Background: The endometrium plays a pivotal role in implantation and pregnancy. Cyclooxygenase II (COX-2) has an important function in biological processes such as cell proliferation and inflammation. Celecoxib is a selective inhibitor of COX-2 with numerous pharmacologic functions. The aim of present study is to investigate the effects of celecoxib on the human endometrium in a three-Dimensional (3D) culture Model.Materials and Methods: In this experimental study, normal human endometria (n=10) obtained from reproductive age women were cut into 1×1 mm sections. Endometrial explants were placed between two layers of fibrin gel. To create the fibrin gel, we poured a thin layer of fibrinogen solution [3 mg/ml in medium 199 (M199)] into each well of a 24-well culture dish and added thrombin enzyme. Endometrial fragments were placed in the center of each well and covered with a second layer of fibrinogen solution. M199 supplemented with L-glutamine, fetal bovine serum (FBS, 5%) and antibiotics were added to each well. The media in each experimental well contained either1, 10 or 50 mM of celecoxib. At the end of the study, we calculated endometrial tissue growth changes by scoring methods and determined the percentage of angiogenesis. Data were analyzed by the Kruskal-Wallis method. P<0.05 was considered significant.Results: The growth scores were as folLows: control (1.37±0.16), 1 mM (1.96±0.28), 10 mM (2.01±0.25), and 50 mM (1.17±0.14) celecoxib, all of which were significantly different. The angiogenesis percentages were: 25.56±6.72% (control), 31.98±6.18% (1 mM), 42.67±7.27% (10 mM) and 23.44±4.03% (50 mM), which were not significantly different from each other.Conclusion: Lower celecoxib concentrations had stimulatory effects on the growth of normal endometrium.

This paper presents a new Model to study the friction between rough surfaces with random distribution of the asperities, taking into account the contact mechanics. The results obtained show that as the surface separation decreases, the normal and friction forces increase and the coefficient of friction decreases. This Model predicts higher friction forces and coefficient of friction than the Model based on the Hertzian contact Model. The sensitivity of the coefficient of friction to material properties is investigated using two sets of material properties. Assuming that the standard deviation and the radius of the asperities are constant, the first set investigates the variation of the adhesion energy, length of Burgers vectors, and elastic modulus parameters for the base material silicon. In the second set, real materials such as silicon, Fe, Cu, Au, and Ag are studied in contact with a silicon substrate. The results show that the friction coefficient decreases with the increase of the adhesion energy and increases with the increase of the length of Burgers vectors and elastic modulus.

This study was an attempt to apply a multiDimensional Model of cooperative learning to motivate students in five steps and increase their engagement in active classroom activities. However, it was conducted in face-to-face (F2F) contexts. We believe the findings are applicable in virtual and hybrid contexts....

Nowadays, we face a huge number of high-Dimensional data in different applications and technologies. To tackle the challenge, various feature selection methods have been recently proposed for reducing the computational complexity of the learning algorithms as well as simplifying the learning Models. Maintaining the geometric structures and considering the discriminative information in data are two important factors that should be borne in mind particularly for unsupervised feature selection methods. In this paper, our aim is to propose a new unsupervised feature selection approach by considering global and local similarities and discriminative information. Furthermore, this unsupervised framework incorporates cluster analysis to consider the underlying structure of the samples. Moreover, the correlation of features and clusters is computed by an
 , -norm regularized regression to eliminate the redundant and irrelevant features. Finally, a unified objective function is presented as well as an efficient iterative optimization algorithm to solve the corresponding problem with some theoretical analysis of the convergence and the complexity of the algorithm. We compare the proposed approach with the state-of-the-art method based on clustering results on the various standard datasets including biology, image, voice, and artificial data. The experimental results have presented the strength and performance improvement of the proposed method by outperforming the well-known methods.