High-intensity interval training increase GATA4, CITED4 and c-Kit and decreases C/EBPβ in rats after myocardial infarction

Aim: Myocardial infarction (MI), an important cause of morbidity and mortality, can be followed by left ventricular dysfunction and cardiomyocyte loss. Cardiac repair mechanisms may subsequently improve left ventricular function. Exercise training has been suggested to have cardioprotective effects against MI damage, but detailed knowledge is lacking on the effects of different types and intensities of exercise training on molecular targets of cardiomyocyte regeneration. Main methods: MI was induced in male Wistar rats by ligating the left anterior descending coronary artery. After MI induction, the rats were randomly assigned to one of five groups: sham operated, and experimental MI followed by no exercise, or low, moderate or high intensity exercise Cardiac function and infarct size were assessed by echocardiography and Evans blue/TTC staining, respectively. The expression of mRNA markers and proteins associated with myocardial regeneration was measured with RT-PCR and western blotting. Key findings: Exercise training at different intensities improved cardiac function and levels of stem cell and cardiomyocyte markers, and reduced infarct size. mRNA levels of GATA4, Nkx2.5 and c-Kit and protein expression of Nkx2.5 and c-Kit were significantly increased in all MI-exercise groups. The high-intensity exercise group had greater increases than the low and moderate intensity exercise groups. In the high-intensity exercise group, Sca-1 and CITED4 increased more than in the low-intensity exercise group. C/EBPβ mRNA and protein levels decreased after exercise training, with greater reductions in the high-intensity exercise group than the low- or moderate-intensity groups. Significance: The findings suggest that by targeting cardiogenesis, high-intensity training can exert cardioprotective effects against cardiac dysfunction in an experimental model of MI. © 2019 Elsevier Inc

Prediction of water inflow into underground excavations in fractured rocks using a 3D discrete fracture network (DFN) model

© 2017, Saudi Society for Geosciences. Groundwater flow is a major issue in underground opening in fractured rocks. Because of finding the fracture connectivity, contribution of each fracture in flow, and fracture connectivity to excavation boundary, the prediction of water flow to underground excavations is difficult. Simulation of fracture characteristics and spatial distribution is necessary to obtain realistic estimation of inflow quantity to tunnel and underground excavations. In this research, a computer code for three-dimensional discrete fracture network modeling of water inflow into underground excavations was developed. In this code, the fractures are simulated as ellipsoid while geometrical properties of the fractures are reproduced using a stochastic method. Properties such as the size, orientation, and density of the fractures are modeled by their respective probability distributions, which are obtained from field measurements. According to the fracture condition, the flow paths in rock mass are determined. The flow paths are considered as channels with rectangular sections in which channel width and fracture aperture determine geometry of channel section. Inflow into excavation is predicted ignoring matrix permeability and considering the hydrogeological conditions. To verify presented model, simulation results were compared to a part of the Cheshmeh-Roozieh water transfer tunnel in Iran. The results obtained from this research are in good agreement with the field data. Thus, the average of the predicted inflow has just an approximation error equal to 17.8%, and its standard deviation is 8.6 l/s, which is equal to 21% of the observed value that demonstrates low dispersion of the predicted values

ESICM LIVES 2016: part two : Milan, Italy. 1-5 October 2016.

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