6-Meth­oxy-2-methyl-1-phenyl-1H-indole-3-carbonitrile

In the title compound, C17H14N2O, the dihedral angle between the indole ring system and the phenyl ring is 64.48 (7)°. The crystal packing features weak C—H⋯π inter­actions

2-Benzyl-6-chloro-1-(4-methyl­phen­yl)-1H-indole-3-carbonitrile

In the title compound, C23H17ClN2, the dihedral angle between the indole ring and the attached tolyl ring is 86.97 (8)°. Weak C—H⋯N(nitrile) hydrogen bonding, and C—H⋯π(aromatic) and short Cl⋯π(aromatic) [3.628 (1) Å] inter­actions consolidate the crystal packing

1-(4-Meth­oxy­phen­yl)-2-methyl-1H-indole-3-carbonitrile

In the title compound, C17H14N2O, the dihedral angle between the indole ring system and the benzene ring is 58.41 (4)°. The crystal packing features π–π stacking [shortest centroid–centroid separation = 3.8040 (9) Å] and C—H⋯π inter­actions

Is interleukin-18 associated with polycystic ovary syndrome?

<p>Abstract</p> <p>Background</p> <p>Recent research show that polycystic ovary syndrome (PCOS) may have an association with low-grade chronic inflammation, IL-18 is considered as a strong risk marker of inflammation.</p> <p>Methods</p> <p>To investigate serum IL-18 concentrations in PCOS patients and focus on its relationship between obesity and insulin resistance (IR). Sixty consecutive women with PCOS and thirty controls were recruited. Serum level of IL-18 and fasting blood glucose, fasting insulin, follicle-stimulating hormone (FSH), luteinizing hormone (LH) and testosterone (T) were measured.</p> <p>Results</p> <p>Serum levels of IL-18 was significantly higher in the PCOS group than in the control group. Serum level of IL-18 was higher in the PCOS group with IR than in the PCOS group without IR. Serum level of IL-18 was higher in obese PCOS patients than in lean PCOS patients. Serum level of IL-18 was higher in lean PCOS patients than in the lean control group. Serum level of IL-18 in the PCOS group was positively related to BMI, IR index and T.</p> <p>Conclusion</p> <p>IL-18 level was increased in PCOS patients, and correlated with insulin resistance, obesity and hyperandrogenism.</p

Higher-order Graph Attention Network for Stock Selection with Joint Analysis

Stock selection is important for investors to construct profitable portfolios. Graph neural networks (GNNs) are increasingly attracting researchers for stock prediction due to their strong ability of relation modelling and generalisation. However, the existing GNN methods only focus on simple pairwise stock relation and do not capture complex higher-order structures modelling relations more than two nodes. In addition, they only consider factors of technical analysis and overlook factors of fundamental analysis that can affect the stock trend significantly. Motivated by them, we propose higher-order graph attention network with joint analysis (H-GAT). H-GAT is able to capture higher-order structures and jointly incorporate factors of fundamental analysis with factors of technical analysis. Specifically, the sequential layer of H-GAT take both types of factors as the input of a long-short term memory model. The relation embedding layer of H-GAT constructs a higher-order graph and learn node embedding with GAT. We then predict the ranks of stock return. Extensive experiments demonstrate the superiority of our H-GAT method on the profitability test and Sharp ratio over both NSDAQ and NYSE datasetsComment: 12 pages, 6 figures