Nanog is a potential stem cell marker and is considered a regeneration factor during tissue repair. In the present study, we investigated expression patterns of nanog in the rat heart after acute myocardial infarction by semi-quantitative RT-PCR, immunohistochemistry and Western blot analyses. Our results show that nanog at both mRNA and protein levels is positively expressed in myocardial cells, fibroblasts and small round cells in different myocardial zones at different stages after myocardial infarction, showing a spatio-temporal and dynamic change. After myocardial infarction, the nanog expression in fibroblasts and small round cells in the infarcted zone (IZ) is much stronger than that in the margin zone (MZ) and remote infarcted zone (RIZ). From day 7 after myocardial infarction, the fibroblasts and small cells strongly expressed nanog protein in the IZ, and a few myocardial cells in the MZ and the RIZ and the numbers of nanog-positive fibroblasts and small cells reached the highest peak at 21 days after myocardial infarction, but in this period the number of nanog-positive myocardial cells decreased gradually. At 28 days after myocardial infarction, the numbers of all nanog-positive cells decreased into a low level. Therefore, our data suggest that all myocardial cells, fibroblasts and small round cells are involved in myocardial reconstruction after cardiac infarction. The nanog-positive myocardial cells may respond to early myocardial repair, and the nanog-positive fibroblasts and small round cells are the main source for myocardial reconstruction after cardiac infarction.
Some chemicals in the environment possess the potential to interact with the endocrine system in the human body. Multiple receptors are involved in the endocrine system; estrogen receptor α (ERα) plays very important roles in endocrine activity and is the most studied receptor. Understanding and predicting estrogenic activity of chemicals facilitates the evaluation of their endocrine activity. Hence, we have developed a decision forest classification model to predict chemical binding to ERα using a large training data set of 3308 chemicals obtained from the U.S. Food and Drug Administration's Estrogenic Activity Database. We tested the model using cross validations and external data sets of 1641 chemicals obtained from the U.S. Environmental Protection Agency's ToxCast project. The model showed good performance in both internal (92% accuracy) and external validations (∼ 70-89% relative balanced accuracies), where the latter involved the validations of the model across different ER pathway-related assays in ToxCast. The important features that contribute to the prediction ability of the model were identified through informative descriptor analysis and were related to current knowledge of ER binding. Prediction confidence analysis revealed that the model had both high prediction confidence and accuracy for most predicted chemicals. The results demonstrated that the model constructed based on the large training data set is more accurate and robust for predicting ER binding of chemicals than the published models that have been developed using much smaller data sets. The model could be useful for the evaluation of ERα-mediated endocrine activity potential of environmental chemicals.
The biomass of Urochloa mutica was subjected to thermal degradation analyses to understand its pyrolytic behavior for bioenergy production. Thermal degradation experiments were performed at three different heating rates, 10, 30 and 50°Cmin-1 using simultaneous thermogravimetric-differential scanning calorimetric analyzer, under an inert environment. The kinetic analyses were performed using isoconversional models of Kissenger-Akahira-Sunose (KAS) and Flynn-Wall-Ozawa (FWO). The high heating value was calculated as 15.04MJmol-1. The activation energy (E) values were shown to be ranging from 103 through 233 kJmol-1. Pre-exponential factors (A) indicated the reaction to follow first order kinetics. Gibbs free energy (ΔG) was measured to be ranging from 169 to 173kJmol-1 and 168 to 172kJmol-1, calculated by KAS and FWO methods, respectively. We have shown that Para grass biomass has considerable bioenergy potential comparable to established bioenergy crops such as switchgrass and miscanthus.