Association of the XPD and XRCC3 gene polymorphisms with oral squamous cell carcinoma in a Northeastern Brazilian population: A pilot study

Objective to evaluate the association between XPD and XRCC3 polymorphisms and oral squamous cell carcinoma (OSCC). Design the sample consisted of 54 cases of OSCC and 40 cases of inflammatory fibrous hyperplasia (IFH). Genotypes were determined by the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. Results XPD-Lys/Gln was more common in IFH (n = 28; 70%) than in OSCC (n = 24; 44.4%) (OR: 0.3; p < 0.05). XPD-Gln was more frequent in high-grade lesions (0.48) than in low-grade lesions (0.21) (OR: 3.4; p < 0.05). The Gln/Gln genotype was associated with III and IV clinical stages (OR: 0.07; p < 0.05). XRCC3-Met was more frequent in OSCC (0.49) than in IFH (0.35) (OR: 2.6; p < 0.05). The Met/Met genotype was associated with the presence of metastases (OR: 8.1; p < 0.05) and with III and IV clinical stages (OR: 0.07; p < 0.05). Conclusions in this sample, the frequency of XPD-Gln in IFH suggests that this variant may protect against OSCC. The presence of the XRCC3-Met allele seems to contribute to the development of OSCC, metastases and more advanced stages in these lesions.

Mapeamento robótico 2,5-D com representação em grade de ocupação-elevação

This work introduces a new method for environment mapping with three-dimensional information from visual information for robotic accurate navigation. Many approaches of 3D mapping using occupancy grid typically requires high computacional effort to both build and store the map. We introduce an 2.5-D occupancy-elevation grid mapping, which is a discrete mapping approach, where each cell stores the occupancy probability, the height of the terrain at current place in the environment and the variance of this height. This 2.5-dimensional representation allows that a mobile robot to know whether a place in the environment is occupied by an obstacle and the height of this obstacle, thus, it can decide if is possible to traverse the obstacle. Sensorial informations necessary to construct the map is provided by a stereo vision system, which has been modeled with a robust probabilistic approach, considering the noise present in the stereo processing. The resulting maps favors the execution of tasks like decision making in the autonomous navigation, exploration, localization and path planning. Experiments carried out with a real mobile robots demonstrates that this proposed approach yields useful maps for robot autonomous navigation