Clinical Characteristics, Radiological Features and Gene Mutation in 10 Chinese Families with Spinocerebellar Ataxias / 中华医学杂志(英文版)

<p><b>BACKGROUND</b>Spinocerebellar ataxias (SCAs) are a group of neurodegenerative disorders that primarily cause the degeneration in the cerebellum, spinal cord, and brainstem. We study the clinical characteristics, radiological features and gene mutation in Chinese families with SCAs.</p><p><b>METHODS</b>In this study, we investigated 10 SCAs Chinese families with SCA1, SCA3/Machado-Joseph disease (MJD), SCA7, SCA8. There were 27 people who were genetically diagnosed as SCA, of which 21 people showed clinical symptoms, and 6 people had no clinical phenotype that we called them presymptomatic patients. In addition, 3 people with cerebellar ataxia and cataracts were diagnosed according to the Harding diagnostic criteria but failed to be recognized as SCAs on genetic testing. Clinical characteristic analyses of each type of SCAs and radiological examinations were performed.</p><p><b>RESULTS</b>We found that SCA3/MJD was the most common subtype in Han population in China, and the ratio of the pontine tegmentum and the posterior fossa area was negatively correlated with the number of cytosine-adenine-guanine (CAG) repeats; the disease duration was positively correlated with the International Cooperative Ataxia Rating Scale score; and the CAG repeats number of abnormal alleles was negatively correlated with the age of onset.</p><p><b>CONCLUSIONS</b>Collectively our study is a systematic research on SCAs in China, which may help for the clinical diagnosis and prenatal screening of this disease, and it may also aid toward better understanding of this disease.</p>

Segmentation of the prostate ultrasound image based on an improved geodesic active contour model / 中国医疗器械杂志

In this paper, we propose that, the need of the costly re-initialization procedure can be completely eliminated by using the variation formulation, thus increasing the speed of computing operations. The edge detecting function in the geodesic active contour model is improved by incorporating a prior knowledge. The accuracy of the segmentation algorithm can be enhanced by using the minimal variance. Experimental results show that the proposed algorithm can segment the prostate ultrasound images effectively and avoid the problem of contour leakage.