• The Korean Journal of Physiology and Pharmacology
• /
• 제24권4호
• /
• pp.299-310
• /
• 2020

Alzheimer's disease (AD) is a multi-faceted neurodegenerative disease. Thus, current therapeutic strategies require multitarget-drug combinations to treat or prevent the disease. At the present time, single drugs have proven to be inadequate in terms of addressing the multifactorial pathology of AD, and multitarget-directed drug design has not been successful. Based on these points of views, it is judged that combinatorial drug therapies that target several pathogenic factors may offer more attractive therapeutic options. Thus, we explored that the combination therapy with lower doses of cilostazol and aripiprazole with add-on donepezil (CAD) might have potential in the pathogenesis of AD. In the present study, we found the superior efficacies of donepezil add-on with combinatorial mixture of cilostazol plus aripiprazole in modulation of expression of AD-relevant genes: Aβ accumulation, GSK-3β, P300, acetylated tau, phosphorylated-tau levels, and activation of α-secretase/ADAM 10 through SIRT1 activation in the N2a Swe cells expressing human APP Swedish mutation (N2a Swe cells). We also assessed that CAD synergistically raised acetylcholine release and choline acetyltransferase (CHAT) expression that were declined by increased β-amyloid level in the activated N2a Swe cells. Consequently, CAD treatment synergistically increased neurite elongation and improved cell viability through activations of PI3K, BDNF, β-catenin and α7-nicotinic cholinergic receptors in neuronal cells in the presence of Aβ_1-42. This work endorses the possibility for efficient treatment of AD by supporting the synergistic therapeutic potential of donepezil add-on therapy in combination with lower doses of cilostazol and aripiprazole.

• Journal of Mechanical Science and Technology
• /
• 제19권5호
• /
• pp.1072-1079
• /
• 2005

Conceptual design is the first step in the overall process of product design. Its intrinsic uncertainty, imprecision, and lack of information lead to the fact that current conceptual design activities in engineering have not been computerized and very few CAD systems are available to support conceptual design. In most of the current intelligent design systems, approach of principle synthesis, such as morphology matrix, bond graphic, or design catalogues, is usually adopted to deal with the concept generation, in which optional concepts are generally combined and enumerated through function analysis. However, as a large number of concepts are generated, it is difficult to evaluate and optimize these design candidates using regular algorithm. It is necessary to develop a new approach or a tool to solve the concept generation. Generally speaking, concept generation is a problem of concept synthesis. In substance, this process of developing design candidate is a combinatorial optimization process, viz., the process of concept generation can be regarded as a solution for a state-place composed of multi-concepts. In this paper, genetic algorithm is utilized as a feasible tool to solve the problem of combinatorial optimization in concept generation, in which the encoding method of morphology matrix based on function analysis is applied, and a sequence of optimal concepts are generated through the search and iterative process which is controlled by genetic operators, including selection, crossover, mutation, and reproduction in GA. Several crucial problems on GA are discussed in this paper, such as the calculation of fitness value and the criteria for heredity termination, which have a heavy effect on selection of better concepts. The feasibility and intellectualization of the proposed approach are demonstrated with an engineering case. In this work concept generation is implemented using GA, which can facilitate not only generating several better concepts, but also selecting the best concept. Thus optimal concepts can be conveniently developed and design efficiency can be greatly improved.

• 한국경영과학회:학술대회논문집
• /
• 한국경영과학회 2001년도 추계학술대회 논문집
• /
• pp.253-256
• /
• 2001

Today's rapid development in the computer and network technology makes the environment which enables the companies to consider their decisions on the wide point of view and enables the software vendors to make the software packages to help these decisions. To make these software packages, many algorithms should be developed. The production and distribution planning problem belongs to those problems that industry manufacturers daily face in organizing their overall production plan. However, this combinatorial optimization problem can not be solved optimally in a reasonable time when large instances are considered. This legitimates the search for heuristic techniques. As one of these heuristic techniques, genetic algorithm has been considered in many researches. A standard genetic algorithm is a problem solving method that apply the rules of reproduction, gene crossover, and mutation to these pseudo-organisms so those organisms can Pass beneficial and survival-enhancing traits to new generation. This standard genetic algorithm should not be applied to this problem directly because when we represent the chromosome of this problem, there may exist high epitasis between genes. So in this paper, we proposed the hybrid genetic algorithm which turns out to better result than standard genetic algorithms