E2-BSA and G1 exert neuroprotective effects and improve behavioral abnormalities following traumatic brain injury: The role of classic and non-classic estrogen receptors.

The role of classical and non-classical estrogen receptors (ERs) in mediating the neuroprotective effects of this hormone on brain edema and long-term behavioral disorders was evaluated after traumatic brain injury (TBI). Ovariectomized rats were divided as follows: E2 (17 ß-estradiol), E2-BSA (E2 conjugated to bovine serum albumin), G1 [G-protein-coupled estrogen receptor agonist (GPER)] or their vehicle was injected following TBI, whereas ICI (classical estrogen receptor antagonist), G15 (GPER antagonist), ICI + G15, and their vehicle were injected before the induction of TBI and the injection of E2 and E2-BSA. Brain water (BWC) and Evans blue (EB) contents were measured 24 h and 5 h after TBI, respectively. Intracranial pressure (ICP) and cerebral perfusion pressure (CPP) were measured before and at different times after TBI. Locomotor activity, anxiety-like behavior, and spatial memory were assessed on days 3, 7, 14, and 21 after injury. E2, E2-BSA, and G1 prevented the increase of BWC and EB content after TBI, and these effects were inhibited by ICI and G15. ICI and G15 also inhibited the beneficial effects of E2, E2-BSA on ICP, as well as CPP, after trauma. E2, E2-BSA, and G1 prevented the cognitive deficiency and behavioral abnormalities induced by TBI. Similar to the above parameters, ICI and G15 also reversed this E2 and E2-BSA effects on days 3, 7, 14, and 21. Our findings indicated that the beneficial effects of E2-BSA and E2 were inhibited by both ICI and G15, suggesting that GPER and classic ERs were involved in mediating the long-term effects of E2.

Presenting a new method to improve the detection of micro-seismic events.

Seismic events such as earthquakes are one of the most important issues in the field of geology. Meanwhile, less attention has been paid to micro-seismic events, despite the high number of earthquakes. Earthquakes, regardless of their size, affect human life; therefore, their detection and management is considered an important issue. For this purpose, experts developed seismic arrays as a system of linked seismometers. These systems equipped with sensors and seismographs are able to receive a range of waves from the earth, which are then sent to the central seismic station for analysis. So far, many tools and methods have been devised to analyze seismic data. However, the dominant method in most seismic mechanisms is trigger function, based on STA/LTA (short-time-average through long-time-average trigger). These mechanisms have considerable threshold in terms of earthquake range, so many micro-events are ignored as noise. Generally, in this field of geology, computer science techniques have been used to detect and classify these events. Statistical methods such as kurtosis, variance, and skewness can be applied to understand the changes in the signal curves of geophones in a seismic event, thereby helping in the initial detection of fuzzy features. According to the last 3 years' reports of global data mining agencies such as Rexer, KDnugget, and Gartner, Rapid Miner is one of the most popular tools for data mining in recent years. Furthermore, these institutions considered artificial neural networks, especially multilayer perceptron (MLP) and base radial function (RBF), to be among the most successful algorithms for detection and classification of stream data. In this research, the recorded data from several seismic experiments has been classified by a hybrid model. Hence, the present study was aimed to enhance the authenticity of data based on the application of effective variables. This was undertaken through use of a fuzzy method and an integrated neural network algorithm, involving MLP perceptron and radial network of RBF in the form of a collective learning system, in order to identify seismic events on a small scale. Based on the results, in comparison to basic methods, the proposed method significantly improved using the actual error and root-mean-square error (RMSE) criteria.

Heparin/Collagen 3D Scaffold Accelerates Hepatocyte Differentiation of Wharton's Jelly-Derived Mesenchymal Stem Cells.

Both mature and stem cell-derived hepatocytes lost their phenotype and functionality under conventional culture conditions. However, the 3D scaffolds containing the main extracellular matrix constitutions, such as heparin, may provide appropriate microenvironment for hepatocytes to be functional. The current study aimed to investigate the efficacy of the differentiation capability of hepatocytes derived from human Wharton's jelly mesenchymal stem cells (WJ-MSCs) in 3D heparinized scaffold. In this case, the human WJ-MSCs were cultured on the heparinized and non-heparinized 2D collagen gels or within 3D scaffolds in the presence of hepatogenic medium. Immunostaining was performed for anti-alpha fetoprotein, cytokeratin-18 and -19 antibodies. RT-PCR was performed for detection of hepatic nuclear factor-4 (HNF-4), albumin, cytokeratin-18 and -19, glucose-6-phosphatase (G6P), c-met and Cyp2B. The results indicated that hepatogenic media induced the cells to express early liver-specific markers including HNF4, albumin, cytokeratin-18 and 19 in all conditions. The cells cultured on both heparinized culture conditions expressed late liver-specific markers such as G6P and Cyp2B as well. Besides, the hepatocytes differentiated in 3D heparinized scaffolds stored more glycogen that indicated they were more functional. Non-heparinized 2D gel was the superior condition for cholangiocyte differentiation as indicated by higher levels of cytokeratin 19 expression. In conclusion, the heparinized 3D scaffolds provided a microenvironment to mimic Disse space. Therefore, 3D heparinized collagen scaffold can be suggested as a good vehicle for hepatocyte differentiation.