249 resultados para Vascular segmentation
Resumo:
BACKGROUND:
Increased superoxide anion production increases oxidative stress and reduces nitric oxide bioactivity in vascular disease states. NAD(P)H oxidase is an important source of superoxide in human blood vessels, and some studies suggest a possible association between polymorphisms in the NAD(P)H oxidase CYBA gene and atherosclerosis; however, no functional data address this hypothesis. We examined the relationships between the CYBA C242T polymorphism and direct measurements of superoxide production in human blood vessels.
METHODS AND RESULTS:
Vascular NAD(P)H oxidase activity was determined in human saphenous veins obtained from 110 patients with coronary artery disease and identified risk factors. Immunoblotting, reverse-transcription polymerase chain reaction, and DNA sequencing showed that p22phox protein, mRNA, and 242C/T allelic variants are expressed in human blood vessels. Vascular superoxide production, both basal and NADH-stimulated, was highly variable between patients, but the presence of the CYBA 242T allele was associated with significantly reduced vascular NAD(P)H oxidase activity, independent of other clinical risk factors for atherosclerosis.
CONCLUSIONS:
Association of the CYBA 242T allele with reduced NAD(P)H oxidase activity in human blood vessels suggests that genetic variation in NAD(P)H oxidase components may play a significant role in modulating superoxide production in human atherosclerosis.
Resumo:
Tissue microarray (TMA) is a high throughput analysis tool to identify new diagnostic and prognostic markers in human cancers. However, standard automated method in tumour detection on both routine histochemical and immunohistochemistry (IHC) images is under developed. This paper presents a robust automated tumour cell segmentation model which can be applied to both routine histochemical tissue slides and IHC slides and deal with finer pixel-based segmentation in comparison with blob or area based segmentation by existing approaches. The presented technique greatly improves the process of TMA construction and plays an important role in automated IHC quantification in biomarker analysis where excluding stroma areas is critical. With the finest pixel-based evaluation (instead of area-based or object-based), the experimental results show that the proposed method is able to achieve 80% accuracy and 78% accuracy in two different types of pathological virtual slides, i.e., routine histochemical H&E and IHC images, respectively. The presented technique greatly reduces labor-intensive workloads for pathologists and highly speeds up the process of TMA construction and provides a possibility for fully automated IHC quantification.
