Effect of microwave treatment on the shear bond strength of different types of commercial teeth to acrylic resin

doi:10.1111/j.1741‐2358.2009.00333.x
Effect of microwave treatment on the shear bond strength of different types of commercial teeth to acrylic resin Objective: The purpose of this study was to verify the effect of microwave treatment on the shear bond strength of commercial types of teeth to acrylic resin, when the glossy ridge laps were unmodified (groups 1 and 5), bur abraded (groups 2 and 6), bur grooved (groups 3 and 7) or etched by monomer (groups 4 and 8). Background: Controversial findings have shown that mechanical or chemical changes in ridge‐lap surface of the tooth increase or decrease the bond strength between tooth and acrylic resin, and the microwave disinfection may cause different changes on this bond strength. Materials and methods: Eighty specimens (n = 10) were made with the acrylic resin bonded to tooth glossy ridge lap, polymerised in water at 74°C for 9 h, and deflasked after flask cooling. Specimens of the groups 5, 6, 7 and 8 were individually immersed in 150 ml of water and submitted to microwave treatment in an oven at 650 W for 3 min. Control specimens (groups 1, 2, 3 and 4) were not microwave treated. Shear bond strength test was performed in an Instron machine with a cross‐speed of 1 mm/min. Collected data were submitted to anova and Tukey’s test (α = 0.05). Results: Microwave treatment decreased the shear bond strength values of the tooth/resin bond. In the microwaved and non‐microwaved procedures, mechanical retention improved the shear bond strength when compared with the control and monomer treatments. Conclusion: Shear bond strength of the tooth/resin bond was influenced by the microwave treatment and different commercial teeth association, and was lower for the Biotone tooth.     

Soil Properties,Nutrient Dynamics,and Soil Enzyme Activities Associated with Garlic Stalk Decomposition under Various Conditions

The garlic stalk is a byproduct of garlic production and normally abandoned or burned, both of which cause environmental pollution. It is therefore appropriate to determine the conditions of efficient decomposition, and equally appropriate to determine the impact of this decomposition on soil properties. In this study, the soil properties, enzyme activities and nutrient dynamics associated with the decomposition of garlic stalk at different temperatures, concentrations and durations were investigated. Stalk decomposition significantly increased the values of soil pH and electrical conductivity. In addition, total nitrogen and organic carbon concentration were significantly increased by decomposing stalks at 40°C, with a 5∶100 ratio and for 10 or 60 days. The highest activities of sucrase, urease and alkaline phosphatase in soil were detected when stalk decomposition was performed at the lowest temperature (10°C), highest concentration (5∶100), and shortest duration (10 or 20 days). The evidence presented here suggests that garlic stalk decomposition improves the quality of soil by altering the value of soil pH and electrical conductivity and by changing nutrient dynamics and soil enzyme activity, compared to the soil decomposition without garlic stalks.     

Predicting Metabolic Pathways of Small Molecules and Enzymes Based on Interaction Information of Chemicals and Proteins

Metabolic pathway analysis, one of the most important fields in biochemistry, is pivotal to understanding the maintenance and modulation of the functions of an organism. Good comprehension of metabolic pathways is critical to understanding the mechanisms of some fundamental biological processes. Given a small molecule or an enzyme, how may one identify the metabolic pathways in which it may participate? Answering such a question is a first important step in understanding a metabolic pathway system. By utilizing the information provided by chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions, a novel method was proposed by which to allocate small molecules and enzymes to 11 major classes of metabolic pathways. A benchmark dataset consisting of 3,348 small molecules and 654 enzymes of yeast was constructed to test the method. It was observed that the first order prediction accuracy evaluated by the jackknife test was 79.56% in identifying the small molecules and enzymes in a benchmark dataset. Our method may become a useful vehicle in predicting the metabolic pathways of small molecules and enzymes, providing a basis for some further analysis of the pathway systems.