048, P＜0.001), RI (β=0.144, P＜0.001), and PI (β=0.103, P＜0.001) but not with mean IMT. AUCins (β=-0.064, P＜0.001) and HOMA-β (β=-0.054, P＜0.001) were significantly and negatively associated with CCA interadventitial diameter, but not with mean IMT. Both HOMA-IR and Matsuda index were significantly associated with RI and PI.
 
  These findings indicate that all CCA parameters except IMT are associated with impaired glucose metabolism in patients without cardiovascular disease.
 These findings indicate that all CCA parameters except IMT are associated with impaired glucose metabolism in patients without cardiovascular disease.
  This study aimed to elucidate the gene and lipid profiles of children clinically diagnosed with familial hypercholesterolemia (FH).
 
  A total of 21 dyslipidemia-related Mendelian genes, including FH causative genes (LDLR, APOB, and PCSK9) and LDL-altering genes (APOE, LDLRAP1, and ABCG5/8), were sequenced in 33 Japanese children (mean age, 9.7±4.2 years) with FH from 29 families.
 
  Fifteen children (45.5%) with pathogenic variants in LDLR (eight different heterozygous variants) and one child (3.0%) with the PCSK9 variant were found. Among 17 patients without FH causative gene variants, 3 children had variants in LDL-altering genes, an APOE variant and two ABCG8 variants. The mean serum total cholesterol (280 vs 246 mg/dL), LDL-cholesterol (LDL-C, 217 vs 177 mg/dL), and non-HDL cholesterol (228 vs 188 mg/dL) levels were significantly higher in the pathogenic variant-positive group than in the variant-negative group. In the variant-positive group, 81.3% of patients had LDL-C levels ≥ 180 mg/dL but 35.3% in the variant-negative group. The mean LDL-C level was significantly lower in children with missense variants, especially with the p.Leu568Val variant, than in children with other variants in LDLR, whereas the LDL-altering variants had similar effects on the increase in serum LDL-C to LDLR p.Leu568Val.
 
  Approximately half of the children clinically diagnosed with FH had pathogenic variants in FH causative genes. The serum LDL-C levels tend to be high in FH children with pathogenic variations, and the levels are by the types of variants. Genetic analysis is useful; however, further study on FH without any variants is required.
 Approximately half of the children clinically diagnosed with FH had pathogenic variants in FH causative genes. The serum LDL-C levels tend to be high in FH children with pathogenic variations, and the levels are by the types of variants. Genetic analysis is useful; however, further study on FH without any variants is required.Mammography equipment attached to the digital breast tomosynthesis (DBT) system is widespread in Japan. However, there are no guidelines for quality control methods for DBT in Japan.  https://www.selleckchem.com/products/z-vad(oh)-fmk.html  Therefore, it is necessary to rapidly establish a performance evaluation procedure and a quality control procedure for DBT. In this study, we conducted basic experiments using DBTs of five companies (Canon Medical, Fujifilm Medical, GE Healthcare, Hologic, Siemens) already sold in Japan and examined feasible common items. We aimed to establish a quality control method for DBT in Japan. The measurement was performed based on the European Reference Organisation for Quality Assured Breast Screening and Diagnostic Services (EUREF) breast tomosynthesis quality control protocol, version 1.03. In this study, we tried to measure 18 items in DBT. We examined whether the 18 items could be measured using each device; it is not an evaluation of device performance based on the measured values. There were some management items that were difficult to implement due to the specifications of DBT, such as devices that required pressure on DBT operation, problems due to the shape of bucky, and devices that did not have stationary mode. There were also problems with measurement data; for example, devices could not retrieve projection data and reconstruction data. This study clarified points to be considered for establishing common quality control items. In the future, we will carefully refer to the recently published IEC 61223-3-6, consider international harmonization, and establish DBT guidelines customized for the Japanese market.Recently, diffusion-weighted imaging (DWI) has become essential for diagnosing acute cerebral infractions and detecting lesions via magnetic resonance imaging (MRI). Investigations using phantoms have been performed to evaluate the optimizing parameters before clinical practice. However, there have been no studies on extracting appropriate phantom materials. It is known that the apparent diffusion coefficient (ADC) changes with temperature. To extract optimal materials from polyethylene glycol, sucrose, and dextrin in previous studies, evaluations were performed using ADC with temperature change and signal-to-noise ratio (SNR) . Results of comparison with difference between true and measured values depend on the Stokes-Einstein formula for ADC change with temperature change; the highest value was obtained for polyethylene glycol. In the SNR measurement, when the temperature increased, the rate of change of ADC decreased. Polyethylene glycol showed the highest value. According to these results, it can be concluded that polyethylene glycol can be extracted when nearest to true value and when there is a high SNR, thus making polyethylene glycol the most suitable material for diffusion-weighted image phantoms.
  We focused on deep learning for a reduction of motion artifacts in MRI. It is difficult to collect a large number of images with and without motion artifacts from clinical images. The purpose of this study was to create motion artifact images in MRI by simulation.
 
  We created motion artifact images by computer simulation. First, 20 different types of vertical pixel-shifted images were created with different shifts, and the amount of pixel shift was set from -10 to 10 pixels. The same method was used to create pixel-shifted images for horizontal shift, diagonal shift, and rotational shift, and a total of 80 types of pixel-shifted images were prepared. These images were Fourier transformed to create 80 types of k-space data. Then, phase encodings in these k-space data were randomly sampled and Fourier transformed to create artifact images. The reproducibility of the simulation images was verified using the deep learning network model of U-net. In this study, the evaluation indices used were the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR).