (BDL)-induced biliary cirrhotic rats. After a 4-week treatment of alirocumab, the hemodynamic data, blood biochemistry, ox-LDL level, oxidative stress markers, severity of hepatic encephalopathy and abnormal angiogenesis of BDL rats were measured and compared to the control group. BDL rats presented cirrhotic pictures and elevated ammonia, total cholesterol, LDL and ox-LDL levels compared to the control group. Alirocumab decreased plasma levels of total cholesterol, LDL, and oxidative stress markers; however, it did not affect the hemodynamics, liver and renal biochemistry, and the plasma levels of ammonia and ox-LDL. The motor activities, portal-systemic collaterals and mesenteric vascular density were not significantly different between alirocumab-treated and control groups. In addition, it did not affect hepatic inflammation, intrahepatic angiogenesis, liver fibrosis and free cholesterol accumulation in the liver of BDL rats. In conclusion, PCSK9 inhibition by alirocumab treatment ameliorates hyperlipidemia and systemic oxidative stress in biliary cirrhotic rats. However, it does not affect the plasma level of ox-LDL, intrahepatic inflammation and fibrosis. In addition, PCSK9 inhibition has a neutral effect on abnormal angiogenesis and hepatic encephalopathy in biliary cirrhotic rats.     

Bayesian classification for data from the same unknown class

In this paper, we address the problem of how to classify a set of query vectors that belong to the same unknown class. Sets of data known to be sampled from the same class are naturally available in many application domains, such as speaker recognition. We refer to these sets as homologous sets. We show how to take advantage of homologous sets in classification to obtain improved accuracy over classifying each query vector individually. Our method, called homologous naive Bayes (HNB), is based on the naive Bayes classifier, a simple algorithm shown to be effective in many application domains. RNB uses a modified classification procedure that classifies multiple instances as a single unit. Compared with a voting method and several other variants of naive Bayes classification, HNB significantly outperforms these methods in a variety of test data sets, even when the number of query vectors in the homologous sets is small. We also report a successful application of HNB to speaker recognition. Experimental results show that HNB can achieve classification accuracy comparable to the Gaussian mixture model (GMM), the most widely used speaker recognition approach, while using less time for both training and classification.     

Domain–Specific High–Level Modeling and Synthesis for ATM Switch Prototyping

ATM switch, the core technology of an ATM networking system, is one of the major products in Fujitsu telecommunication business. However, current gate–level design methodology can no longer satisfy its stringent time–to–market requirement. It becomes necessary to exploit high–level methodology to specify and synthesize the design at an abstraction level higher than logic gates. This paper presents our prototyping experience on domain–specific high–level modeling and synthesis for Fujitsu ATM switch design. We propose a high–level design methodology using VHDL, where ATM switch architectural features are considered during behavior modeling, and a high–level synthesis compiler, MEBS, is prototyped to synthesize the behavior model down to a gate–level implementation. Since the specific ATM switch architecture is incorporated into both modeling and synthesis phases, a high–quality design is efficiently derived. The synthesis results shows that given the design constraints, the proposed high–level design methodology can produce a gate–level implementation by MEBS with about 15 percent area reduction in shorter design cycle when compared with manual design.     

Muscle Wasting in Chronic Kidney Disease: Mechanism and Clinical Implications—A Narrative Review

Muscle wasting, known to develop in patients with chronic kidney disease (CKD), is a deleterious consequence of numerous complications associated with deteriorated renal function. Muscle wasting in CKD mainly involves dysregulated muscle protein metabolism and impaired muscle cell regeneration. In this narrative review, we discuss the cardinal role of the insulin-like growth factor 1 and myostatin signaling pathways, which have been extensively investigated using animal and human studies, as well as the emerging concepts in microRNA- and gut microbiota-mediated regulation of muscle mass and myogenesis. To ameliorate muscle loss, therapeutic strategies, including nutritional support, exercise programs, pharmacological interventions, and physical modalities, are being increasingly developed based on advances in understanding its underlying pathophysiology.     

Integrating DANP and modified grey relation theory for the selection of an outsourcing provider

Globalization has inevitably forced firms to pursue outsourcing as a strategy to reduce operating costs and improve their competitiveness in the global marketplace. However, a successful outsourcing activity depends on the selection of the appropriate provider. Unlike previous multiple criteria decision-making (MCDM) methods that assume the criteria are independent, we propose a novel hybrid model, combining a Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytical Network Process (ANP) method called DANP, which addresses the dependent relationships between the various criteria to better reflect the real-world situation. At the same time, instead of ranking the alternatives, we apply a modified grey relation method to select and improve the criterion-gaps to the aspiration levels. To demonstrate the usefulness and effectiveness of the proposed model, we use data from a Taiwanese company.     

A study of the institutional forces influencing the adoption intention of RFID by suppliers

We extended the application of Roger's model for evaluating the institutional forces influencing the adoption intention of RFID by retailer's suppliers. Drawing on institutional theory, we examined how different institutional forces experienced by retailer's suppliers were related to their relational investment on inter-organizational information sharing. Data collected from 130 major Taiwanese suppliers was used in our empirical analyses. We found that relational investment was the most critical adoption factor, which also indirectly enhanced organizational readiness for technological adoption intention. From a Taiwanese perspective, our findings should be helpful in understanding what factors significantly impact the adoption of RFID technology.     

Influence of Mg3AlNi2 content on the cycling stability of Mg2Ni–Mg3AlNi2 hydrogen storage alloy electrodes

A novel method to improve the cycling capacity degradation of Mg₂Ni-based hydrogen storage alloy by means of composite materials has been proposed. The new composites Mg₂Ni-x mol% Mg₃AlNi₂ (x = 0, 15, 30, 60, 100) were prepared by means of combining electric resistance melting with isothermal evaporation casting process (IECP). Phase analysis and microstructure were investigated by SEM and TEM observations. Results showed that the cycling stability of the alloy with Mg₃AlNi₂ was superior to that of Mg₂Ni. XPS analysis revealed that the formation of an Al oxide film during cycling could enhance the anti-corrosion of the surface of the composites. Among the obtained capacity retaining rates, Mg₂Ni-15 mol% Mg₃AlNi₂ composite had the best anti-corrosion performance. This composite has kept 76% of its maximum discharge capacity (50 mA hg^?1) at 25th cycle compared to 20% for Mg₂Ni. The enhancement of the cycling stability of the electrode alloy with 15 mol% Mg₃AlNi₂ can be ascribed to the decrease in the rate of pulverization of the alloy during cycling, which is considered as the factor in the improvement of the electrochemical properties of the Mg₃AlNi₂-containing alloys.     

A fast Monte Carlo algorithm for studying bottle-brush polymers

Obtaining reliable estimates of the statistical properties of complex macromolecules by computer simulation is a task that requires high computational effort as well as the development of highly efficient simulation algorithms. We present here an algorithm combining local moves, the pivot algorithm, and an adjustable simulation lattice box for simulating dilute systems of bottle-brush polymers with a flexible backbone and flexible side chains under good solvent conditions. Applying this algorithm to the bond fluctuation model, very precise estimates of the mean square end-to-end distances and gyration radii of the backbone and side chains are obtained, and the conformational properties of such a complex macromolecule are studied. Varying the backbone length (from Nb=67 to Nb=1027), side chain length (from N=0 to N=24 or 48), the scaling predictions for the backbone behavior as well as the side chain behavior are checked. We are also able to give a direct comparison of the structure factor between experimental data and the simulation results.