MTHFR Polymorphic Variant C677T Is Associated to Vascular Complications in Sickle-Cell Disease

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Vaso-occlusion is a determinant for most signs and symptoms of sickle-cell anemia (SCA). The mechanisms involved in the pathogenesis of vascular complications in SCA remain unclear. It is known that genetic polymorphisms associated with thrombophilia may be potential modifiers of clinical features of SCA. The genetic polymorphisms C677T and A1298C relating to the enzyme methylenetetrahydrofolate reductase (MTHFR), a clotting Factor V Leiden mutation (1691G -> A substitution of Factor V Leiden), and the mutant prothrombin 20210A allele were analyzed in this study. The aim was to find possible correlations with vascular complications and thrombophilia markers in a group of SCA patients in Pernambuco, Brazil. The study included 277 SCA patients, divided into two groups: one consisting of 177 nonconsanguineous SCA patients who presented vascular manifestations of stroke, avascular necrosis, leg ulcers, priapism, and acute chest syndrome (group 1); and the other consisting of 100 SCA patients without any reported vascular complication (group 2). Molecular tests were done using either polymerase chain reaction (PCR) restriction fragment length polymorphism or allele-specific PCR techniques. Comparisons between the groups were made using the chi(2) test. The 677 CT and TT genotypes showed a significant risk of vascular complications (p = 0.015). No significant associations between the groups were found when samples were analyzed for the MTHFR A1298C allele (p = 0.913), Factor V G1691 (p = 0.555), or prothrombin G20210A mutation (p = 1.000). The polymorphism MTHFR C677T seemed to be possibly predictive for the development of some vascular complications in SCA patients among this population.16910381043Science and Technology Support Foundation of the State of Pernambuco (Fundacao de Amparo a Ciencia e Tecnologia do Estado de PernambucoFACEPE)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

UV friendly T-parity in the SU(6)/Sp(6) little Higgs model

Electroweak precision tests put stringent constraints on the parameter space of little Higgs models. Tree-level exchange of TeV scale particles in a generic little Higgs model produce higher dimensional operators that make contributions to electroweak observables that are typically too large. To avoid this problem a discrete symmetry dubbed T-parity can be introduced to forbid the dangerous couplings. However, it was realized that in simple group models such as the littlest Higgs model, the implementation of T-parity in a UV completion could present some challenges. The situation is analogous to the one in QCD where the pion can easily be defined as being odd under a new $Z_2$ symmetry in the chiral Lagrangian, but this $Z_2$ is not a symmetry of the quark Lagrangian. In this paper we examine the possibility of implementing a T-parity in the low energy $SU(6)/Sp(6)$ model that might be easier to realize in the UV. In our model, the T-parity acts on the low energy non-linear sigma model field in way which is different to what was originally proposed for the Littlest Higgs, and lead to a different low energy theory. In particular, the Higgs sector of this model is a inert two Higgs doublets model with an approximate custodial symmetry. We examine the contributions of the various sectors of the model to electroweak precision data, and to the dark matter abundance.Comment: 21 pages,4 figures. Clarifications added, typos corrected and references added. Published in JHE

Astrobiological Complexity with Probabilistic Cellular Automata

Search for extraterrestrial life and intelligence constitutes one of the major endeavors in science, but has yet been quantitatively modeled only rarely and in a cursory and superficial fashion. We argue that probabilistic cellular automata (PCA) represent the best quantitative framework for modeling astrobiological history of the Milky Way and its Galactic Habitable Zone. The relevant astrobiological parameters are to be modeled as the elements of the input probability matrix for the PCA kernel. With the underlying simplicity of the cellular automata constructs, this approach enables a quick analysis of large and ambiguous input parameters' space. We perform a simple clustering analysis of typical astrobiological histories and discuss the relevant boundary conditions of practical importance for planning and guiding actual empirical astrobiological and SETI projects. In addition to showing how the present framework is adaptable to more complex situations and updated observational databases from current and near-future space missions, we demonstrate how numerical results could offer a cautious rationale for continuation of practical SETI searches.Comment: 37 pages, 11 figures, 2 tables; added journal reference belo

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients

Background: Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Results: Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Conclusions: Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Figure not available: see fulltext. © 2015 Freitas et al.; licensee Springer