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Toxicity of Dopamine to Striatal Neurons In Vitro and Potentiation of Cell Death by a Mitochondrial Inhibitor 总被引:8,自引:2,他引:6
B. A. McLaughlin †D. Nelson †‡M. Ereciska † M.-F. Chesselet 《Journal of neurochemistry》1998,70(6):2406-2415
Abstract: Intrastriatal injections of the mitochondrial toxins malonate and 3-nitropropionic acid produce selective cell death similar to that seen in transient ischemia and Huntington's disease. The extent of cell death can be attenuated by pharmacological or surgical blockade of cortical glutamatergic input. It is not known, however, if dopamine contributes to toxicity caused by inhibition of mitochondrial function. Exposure of primary striatal cultures to dopamine resulted in dose-dependent death of neurons. Addition of medium supplement containing free radical scavengers and antioxidants decreased neuronal loss. At high concentrations of the amine, cell death was predominantly apoptotic. Methyl malonate was used to inhibit activity of the mitochondrial respiratory chain. Neither methyl malonate (50 µ M ) nor dopamine (2.5 µ M ) caused significant toxicity when added individually to cultures, whereas simultaneous addition of both compounds killed 60% of neurons. Addition of antioxidants and free radical scavengers to the incubation medium prevented this cell death. Dopamine (up to 250 µ M ) did not alter the ATP/ADP ratio after a 6-h incubation. Methyl malonate, at 500 µ M , reduced the ATP/ADP ratio by ∼30% after 6 h; this decrease was not augmented by coincubation with 25 µ M dopamine. Our results suggest that dopamine causes primarily apoptotic death of striatal neurons in culture without damaging cells by an early adverse action on oxidative phosphorylation. However, when combined with minimal inhibition of mitochondrial function, dopamine neurotoxicity is markedly enhanced. 相似文献
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Solving the N-Queens problem with a binary Hopfield-type network 总被引:3,自引:0,他引:3
Jacek Mańdziuk 《Biological cybernetics》1995,72(5):439-445
The application of a discrete Hopfield-type neural network to solving the NP-Hard optimization problem — the N-Queens Problem (NQP) — is presented. The applied network is binary, and at every moment each neuron potential is equal to either 0 or 1. The network can be implemented in the asynchronous mode as well as in the synchronous one with n parallel running processors. In both cases the convergence rate is up to 100%, and the experimental estimate of the average computational complexity is polynomial. Based on the computer simulation results and the theoretical analysis, the proper network parameters are established. The behaviour of the network is explained. 相似文献
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