4-Chloro-5-[(5,5-dimethyl-4,5-dihydro­isoxazol-3-yl)sulfonyl­meth­yl]-3-methyl-1-(2,2,2-trifluoro­ethyl)-1H-pyrazole

The mol­ecule of the title compound, C12H15ClF3N3O3S, is twisted, as indicated by the C—S—C—C torsion angle of 66.00 (18)° for the atoms linking the ring systems. An intra­molecular C—H⋯F short contact occurs. In the crystal, non-classical C—H⋯O inter­actions, one of which has a short H⋯O contact of 2.28 Å, link the mol­ecules

High Body Mass Index Is Associated with an Increased Risk of the Onset and Severity of Ossification of Spinal Ligaments

BackgroundOssification of the posterior longitudinal ligament (OPLL) and that of ligamentum flavum (OLF) are the main types of the ossification of spinal ligaments (OSL) that cause the thoracic myelopathy. Although several studies have investigated the relationship of body mass index (BMI) with the onset or severity of OSL, it remains unverified due to the contradictory results of existing evidence. A systematic review and meta-analysis were performed in this work to determine the relationship of BMI with the onset and severity of OSL.MethodsPubMed, EMBASE, Web of Science, and Cochrane Library were comprehensively searched online for relevant studies focusing on the relationship of BMI with the onset or severity of the OSL. The difference in BMI of OSL (or severe OSL group) and non-OSL (or nonsevere OSL group) groups was evaluated using the mean difference (MD) with a corresponding 95% confidence interval (CI).ResultsFifteen studies were included in this systematic review and meta-analysis. The BMI of the OSL group was significantly higher than that of the non-OSL group (MD = 1.70 kg/m2, 95% CI = 1.02–2.39 kg/m2, and P < 0.01). Similar results were observed in the subgroup analysis of female (P < 0.01), OPLL (P < 0.01), and OLF (P < 0.01) populations. Three studies reported a significant association of BMI with the ossification index of OSL and the standardized regression coefficient ranging from 0.11 to 0.43 (P < 0.05). Moreover, a significantly higher BMI was observed in the severe OSL group compared with that in the nonsevere OSL group (MD = 3.09, 95% CI, 0.22–5.97 kg/m2, and P = 0.04).ConclusionThe significant association of high BMI with the onset and severity of OSL may provide new evidence and insights into the mechanism research and management of OSL

Diagnostic value of two dimensional shear wave elastography combined with texture analysis in early liver fibrosis.

BACKGROUND: Staging diagnosis of liver fibrosis is a prerequisite for timely diagnosis and therapy in patients with chronic hepatitis B. In recent years, ultrasound elastography has become an important method for clinical noninvasive assessment of liver fibrosis stage, but its diagnostic value for early liver fibrosis still needs to be further improved. In this study, the texture analysis was carried out on the basis of two dimensional shear wave elastography (2D-SWE), and the feasibility of 2D-SWE plus texture analysis in the diagnosis of early liver fibrosis was discussed. AIM: To assess the diagnostic value of 2D-SWE combined with textural analysis in liver fibrosis staging. METHODS: This study recruited 46 patients with chronic hepatitis B. Patients underwent 2D-SWE and texture analysis; Young\u27s modulus values and textural patterns were obtained, respectively. Textural pattern was analyzed with regard to contrast, correlation, angular second moment (ASM), and homogeneity. Pathological results of biopsy specimens were the gold standard; comparison and assessment of the diagnosis efficiency were conducted for 2D-SWE, texture analysis and their combination. RESULTS: 2D-SWE displayed diagnosis efficiency in early fibrosis, significant fibrosis, severe fibrosis, and early cirrhosis (AUC \u3e 0.7, P \u3c 0.05) with respective AUC values of 0.823 (0.678-0.921), 0.808 (0.662-0.911), 0.920 (0.798-0.980), and 0.855 (0.716-0.943). Contrast and homogeneity displayed independent diagnosis efficiency in liver fibrosis stage (AUC \u3e 0.7, P \u3c 0.05), whereas correlation and ASM showed limited values. AUC of contrast and homogeneity were respectively 0.906 (0.779-0.973), 0.835 (0.693-0.930), 0.807 (0.660-0.910) and 0.925 (0.805-0.983), 0.789 (0.639-0.897), 0.736 (0.582-0.858), 0.705 (0.549-0.883) and 0.798 (0.650-0.904) in four liver fibrosis stages, which exhibited equivalence to 2D-SWE in diagnostic efficiency (P \u3e 0.05). Combined diagnosis (PRE) displayed diagnostic efficiency (AUC \u3e 0.7, P \u3c 0.01) for all fibrosis stages with respective AUC of 0.952 (0.841-0.994), 0.896 (0.766-0.967), 0.978 (0.881-0.999), 0.947 (0.835-0.992). The combined diagnosis showed higher diagnosis efficiency over 2D-SWE in early liver fibrosis (P \u3c 0.05), whereas no significant differences were observed in other comparisons (P \u3e 0.05). CONCLUSION: Texture analysis was capable of diagnosing liver fibrosis stage, combined diagnosis had obvious advantages in early liver fibrosis, liver fibrosis stage might be related to the hepatic tissue hardness distribution

Surrogate Model of Predicting Eigenvalue and Power Distribution by Convolutional Neural Network

During loading pattern (LP) optimization and reactor design, a lot of time consumption spent on evaluation is one of the key issues. In order to solve this issue, the surrogate models are investigated in this paper. The convolutional neural network (CNN) and fully convolutional network (FCN) are adopted to predict the eigenvalue and the assembly-wise power distribution (PD) for a simplified pressurized water reactor (PWR) during depletion, respectively. For the eigenvalue prediction during depletion, the error in the begin of cycle (BOC) and middle of cycle (MOC) is higher than that in the end of cycle (EOC). For the BOC and MOC, the samples with discrepancy over 500 pcm are less than 1%, except four burnup points. For the EOC, the fraction of samples with error over 500 pcm is less than 1%. As for the error of assembly power, the average absolute error is on the same level for all test cases. The average absolute relative error in the center region and the peripheral region is higher than that in the inter-ring region. The prediction results indicate the capability of neural network to predict core parameters