DMPP-evoked increases in postganglionic sympathetic nerve activity and blood pressure occurs by two mechanisms in the rat

1 Intravenous administration of the ganglionic nicotinic receptor agonist DMPP (1,1-dimethyl-4-phenylpiperazinium iodide) into urethane-anaesthetized rats evoked dose-dependent increases in mean arterial pressure (MAP) and renal sympathetic nerve activity (RSNA). 2 The ganglionic nicotinic receptor antagonists pentolinium and hexamethonium either alone or combined did not inhibit the increase in RSNA and MAP evoked by 50 to 200 μg kg^?1 doses of DMPP. The increase in renal sympathetic nerve activity evoked by DMPP occurred as a brief burst in firing. 3 The increase in MAP, but not RSNA, evoked by DMPP in the presence of pentolinium was inhibited by the selective α₁-adrenergic receptor antagonist prazosin. 4 The non-selective α-adrenoceptor and NPY receptor antagonist benextramine also inhibited the increase in MAP without inhibiting the increase in RSNA. Surprisingly, the combination of benextramine and pentolinium, or benextramine and hexamethonium, completely blocked the DMPP-evoked increase in RSNA and thus the increase in MAP. 5 The uptake₁ antagonist desipramine combined with pentolinium did not affect the DMPP-evoked increases in MAP or RSNA when compared to the responses evoked in the presence of pentolinium alone. 6 Adding the selective M₁ muscarinic receptor antagonist telenzepine to pentolinium and prazosin did not inhibit the increase in RSNA evoked by a 100 μg kg^?1 dose of DMPP. 7 While the DMPP-evoked increase in MAP in the presence of ganglionic nicotinic receptor antagonists is primarily dependent upon activation of α₁-adrenoceptors, the increase in RSNA occurs via activation of ganglionic nicotinic receptors and activation of a mechanism susceptible to blockade by benextramine.     

混合Poisson分布及其应用：疾病的统计分布（五）

本文讨论了混合Ｐｏｉｓｓｏｎ分布的性质、应用条件、参数的估计及混合Ｐｏｉｓｓｏｎ分布阶数的确定，指出混合Ｐｏｉｓｓｏｎ分布可用于混合样本的判别归类，并用Ｂａｙｅｓ的思想导出其判别归类方法。模拟试验结果表明：当混合Ｐｏｉｓｓｏｎ分布中各部分的比例相差不大，而各部分的均值相差较大时，抽样效果和拟合效果越好，所得到的估计值越接近理论值；反之效果越差。     

灰关联分析应用于因素分析时关联度指标的改进

本文指出在应用灰关联分析进行因素分析时传统的关联度计算方法的不足，并提出采用新的关联度指标来表示因素之间的关联关系，同时给出了实例介绍计算方法。