Electronic working length determination in primary teeth by Propex and Digital Signal Processing

The purpose of this study was to evaluate the accuracy of electronic apex locators Digital Signal Processing (DSP) and ProPex, for root canal length determination in primary teeth. Fifteen primary molars (a total of 34 root canals) were divided into two groups: Group I - without physiological resorption (n = 16); and Group II - with physiological resorption (n = 18). The length of each canal was measured by introducing a file until its tip was visible and then it was retracted 1 mm. For electronic measurement, the devices were set to 1 mm short of the apical resorption. The data were analysed statistically using the intraclass correlation coefficient (ICC). Results showed that the ICC was high for both electronic apex locators in all situations - with (ICC: DSP = 0.82 and Propex = 0.89) or without resorption (ICC: DSP = 0.92 and Propex = 0.90). Both apex locators were extremely accurate in determining the working length in primary teeth, both with or without physiological resorption.

Bond strength of fibre glass and carbon fibre posts to the root canal walls using different resin cements

The aim of the study was to evaluate the bond strength of fibre glass and carbon fibre posts in the root canal walls cemented with self-adhesive (RelyX-Unicem) and chemical (Cement-Post) resin cements. Forty maxillary canines were divided into four groups according to the cement and post used and submitted to the push-out test (0.5 mm min(-1)). The data were submitted to statistical analysis (2-way ANOVA, Bonferroni - P < 0.05) and fracture analysis by Scanning Electronic Microscopy. Fibre glass presented the best results when cemented with RelyX-Unicem and Cement-Post (P < 0.05). RelyX-Unicem presented the highest bond strength values for both posts (P < 0.05). Fracture analysis showed predominance of cohesive fracture of post for RelyX-Unicem and adhesive fracture between dentin/cement and mixed for Cement-Post. The bond strength values were significantly affected by the type of post and cement used and the highest values were found for fibre glass posts and RelyX-Unicem.

Leucine supplementation favors liver protein status but does not reduce body fat in rats during 1 week of food restriction

An important role in protein-energy metabolism has been attributed to leucine because of its long-term effects on body fat reduction and on the improvement of some indicators of protein status in rodents. The present study investigated the influence of leucine supplementation on the body composition and protein status of rats during the early phase of weight loss, which is characterized by a rapid loss of body weight. Thirty adult male Wistar rats were divided into 2 groups, a control and a leucine group (diet supplemented with 0.59% L-leucine), and were submitted to 1 week of 50% food restriction. The following parameters were evaluated: chemical carcass composition, protein and RNA content in liver and gastrocnemius muscle, and serum concentrations of insulin-like growth factor-1 and corticosterone. A higher liver weight and liver protein content were observed in the supplemented group (p < 0.05). However, no difference in body fat was found between groups (p > 0.05). The results indicate that low-dose leucine supplementation favors liver protein status but does not reduce body fat in rats during the early phase of rapid weight loss.

The univalent polynomial of Suffridge as a summability kernel

A positive summability trigonometric kernel {K(n)(theta)}(infinity)(n=1) is generated through a sequence of univalent polynomials constructed by Suffridge. We prove that the convolution {K(n) * f} approximates every continuous 2 pi-periodic function f with the rate omega(f, 1/n), where omega(f, delta) denotes the modulus of continuity, and this provides a new proof of the classical Jackson`s theorem. Despite that it turns out that K(n)(theta) coincide with positive cosine polynomials generated by Fejer, our proof differs from others known in the literature.

Polarization and Spectral Shift of Benzophenone in Supercritical Water

Monte Carlo simulation and quantum mechanics calculations based on the INDO/CIS and TD-DFT methods were utilized to study the solvatochromic shift of benzophenone when changing the environment from normal water to supercritical (P = 340.2 atm and T = 673 K) condition. Solute polarization increases the dipole moment of benzophenone, compared to gas phase, by 88 and 35% in normal and supercritical conditions, giving the in-solvent dipole value of 5.8 and 4.2 D, respectively. The average number of solute-solvent hydrogen bonds was analyzed, and a large decrease of 2.3 in normal water to only 0.8 in the supercritical environment was found. By using these polarized models of benzophenone in the two different conditions of water, we performed MC simulations to generate statistically uncorrelated configurations of the solute surrounded by the solvent molecules and subsequent quantum mechanics calculations on these configurations. When changing from normal to supercritical water environment, INDO/CIS calculations explicitly considering all valence electrons of the 235 solvent water molecules resulted in a solvatochromic shift of 1425 cm(-1) for the most intense transition of benzophenone, that is, slightly underestimated in comparison with the experimentally inferred result of 1700 cm(-1). TD-B3LYP/6-311+G(2d,p) calculations on the same configurations but with benzophenone electrostatically embedded in the 320 water molecules resulted in a solvatochromic shift of 1715 cm(-1) for this transition, in very good agreement with the experimental result. When using the unpolarized model of the benzophenone, this calculated solvatochromic shift was only 640 cm(-1). Additional calculations were also made by using BHandHLYP/6-311+G(2d,p) to analyze the effect of the asymptotic decay of the exchange functional. This study indicates that, contrary to the general expectation, there is a sizable solute polarization even in the low-density regime of supercritical condition and that the inclusion of this polarization is important for a reliable description of the spectral shifts considered here.

Decomposability problem on branched coverings

Given a branched covering of degree d between closed surfaces, it determines a collection of partitions of d, the branch data. In this work we show that any branch data are realized by an indecomposable primitive branched covering on a connected closed surface N with chi(N) <= 0. This shows that decomposable and indecomposable realizations may coexist. Moreover, we characterize the branch data of a decomposable primitive branched covering. Bibliography: 20 titles.

A novel physiological property of snake bradykinin-potentiating peptides-Reversion of MK-801 inhibition of nicotinic acetylcholine receptors

The first naturally occurring angiotensin-converting enzyme (ACE) inhibitors described are pyroglutamyl proline-rich oligopeptides, found in the venom of the viper Bothrops jararaca, and named as bradykinin-potentiating peptides (BPPs). Biochemical and pharmacological properties of these peptides were essential for the development of Captopril, the first active site-directed inhibitor of ACE, currently used for the treatment of human hypertension. However, a number of data have suggested that the pharmacological activity of BPPs could not only be explained by their inhibitory action on enzymatic activity of somatic ACE. In fact, we showed recently that the strong and long-lasting anti-hypertensive effect of BPP-10c [

Insights into the Molecular Requirements for the Anti-obesity Activity of a Series of CB1 Ligands

Two-dimensional and 3D quantitative structure-activity relationships studies were performed on a series of diarylpyridines that acts as cannabinoid receptor ligands by means of hologram quantitative structure-activity relationships and comparative molecular field analysis methods. The quantitative structure-activity relationships models were built using a data set of 52 CB1 ligands that can be used as anti-obesity agents. Significant correlation coefficients (hologram quantitative structure-activity relationships: r 2 = 0.91, q 2 = 0.78; comparative molecular field analysis: r 2 = 0.98, q 2 = 0.77) were obtained, indicating the potential of these 2D and 3D models for untested compounds. The models were then used to predict the potency of an external test set, and the predicted (calculated) values are in good agreement with the experimental results. The final quantitative structure-activity relationships models, along with the information obtained from 2D contribution maps and 3D contour maps, obtained in this study are useful tools for the design of novel CB1 ligands with improved anti-obesity potency.

Artificial Neural Networks and the Study of the Psychoactivity of Cannabinoid Compounds

Cannabinoid compounds have widely been employed because of its medicinal and psychotropic properties. These compounds are isolated from Cannabis sativa (or marijuana) and are used in several medical treatments, such as glaucoma, nausea associated to chemotherapy, pain and many other situations. More recently, its use as appetite stimulant has been indicated in patients with cachexia or AIDS. In this work, the influence of several molecular descriptors on the psychoactivity of 50 cannabinoid compounds is analyzed aiming one obtain a model able to predict the psychoactivity of new cannabinoids. For this purpose, initially, the selection of descriptors was carried out using the Fisher`s weight, the correlation matrix among the calculated variables and principal component analysis. From these analyses, the following descriptors have been considered more relevant: E(LUMO) (energy of the lowest unoccupied molecular orbital), Log P (logarithm of the partition coefficient), VC4 (volume of the substituent at the C4 position) and LP1 (Lovasz-Pelikan index, a molecular branching index). To follow, two neural network models were used to construct a more adequate model for classifying new cannabinoid compounds. The first model employed was multi-layer perceptrons, with algorithm back-propagation, and the second model used was the Kohonen network. The results obtained from both networks were compared and showed that both techniques presented a high percentage of correctness to discriminate psychoactive and psychoinactive compounds. However, the Kohonen network was superior to multi-layer perceptrons.