Increased Cardiac Alpha-1-Adrenoceptor Density in Rats Following Treatment with Amiodarone

Budu CE, Balas N, Nawrath H, Wegener J, Shainberg A. Increased Cardiac Alpha-1-Adrenoceptor Density in Rats Following Treatment with Amiodarone. Journal of Basic and Clinical Physiology and Pharmacology. 2001;12(1):33-48.This study was undertaken to investigate the interaction between amiodarone and alpha-1-adrenoceptors in rat cardiac cells. The level (Bmax) and affinity (Kd) of alpha-1-adrenoceptors in heart cells were determined by [3H]prazosin radioligand binding following amiodarone treatment. In cultured intact cardiocytes treated for 48 h with 10 μΜ amiodarone, [3H]prazosin binding increased by 31% compared with the control cells (p<0.05). The increase was both dose and time dependent and was found to be specific because no significant change occurred in creatine kinase activity. Additionally, under the same conditions, an increase in [3H]prazosin binding to cultured cardiocyte cell membranes was also obtained. Oral gavage of amiodarone to rats for 8 d resulted in a 25% increase in [3H]prazosin binding to isolated ventricle membranes compared with control rats (p<0.05). We conclude that amiodarone treatment can increase the response to alpha-1-adrenoceptors agonist in the heart due to an increase in the density of alpha-1-adrenoceptors

Separation algorithms for 0-1 knapsack polytopes

Valid inequalities for 0-1 knapsack polytopes often prove useful when tackling hard 0-1 Linear Programming problems. To generate such inequalities, one needs separation algorithms for them, i.e., routines for detecting when they are violated. We present new exact and heuristic separation algorithms for several classes of inequalities, namely lifted cover, extended cover, weight and lifted pack inequalities. Moreover, we show how to improve a recent separation algorithm for the 0-1 knapsack polytope itself. Extensive computational results, on MIPLIB and OR Library instances, show the strengths and limitations of the inequalities and algorithms considered

Valid inequalities and restrictions for stochastic programming problems with first order stochastic dominance constraints

Stochastic dominance relations are well-studied in statistics, decision theory and economics. Recently, there has been significant interest in introducing dominance relations into stochastic optimization problems as constraints. In the discrete case, stochastic optimization models involving second order stochastic dominance (SSD) constraints can be solved by linear programming (LP). However, problems involving first order stochastic dominance (FSD) constraints are potentially hard due to the non-convexity of the associated feasible regions. In this paper we consider a mixed 01 linear programming formulation of a discrete FSD-constrained optimization model and present an LP relaxation based on SSD constraints. We derive some valid inequalities and restrictions by employing the probabilistic structure of the problem. We also generate cuts that are valid inequalities for the disjunctive relaxations arising from the underlying combinatorial structure of the problem by applying the lift-and-project procedure. We describe three heuristic algorithms to construct feasible solutions, based on conditional SSD-constraints, variable fixing, and conditional value at risk. Finally, we present numerical results for several instances of a portfolio optimization problem