Aims/Introduction To investigate the impact of visceral adipose tissue (VAT) on atherosclerosis in type 2 diabetes patients with normal bodyweight (OB[−]) in the Chinese population, and to further assess the sex–age differences between them. Materials and Methods A total of 8,839 type 2 diabetes patients from two of the National Metabolic Management Centers in China were included in this study. Participants were classified into four groups by visceral fat area (VFA; cm 2 ) and body mass index (BMI; kg/m 2 ): VFA < 100 and BMI < 23.9 (VA[−]OB[−]), VFA < 100 and BMI ≥ 23.9 (VA[−]OB[+]), VFA ≥ 100 and BMI < 23.9 (VA[+]OB[−]), VFA ≥ 100 and BMI ≥ 23.9 (VA[+]OB[+]). Atherosclerosis was defined by brachial‐ankle pulse wave velocity (baPWV; cm/s), and we analyzed the association between VFA, BMI and the tertiles of baPWV values. Results The VA(+)OB(−) prevalence was 3.7% among these participants. Patients with VA(+)OB(−) had the highest baPWV value ( P < 0.001) and the highest proportion of the tertile 3 of baPWV ( P < 0.001) among four groups, and were significantly associated with baPWV (standardized β = 0.026, P = 0.008). VFA was significantly related to tertile 2 to tertile 3 of baPWV in (OB[−]) type 2 diabetes patients, when compared with tertile 1 of baPWV, respectively. In sex–age stratified analysis, the association of VFA and the tertiles of baPWV showed sex differences. For the 55 years age stratification analysis, there was no age difference in the relationship between VFA and baPWV in (OB[−]) patients. Conclusion Increased VAT was an independent risk factor for atherosclerosis in female type 2 diabetes patients with normal weight.

Pentaprism scanning system has been widely used in the measurement of large flat and wavefront, based on its property that the deviated beam will have no motion in the pitch direction. But the manufacturing and position errors of pentaprisms will bring error to the measurement and so a good error analysis method is indispensable. In this paper, we propose a new method of building mathematic models of pentaprism and through which the size and angle errors of a pentaprism can be put into the model as parameters. 4 size parameters are selected to determine the size and 11 angle parameters are selected to determine the angles of a pentaprism. Yaw, Roll and Pitch are used to describe the position error of a pentaprism and an autocollimator. A pentaprism scanning system of wavefront test is simulated by ray tracing using matlab. We design a method of separating the constant from the measurement results which will improve the measurement accuracy and analyze the system error by Monte Carlo method. This method is simple, rapid, accurate and convenient for computer programming.

A design method of improving imaging quality for infrared holographic optical elements (HOEs), in which a cylindrical lens is added into object beam to form hyperbolic wavefront is researched. Here,we temporarily name this HOEs recorded by aberrant wavefront or aspherical wavefront as AWHOEs to discriminate HOEs recorded by sphreical wavefront.

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Background and objectives Both fractional flow reserve (FFR) and instantaneous wave-free ratio (iFR) are widely used to evaluate ischemia-causing coronary lesions. A new method of CT-iFR, namely AccuiFRct, for calculating iFR based on deep learning and computational fluid dynamics (CFD) using coronary computed tomography angiography (CCTA) has been proposed. In this study, the diagnostic performance of AccuiFRct was thoroughly assessed using iFR as the reference standard. Methods Data of a total of 36 consecutive patients with 36 vessels from a single-center who underwent CCTA, invasive FFR, and iFR were retrospectively analyzed. The CT-derived iFR values were computed using a novel deep learning and CFD-based model. Results Mean values of FFR and iFR were 0.80 ± 0.10 and 0.91 ± 0.06, respectively. AccuiFRct was well correlated with FFR and iFR (correlation coefficients, 0.67 and 0.68, respectively). The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of AccuiFRct ≤ 0.89 for predicting FFR ≤ 0.80 were 78%, 73%, 81%, 73%, and 81%, respectively. Those of AccuiFRct ≤ 0.89 for predicting iFR ≤ 0.89 were 81%, 73%, 86%, 79%, and 82%, respectively. AccuiFRct showed a similar discriminant function when FFR or iFR were used as reference standards. Conclusion AccuiFRct could be a promising noninvasive tool for detection of ischemia-causing coronary stenosis, as well as facilitating in making reliable clinical decisions.