Synthesis, characterization, electrochemical behavior and in vitro protein tyrosine kinase inhibitory activity of the cymene-halogenobenzohydroxamato [Ru(eta(6)-cymene)(bha)Cl] complexes

The ruthenium(II)-cymene complexes [Ru(eta(6)-cymene)(bha)Cl] with substituted halogenobenzohydroxamato (bha) ligands (substituents = 4-F, 4-Cl, 4-Br, 2,4-F-2, 3,4-F-2, 2,5-F-2, 2,6-F-2) have been synthesized and characterized by elemental analysis, IR, H-1 NMR, C-13 NMR, cyclic voltammetry and controlled-potential electrolysis, and density functional theory (DFT) studies. The compositions of their frontier molecular orbitals (MOs) were established by DFT calculations, and the oxidation and reduction potentials are shown to follow the orders of the estimated vertical ionization potential and electron affinity, respectively. The electrochemical E-L Lever parameter is estimated for the first time for the various bha ligands, which can thus be ordered according to their electron-donor character. All complexes exhibit very strong protein tyrosine kinase (PTK) inhibitory activity, even much higher than that of genistein, the clinically used PTK inhibitory drug. The complex containing the 2,4-difluorobenzohydroxamato ligand is the most active one, and the dependences of the PTK activity of the complexes and of their redox potentials on the ring substituents are discussed. (C) 2012 Elsevier B.V. All rights reserved.

The D1822V APC polymorphism interacts with fat, calcium, and fiber intakes in modulating the risk of colorectal cancer in Portuguese persons

Background - Both genetic and environmental factors affect the risk of colorectal cancer (CRC). Objective - We aimed to examine the interaction between the D1822V polymorphism of the APC gene and dietary intake in persons with CRC. Design - Persons with CRC (n = 196) and 200 healthy volunteers, matched for age and sex in a case-control study, were evaluated with respect to nutritional status and lifestyle factors and for the D1822V polymorphism. Results - No significant differences were observed in energy and macronutrient intakes. Cases had significantly (P < 0.05) lower intakes of carotenes, vitamins C and E, folate, and calcium than did controls. Fiber intake was significantly (P = 0.004) lower in cases than in controls, whereas alcohol consumption was associated with a 2-fold risk of CRC. In addition, cases were significantly (P = 0.001) more likely than were controls to be sedentary. The homozygous variant for the APC gene (VV) was found in 4.6% of cases and in 3.5% of controls. Examination of the potential interactions between diet and genotype found that a high cholesterol intake was associated with a greater risk of colorectal cancer only in noncarriers (DD) of the D1822V APC allele (odds ratio: 1.66; 95% CI: 1.00, 2.76). In contrast, high fiber and calcium intakes were more markedly associated with a lower risk of CRC in patients carrying the polymorphic allele (DV/VV) (odds ratio: 0.50; 95% CI: 0.27, 0.94 for fiber; odds ratio: 0.51; 95% CI: 0.28, 0.93 for calcium) than in those without that allele. Conclusion - These results suggest a significant interaction between the D1822V polymorphism and the dietary intakes of cholesterol, calcium, and fiber for CRC risk.

A MAP approach to evidence accumulation clustering

The Evidence Accumulation Clustering (EAC) paradigm is a clustering ensemble method which derives a consensus partition from a collection of base clusterings obtained using different algorithms. It collects from the partitions in the ensemble a set of pairwise observations about the co-occurrence of objects in a same cluster and it uses these co-occurrence statistics to derive a similarity matrix, referred to as co-association matrix. The Probabilistic Evidence Accumulation for Clustering Ensembles (PEACE) algorithm is a principled approach for the extraction of a consensus clustering from the observations encoded in the co-association matrix based on a probabilistic model for the co-association matrix parameterized by the unknown assignments of objects to clusters. In this paper we extend the PEACE algorithm by deriving a consensus solution according to a MAP approach with Dirichlet priors defined for the unknown probabilistic cluster assignments. In particular, we study the positive regularization effect of Dirichlet priors on the final consensus solution with both synthetic and real benchmark data.