Objectives
Esculetin is a coumarin derivative, which is extracted from the dried barks of fraxinus chinensis Roxb. Although it is reported esculetin possesses multiple pharmacological activities, its associated regulatory mechanism on ovarian cancer isn’t well investigated.
Methods
Cytotoxicity is evaluated by MTT, clonogenic and living/dead cells staining assays. Migration and invasion effects are investigated by wound healing, and transwell assays. The effect of cell cycle and apoptosis are analyzed by flow cytometry and western blotting. Mitochondrial membrane potential and intracellular reactive oxygen species (ROS) is assessed by fluorescence microscope. Analysis of animal experiments are carried out by various pathological section assays.
Key findings
Esculetin exerts an anti- ovarian cancer effect. It is found that apoptosis induction is promoted by the accumulation of excessive ROS and inhibition of JAK2/STAT3 signalling pathway. In addition, exposure to esculetin leads to the cell viability reduction, migration and invasion capability decrease and G0/G1 phase cell cycle arrest induced by down-regulating downstream targets of STAT3. In vivo experimental results also indicate esculetin can inhibit tumour growth of mice.
Conclusions
Our study provides some strong evidences to support esculetin as a potential anti-cancer agent in ovarian cancer.
The dam displacement is related to multiple factors such as time, temperature, water level and etc. And it presents a strong nonlinear and certain randomness.Neural network model because of its inherent characteristics can better simulate the dam displacement.Nowadays,It has methods to estimate the displacement of the dam by constructing physical model and BP neural network model.But BP neural network's training time is too long and the forecast effect is not very good.So this paper introduces Elm neural network model,establishs Elm neural network model of dam displacement early warning considering multiple factors to estimate the displacement.By a simple example and compared with BP neural network model to reflect the rationality and scientificity of this method.
The utilization degree of irrigation water is the key indicator of agricultural water-saving efficiency. Currently, water efficiency of irrigation is used to reflect the utilization degree of irrigation water, people commonly uses hydrodynamic measurement and static water measurement, but the result is still affected by many factors. This thesis is based on the method of extenics that combines with various kinds of irrigated area characteristics, realizes the comprehensive evaluation of irrigation and irrigation water utilization. And a simple example shows that is more reasonable compared with traditional methods.
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