Impact Of Pregabalin Treatment On Synaptic Plasticity And Glial Reactivity During The Course Of Experimental Autoimmune Encephalomyelitis.

Multiple sclerosis (MS) is an autoimmune and neurodegenerative disease that affects young adults. It is characterized by generating a chronic demyelinating autoimmune inflammation in the central nervous system. An experimental model for studying MS is the experimental autoimmune encephalomyelitis (EAE), induced by immunization with antigenic proteins from myelin. The present study investigated the evolution of EAE in pregabalin treated animals up to the remission phase. The results demonstrated a delay in the onset of the disease with statistical differences at the 10th and the 16th day after immunization. Additionally, the walking track test (CatWalk) was used to evaluate different parameters related to motor function. Although no difference between groups was obtained for the foot print pressure, the regularity index was improved post treatment, indicating a better motor coordination. The immunohistochemical analysis of putative synapse preservation and glial reactivity revealed that pregabalin treatment improved the overall morphology of the spinal cord. A preservation of circuits was depicted and the glial reaction was downregulated during the course of the disease. qRT-PCR data did not show immunomodulatory effects of pregabalin, indicating that the positive effects were restricted to the CNS environment. Overall, the present data indicate that pregabalin is efficient for reducing the seriousness of EAE, delaying its course as well as reducing synaptic loss and astroglial reaction.

Key Issues In Inhibitor Management In Patients With Haemophilia.

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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.