Expressão Imuno-histoquímica das proteínas GLUT-1 e HIF-1α em lesões vasculares de mucosa oral

The correct histological diagnosis of vascular lesions in the oral mucosa is critical, especially in defining the treatment and prognosis, as some vascular lesions show spontaneous involution and others do not show such behavior. This study analyzed the expression immunohistochemistry of human glucose transporter protein (GLUT-1), in oral benign vascular tumors and to reclassify such lesions according to with his immunoexpression. In addition, we evaluated the immunohistochemical expression of hypoxia-inducible factor 1 alpha (HIF-1α), the main transcription factor involved in cellular adaptation to hypoxia. We analyzed 60 cases of benign oral vascular tumors: 30 cases with histological diagnosis of HEM and 30 cases of oral pyogenic granuloma (PG). The results of this research showed that of the 30 lesions initially classified as HEM, only 7 showed immuno-positivity for GLUT-1, remaining with the initial diagnosis. The remaining 23 were reclassified as vascular malformation (VM) (13 cases) and PG (10 cases). All cases in the sample with an initial diagnosis of PG were negative for GLUT-1, demonstrating the accuracy of histological diagnosis of these lesions. Concerning to the immunoexpression of HIF-1α, the Mann-Whitney test revealed a statistically significant difference between the cases of GP and MV (p = 0.002), where the median of GP (m=78) was higher than the MV (m=53). Based on these results, this study showed that a histological diagnosis alone is not always sufficient for the correct diagnosis of oral HEM and that HIF-1α participates in the pathogenesis of vascular lesions

Paralelização em GPU da segmentação vascular com extração de Centerlines por Height Ridges

The vascular segmentation is important in diagnosing vascular diseases like stroke and is hampered by noise in the image and very thin vessels that can pass unnoticed. One way to accomplish the segmentation is extracting the centerline of the vessel with height ridges, which uses the intensity as features for segmentation. This process can take from seconds to minutes, depending on the current technology employed. In order to accelerate the segmentation method proposed by Aylward [Aylward & Bullitt 2002] we have adapted it to run in parallel using CUDA architecture. The performance of the segmentation method running on GPU is compared to both the same method running on CPU and the original Aylward s method running also in CPU. The improvemente of the new method over the original one is twofold: the starting point for the segmentation process is not a single point in the blood vessel but a volume, thereby making it easier for the user to segment a region of interest, and; the overall gain method was 873 times faster running on GPU and 150 times more fast running on the CPU than the original CPU in Aylward