Surface Roughness of Commercial Composites after Different Polishing Protocols: An Analysis with Atomic Force Microscopy

Polishing may increase the surface roughness of composites, with a possible effect on bacterial growth and material properties. This preliminary in vitro study evaluates the effect of three different polishing systems (PoGo polishers, Enhance, Venus Supra) on six direct resin composites (Gradia Direct, Venus, Venus Diamond, Enamel Plus HFO, Tetric Evoceram, Filtek Supreme XT)

Predicting the phosphorylation sites using hidden Markov models and machine learning methods.

Accurately predicting phosphorylation sites in proteins is an important issue in postgenomics, for which how to efficiently extract the most predictive features from amino acid sequences for modeling is still challenging. Although both the distributed encoding method and the bio-basis function method work well, they still have some limits in use. The distributed encoding method is unable to code the biological content in sequences efficiently, whereas the bio-basis function method is a nonparametric method, which is often computationally expensive. As hidden Markov models (HMMs) can be used to generate one model for one cluster of aligned protein sequences, the aim in this study is to use HMMs to extract features from amino acid sequences, where sequence clusters are determined using available biological knowledge. In this novel method, HMMs are first constructed using functional sequences only. Both functional and nonfunctional training sequences are then inputted into the trained HMMs to generate functional and nonfunctional feature vectors. From this, a machine learning algorithm is used to construct a classifier based on these feature vectors. It is found in this work that (1) this method provides much better prediction accuracy than the use of HMMs only for prediction, and (2) the support vector machines (SVMs) algorithm outperforms decision trees and neural network algorithms when they are constructed on the features extracted using the trained HMMs

Bond strength of two adhesive systems to primary and permanent enamel

The bonding performance of current adhesive systems to primary enamel has not been thoroughly researched. This study compared the micro-shear bond strength of two adhesive systems to primary and permanent tooth enamel. Two commercially available resin adhesives, a self-etching primer system (Clearfil SE Bond) and a single-bottle adhesive system (Single Bond) used with a total-etch wet bonding technique were tested. A micro-shear bond test was used to examine the adhesive systems on mid-coronal buccal enamel of extracted primary or permanent teeth. In addition, etched enamel surfaces and etched-bonded enamel interfaces were examined using scanning electron microscopy (SEM). No statistically significant differences of shear bond strength values were found between the primary and permanent enamel or the adhesive systems used (p>0.01). The SEM observations showed that both adhesive systems etched the primary enamel deeper than the permanent enamel, suggesting that the action of acid etch seemed to be more intense on primary enamel than on permanent enamel. Bonding of the adhesive systems to primary enamel was almost identical to permanent enamel. ©Operative Dentistry, 2002.link_to_subscribed_fulltex

Response to Letter to the Editor, "Dentin Bonding Testing Using a Mini-interfacial Fracture Toughness Approach"

status: publishe