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Coordination and crystallization molecules: their interactions affecting the dimensionality of metalloporphyrinic SCFs.

ABSTRACT: Synthetic metalloporphyrin complexes are often used as analogues of natural systems, and they can be used for the preparation of new Solid Coordination Frameworks (SCFs). In this work, a series of six metalloporphyrinic compounds constructed from different meso substituted metalloporphyrins (phenyl, carboxyphenyl and sulfonatophenyl) have been structurally characterized by means of single crystal X-ray diffraction, IR spectroscopy and elemental analysis. The compounds were classified considering the dimensionality of the crystal array, referred just to coordination bonds, into 0D, 1D and 2D compounds. This way, the structural features and relationships of those crystal structures were analyzed, in order to extract conclusions not only about the dimensionality of the networks but also about possible applications of the as-obtained compounds, focusing the interest on the interactions of coordination and crystallization molecules. These interactions provide the coordination bonds and the cohesion forces which produce SCFs with different dimensionalities.

SUBMITTER: Fidalgo-Marijuan A

PROVIDER: S-EPMC6272730 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Coordination and crystallization molecules: their interactions affecting the dimensionality of metalloporphyrinic SCFs.

Fidalgo-Marijuan Arkaitz A Amayuelas Eder E Barandika Gotzone G Bazán Begoña B Urtiaga Miren Karmele MK Arriortua María Isabel MI

Molecules (Basel, Switzerland) 20150415 4

Synthetic metalloporphyrin complexes are often used as analogues of natural systems, and they can be used for the preparation of new Solid Coordination Frameworks (SCFs). In this work, a series of six metalloporphyrinic compounds constructed from different meso substituted metalloporphyrins (phenyl, carboxyphenyl and sulfonatophenyl) have been structurally characterized by means of single crystal X-ray diffraction, IR spectroscopy and elemental analysis. The compounds were classified considering ...[more]

PMID: 25884550

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Project description:BackgroundRecently, one of the greatest challenges in genome-wide association studies is to detect gene-gene and/or gene-environment interactions for common complex human diseases. Ritchie et al. (2001) proposed multifactor dimensionality reduction (MDR) method for interaction analysis. MDR is a combinatorial approach to reduce multi-locus genotypes into high-risk and low-risk groups. Although MDR has been widely used for case-control studies with binary phenotypes, several extensions have been proposed. One of these methods, a generalized MDR (GMDR) proposed by Lou et al. (2007), allows adjusting for covariates and applying to both dichotomous and continuous phenotypes. GMDR uses the residual score of a generalized linear model of phenotypes to assign either high-risk or low-risk group, while MDR uses the ratio of cases to controls.MethodsIn this study, we propose multivariate GMDR, an extension of GMDR for multivariate phenotypes. Jointly analysing correlated multivariate phenotypes may have more power to detect susceptible genes and gene-gene interactions. We construct generalized estimating equations (GEE) with multivariate phenotypes to extend generalized linear models. Using the score vectors from GEE we discriminate high-risk from low-risk groups. We applied the multivariate GMDR method to the blood pressure data of the 7,546 subjects from the Korean Association Resource study: systolic blood pressure (SBP) and diastolic blood pressure (DBP). We compare the results of multivariate GMDR for SBP and DBP to the results from separate univariate GMDR for SBP and DBP, respectively. We also applied the multivariate GMDR method to the repeatedly measured hypertension status from 5,466 subjects and compared its result with those of univariate GMDR at each time point.ResultsResults from the univariate GMDR and multivariate GMDR in two-locus model with both blood pressures and hypertension phenotypes indicate best combinations of SNPs whose interaction has significant association with risk for high blood pressures or hypertension. Although the test balanced accuracy (BA) of multivariate analysis was not always greater than that of univariate analysis, the multivariate BAs were more stable with smaller standard deviations.ConclusionsIn this study, we have developed multivariate GMDR method using GEE approach. It is useful to use multivariate GMDR with correlated multiple phenotypes of interests.

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Coordination and crystallization molecules: their interactions affecting the dimensionality of metalloporphyrinic SCFs.

Publications

Coordination and crystallization molecules: their interactions affecting the dimensionality of metalloporphyrinic SCFs.

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