Aim/Background: Glioma is a malignant brain tumor with the characteristics of rapid growth, diffuse invasion and therapeutic resistance. MicroRNAs (miRNAs) recently have be studied for the treatment of glioma. Here, we conducted cell-based experiments to analyze the role of miR-425-5p
by targeting RAB2B in glioma though regulating the proliferation, invasion and migration of glioma cells. Methods: The qRT-PCR analysis detected the expression level of miR-425-5p in glioma cells. The transfection efficiency was verified by qRT-PCR. Cell viability, cell apoptosis, and
the expression of cell cycle regulators were determined by CCK-8, flow cytometry and western blot analysis, respectively. And, the invasion and migration of glioma cells were assessed by wound-healing experiment and transwell assay. Result: Among five kinds of human glioma cell lines
(U251, SHG44, LN229, T98G), the U251 cell line was chosen for the subsequent experiment. MiR-425-5p overexpression inhibited the proliferation, invasion and migration of glioma cells and promoted the glioma cells apoptosis. In addition, RAB2B was demonstrated to be a target of miR-129-5p.
RAB2B inhibition could also inhibited the proliferation, invasion and migration of glioma cells and promoted the glioma cells apoptosis. Conclusion: Our findings suggested that miR-425-5p could inhibit the proliferation, invasion and migration, and promoted apoptosis of glioma cells
by downregulation of RAB2B.
Reducing energy consumption while providing a high-quality environment for building occupants has become an important target worthy of consideration in the pre-design stage. A reasonable design can achieve both better performance and energy conservation. Parametric design tools show potential to integrate performance simulation and control elements into the early design stage. The large number of design scheme iterations, however, increases the computational load and simulation time, hampering the search for optimized solutions. This paper proposes an integration of parametric design and optimization methods with performance simulation, machine learning, and algorithmic generation. Architectural schemes were modeled parametrically, and numerous iterations were generated systematically and imported into neural networks. Generative Adversarial Networks (GANs) were used to predict environmental performance based on the simulation results. Then, multi-object optimization can be achieved through the fast evolution of the genetic algorithm binding with the database. The test case used in this paper demonstrates that this approach can solve the optimization problem with less time and computational cost, and it provides architects with a fast and easily implemented tool to optimize design strategies based on specific environmental objectives.
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