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DDC: 610 × Subject: Time Factors ×

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    The novel arylindolylmaleimide PDA-66 displays pronounced antiproliferative effects in acute lymphoblastic leukemia cells
    (London : BioMed Central, 2014) Kretzschmar, C.; Roolf, C.; Langhammer, T.-S.; Sekora, A.; Pews-Davtyan, A.; Beller, M.; Frech, M.J.; Eisenlöffel, C.; Rolfs, A.; Junghanss, C.
    Background: Prognosis of adult patients suffering from acute lymphoblastic leukemia (ALL) is still unsatisfactory. Targeted therapy via inhibition of deregulated signaling pathways appears to be a promising therapeutic option for the treatment of ALL. Herein, we evaluated the influence of a novel arylindolylmaleimide (PDA-66), a potential GSK3β inhibitor, on several ALL cell lines.Methods: ALL cell lines (SEM, RS4;11, Jurkat and MOLT4) were exposed to different concentrations of PDA-66. Subsequently, proliferation, metabolic activity, apoptosis and necrosis, cell cycle distribution and protein expression of Wnt and PI3K/Akt signaling pathways were analyzed at different time points.Results: PDA-66 inhibited the proliferation of ALL cells significantly by reduction of metabolic activity. The 72 h IC50 values ranged between 0.41 to 1.28 μM PDA-66. Additionally, caspase activated induction of apoptosis could be detected in the analyzed cell lines. PDA-66 influenced the cell cycle distribution of ALL cell lines differently. While RS4;11 and MOLT4 cells were found to be arrested in G2 phase, SEM cells showed an increased cell cycle in G0/1 phase.Conclusion: PDA-66 displays significant antileukemic activity in ALL cells and classifies as candidate for further evaluation as a potential drug in targeted therapy of ALL.
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    The stability of memristive multidirectional associative memory neural networks with time-varying delays in the leakage terms via sampled-data control
    (San Francisco, California, US : PLOS, 2018) Wang, Weiping; Yu, Xin; Luo, Xiong; Wang, Long; Li, Lixiang; Kurths, Jürgen; Zhao, Wenbing; Xiao, Jiuhong
    In this paper, we propose a new model of memristive multidirectional associative memory neural networks, which concludes the time-varying delays in leakage terms via sampled-data control. We use the input delay method to turn the sampling system into a continuous time-delaying system. Then we analyze the exponential stability and asymptotic stability of the equilibrium points for this model. By constructing a suitable Lyapunov function, using the Lyapunov stability theorem and some inequality techniques, some sufficient criteria for ensuring the stability of equilibrium points are obtained. Finally, numerical examples are given to demonstrate the effectiveness of our results.