OBJECTIVES: The non-homogenous aspect of periodontal ligament (PDL) has been examined using finite element analysis (FEA) to better simulate PDL behavior. The aim of this study was to assess, by 2-D FEA, the influence of non-homogenous PDL on the stress distribution when the free-end saddle removable partial denture (RPD) is partially supported by an osseointegrated implant. MATERIAL AND METHODS: Six finite element (FE) models of a partially edentulous mandible were created to represent two types of PDL (non-homogenous and homogenous) and two types of RPD (conventional RPD, supported by tooth and fibromucosa; and modified RPD, supported by tooth and implant [10.00x3.75 mm]). Two additional Fe models without RPD were used as control models. The non-homogenous PDL was modeled using beam elements to simulate the crest, horizontal, oblique and apical fibers. The load (50 N) was applied in each cusp simultaneously. Regarding boundary conditions the border of alveolar ridge was fixed along the x axis. The FE software (Ansys 10.0) was used to compute the stress fields, and the von Mises stress criterion (svM) was applied to analyze the results. RESULTS: The peak of svM in non-homogenous PDL was higher than that for the homogenous condition. The benefits of implants were enhanced for the non-homogenous PDL condition, with drastic svM reduction on the posterior half of the alveolar ridge. The implant did not reduce the stress on the support tooth for both PDL conditions. Conclusion: The PDL modeled in the non-homogeneous form increased the benefits of the osseointegrated implant in comparison with the homogeneous condition. Using the non-homogenous PDL, the presence of osseointegrated implant did not reduce the stress on the supporting tooth.

O objetivo deste estudo foi avaliar a influência dos altos valores de torque de inserção na distribuição de tensões e deformações no osso cortical e medular. Com base em imagens de tomografia computadorizada, um modelo matemático representativo de um segmento da maxila foi construído utilizando os programas Mimics 11.11 e Solid Works 2010. Seis modelos foram construídos e cada um recebeu um implante com os seguintes torques de inserção no hexágono externo: 30, 40, 50, 60, 70 ou 80 Ncm. O osso cortical e medular foi considerado anisotrópico. A interface osso/implante foi considerada perfeitamente unida. A análise numérica foi realizada através do Ansys Workbench 10.0. A convergência de análise (6%) determinou o refinamento da malha. A tensão máxima principal (σmax) e a deformação máxima principal (εmax) foram obtidos para o osso cortical e medular ao redor do implante. O teste de correlação de Pearson foi utilizado para determinar a correlação entre torque de inserção e de concentração de tensões e deformações no tecido ósseo peri-implantar, considerando o nível de significância de 5%. O aumento no torque de inserção gerou um aumento nos valores σmax e εmax para o osso cortical e medular. O σmax foi menor para o osso medular, com maior variação de tensão entre os torques de inserção. O εmax foi maior no osso medular em relação ao osso cortical. De acordo com a metodologia utilizada e com os limites do estudo, pode-se concluir que torques alto de inserção aumentou as concentrações de tensões de tração e compressão no tecido ósseo peri-implantar.

The aim of this study was to evaluate the influence of the high values of insertion torques on the stress and strain distribution in cortical and cancellous bones. Based on tomography imaging, a representative mathematical model of a partial maxilla was built using Mimics 11.11 and Solid Works 2010 softwares. Six models were built and each of them received an implant with one of the following insertion torques: 30, 40, 50, 60, 70 or 80 Ncm on the external hexagon. The cortical and cancellous bones were considered anisotropic. The bone/implant interface was considered perfectly bonded. The numerical analysis was carried out using Ansys Workbench 10.0. The convergence of analysis (6%) drove the mesh refinement. Maximum principal stress (σmax) and maximum principal strain (εmax) were obtained for cortical and cancellous bones around to implant. Pearson's correlation test was used to determine the correlation between insertion torque and stress concentration in the periimplant bone tissue, considering the significance level at 5%. The increase in the insertion torque generated an increase in the σmax and εmax values for cortical and cancellous bone. The σmax was smaller for the cancellous bone, with greater stress variation among the insertion torques. The εmax was higher in the cancellous bone in comparison to the cortical bone. According to the methodology used and the limits of this study, it can be concluded that higher insertion torques increased tensile and compressive stress concentrations in the periimplant bone tissue.

El valor estético de una restauración cerámica es parcialmente influenciado por múltiples factores. Muchos de estos factores son conocidos por el odontólogo, sin embargo, existen otros , tales como: grosor de la cerámica, color del agente cementante, color de la estructura dental subyacente, limitaciones de las guías de colores disponibles, composición de los materiales cerámicos, etc. que pueden interferir en la correcta selección del color, siendo un problema clínico muy común. El propósito de este trabajo fue hacer una revisión bibliográfica sobre la influencia de los factores físicos y sensoriales y como estos pueden afectar en la selección del color de las restauraciones protésicas con el fin de evitarlos o disminuirlos

The esthetic value of dental ceramic restorations is influenced by several factors. Many factors is know by the dentist , however, others factors like: ceramic thickness, luting agent, shade guides, color of dental substrate, the limited range of available ceramic shades, varying compositions of ceramic materials, is a common clinical problem. The aim of this study was made a literature review of the influence of physical and sensorial factors on the shade selection in ceramics restorations for avoid or minimize these factors