Synergistic acceleration of thyroid hormone degradation by phenobarbital and the PPAR alpha agonist WY14643 in rat hepatocytes

Energy balance is maintained by controlling both energy intake and energy expenditure. Thyroid hormones play a crucial role in regulating energy expenditure. Their levels are adjusted by a tight feed back-control led regulation of thyroid hormone production/incretion and by their hepatic metabolism. Thyroid hormone degradation has previously been shown to be enhanced by treatment with phenobarbital or other antiepileptic drugs due to a CAR-dependent induction of phase 11 enzymes of xenobiotic metabolism. We have recently shown, that PPAR alpha agonists synergize with phenobarbital to induce another prototypical CAR target gene, CYP2B1. Therefore, it was tested whether a PPAR alpha agonist could enhance the phenobarbital-dependent acceleration of thyroid hormone elimination. In primary cultures of rat hepatocytes the apparent half-life of T3 was reduced after induction with a combination of phenobarbital and the PPARa agonist WY14643 to a larger extent than after induction with either Compound alone. The synergistic reduction of the half-life could be attributed to a synergistic induction of CAR and the CAR target genes that code for enzymes and transporters involved in the hepatic elimination of T3, such as OATP1A1, OATP1A3, UGT1A3 and UCT1A10. The PPAR alpha-dependent CAR induction and the subsequent induction of T3-eliminating enzymes might be of physiological significance for the fasting-incluced reduction in energy expenditure by fatty acids as natural PPARa ligands. The synergism of the PPAR alpha agonist WY14643 and phenobarbital in inducing thyroid hormone breakdown might serve as a paradigm for the synergistic disruption of endocrine control by other combinations of xenobiotics. (C) 2009 Elsevier Inc. All rights reserved.

Nonexistence of global solutions for a class of complex vector fields on two-torus

The goal of this paper is study the global solvability of a class of complex vector fields of the special form L = partial derivative/partial derivative t + (a + ib)(x)partial derivative/partial derivative x, a, b epsilon C(infinity) (S(1) ; R), defined on two-torus T(2) congruent to R(2)/2 pi Z(2). The kernel of transpose operator L is described and the solvability near the characteristic set is also studied. (c) 2008 Elsevier Inc. All rights reserved.

Sensitivity of complex networks measurements

Complex networks obtained from real-world networks are often characterized by incompleteness and noise, consequences of imperfect sampling as well as artifacts in the acquisition process. Because the characterization, analysis and modeling of complex systems underlain by complex networks are critically affected by the quality and completeness of the respective initial structures, it becomes imperative to devise methodologies for identifying and quantifying the effects of the sampling on the network structure. One way to evaluate these effects is through an analysis of the sensitivity of complex network measurements to perturbations in the topology of the network. In this paper, measurement sensibility is quantified in terms of the relative entropy of the respective distributions. Three particularly important kinds of progressive perturbations to the network are considered, namely, edge suppression, addition and rewiring. The measurements allowing the best balance of stability (smaller sensitivity to perturbations) and discriminability (separation between different network topologies) are identified with respect to each type of perturbation. Such an analysis includes eight different measurements applied on six different complex networks models and three real-world networks. This approach allows one to choose the appropriate measurements in order to obtain accurate results for networks where sampling bias cannot be avoided-a very frequent situation in research on complex networks.

Border trees of complex networks

The comprehensive characterization of the structure of complex networks is essential to understand the dynamical processes which guide their evolution. The discovery of the scale-free distribution and the small-world properties of real networks were fundamental to stimulate more realistic models and to understand important dynamical processes related to network growth. However, the properties of the network borders (nodes with degree equal to 1), one of its most fragile parts, remained little investigated and understood. The border nodes may be involved in the evolution of structures such as geographical networks. Here we analyze the border trees of complex networks, which are defined as the subgraphs without cycles connected to the remainder of the network (containing cycles) and terminating into border nodes. In addition to describing an algorithm for identification of such tree subgraphs, we also consider how their topological properties can be quantified in terms of their depth and number of leaves. We investigate the properties of border trees for several theoretical models as well as real-world networks. Among the obtained results, we found that more than half of the nodes of some real-world networks belong to the border trees. A power-law with cut-off was observed for the distribution of the depth and number of leaves of the border trees. An analysis of the local role of the nodes in the border trees was also performed.