Kumiko Seto

A common Ile 823 Met variant of ATP-binding cassette transporter A1 gene (ABCA1) alters high density lipoprotein cholesterol level in Japanese population

Recently, variants in ATP-binding cassette transporter A1 (ABCA1) were demonstrated to be associated with increased level of high density lipoprotein cholesterol (HDL-C) and decreased risk of coronary artery disease (CAD) in Caucasians. However, this is not universally applicable due to the ethnic or environmental differences. In this context, to clarify the effect of ABCA1 in Japanese, we evaluated the phenotypic effects of I/M 823 and R/K 219 variants on the plasma level of HDL-C in 410 patients recruited in our hospital. Subjects with M 823 allele had significantly higher level of HDL-C than those without M823 allele (49.0+/-15.1 vs. 44.9+/-11.5 mg/dl, respectively, P<0.05). This statistical significance did not change even after multiple regression analysis. In contrast, there was no difference in HDL-C level among the genotypes in R/K 219 polymorphism. Further, in our study population an inverse relationship was shown to exist between HDL-C level and incidence of CAD. However, no positive association was observed between those variants and susceptibility to CAD. In this study, we provide evidence that I/M 823 variant, not R/K 219 variant, in ABCA1 is one of the determinants of HDL-C level, suggesting the importance of this gene on lipid metabolism in Japanese.

Efficient registration method of medical images using GPU

Registration of medical images is an important task; however, automatic image-based registration is computationally expensive. Given this task, the authors propose an efficient rigid registration method, which is based on mutual information and uses a graphics processing unit (GPU). Mutual-information-based registration methods require joint-histogram computation. Although a GPU can provide high performance computing, a joint histogram has a large number of bins, and the computation of such a histogram is not suitable for a GPU (whose shared memory is limited). Taking advantage of the fact that one image (the reference image) is not transformed during the registration process, the proposed method computes a joint histogram by computing multiple onedimensional histograms and combining them. The method can therefore be efficiently implemented on a GPU even with limited shared memory. Experimental results for 256 × 256 × 256 image registration show that the method is about 140 times faster than a standard implementation on a CPU and 2.6 times faster than previous methods using GPUs.