In Vitro Effects Of Hydrogen Peroxide Combined With Different Activators For The In-office Bleaching Technique On Enamel.

Abstract Objective. The aim of this study was to evaluate the alteration of human enamel bleached with high concentrations of hydrogen peroxide associated with different activators. Materials and methods. Fifty enamel/dentin blocks (4 × 4 mm) were obtained from human third molars and randomized divided according to the bleaching procedure (n = 10): G1 = 35% hydrogen peroxide (HP - Whiteness HP Maxx); G2 = HP + Halogen lamp (HL); G3 = HP + 7% sodium bicarbonate (SB); G4 = HP + 20% sodium hydroxide (SH); and G5 = 38% hydrogen peroxide (OXB - Opalescence Xtra Boost). The bleaching treatments were performed in three sessions with a 7-day interval between them. The enamel content, before (baseline) and after bleaching, was determined using an FT-Raman spectrometer and was based on the concentration of phosphate, carbonate, and organic matrix. Statistical analysis was performed using two-way ANOVA for repeated measures and Tukey's test. Results. The results showed no significant differences between time of analysis (p = 0.5175) for most treatments and peak areas analyzed; and among bleaching treatments (p = 0.4184). The comparisons during and after bleaching revealed a significant difference in the HP group for the peak areas of carbonate and organic matrix, and for the organic matrix in OXB and HP+SH groups. Tukey's analysis determined that the difference, peak areas, and the interaction among treatment, time and peak was statistically significant (p < 0.05). Conclusion. The association of activators with hydrogen peroxide was effective in the alteration of enamel, mainly with regards to the organic matrix.

Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetry

PURPOSE: To evaluate the sensitivity and specificity of machine learning classifiers (MLCs) for glaucoma diagnosis using Spectral Domain OCT (SD-OCT) and standard automated perimetry (SAP). METHODS: Observational cross-sectional study. Sixty two glaucoma patients and 48 healthy individuals were included. All patients underwent a complete ophthalmologic examination, achromatic standard automated perimetry (SAP) and retinal nerve fiber layer (RNFL) imaging with SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec Inc., Dublin, California). Receiver operating characteristic (ROC) curves were obtained for all SD-OCT parameters and global indices of SAP. Subsequently, the following MLCs were tested using parameters from the SD-OCT and SAP: Bagging (BAG), Naive-Bayes (NB), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Random Forest (RAN), Ensemble Selection (ENS), Classification Tree (CTREE), Ada Boost M1(ADA),Support Vector Machine Linear (SVML) and Support Vector Machine Gaussian (SVMG). Areas under the receiver operating characteristic curves (aROC) obtained for isolated SAP and OCT parameters were compared with MLCs using OCT+SAP data. RESULTS: Combining OCT and SAP data, MLCs' aROCs varied from 0.777(CTREE) to 0.946 (RAN).The best OCT+SAP aROC obtained with RAN (0.946) was significantly larger the best single OCT parameter (p<0.05), but was not significantly different from the aROC obtained with the best single SAP parameter (p=0.19). CONCLUSION: Machine learning classifiers trained on OCT and SAP data can successfully discriminate between healthy and glaucomatous eyes. The combination of OCT and SAP measurements improved the diagnostic accuracy compared with OCT data alone.