Short-Term Creatine Supplementation May Alleviate the Malnutrition-Inflammation Score and Lean Body Mass Loss in Hemodialysis Patients: A Pilot Randomized Placebo-Controlled Trial

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this recordBackground Creatine supplementation has been proposed to alleviate muscle loss in various populations, but has not been investigated in hemodialysis (HD) patients. Thus, our objective was to evaluate whether creatine supplementation could attenuate the loss of lean body mass (LBM) and malnutrition‐inflammation score (MIS) in HD patients. Methods A randomized, placebo‐controlled, double blind, parallel‐design study included HD patients, of both sexes, aged 18–59 years. The patients were allocated to a Placebo Group (PG; n = 15; received maltodextrin, 1st week: 40 g/day and 2nd–4th weeks: 10 g/day) and a Creatine Group (CG; n = 15; received creatine plus maltodextrin, 1st week: 20 g/day of creatine plus 20 g/day of maltodextrin and 2nd–4th weeks: 5 g/day of creatine plus 5 g/day of maltodextrin). Pre and post the intervention, patients were evaluated for food intake, MIS, body composition and biochemical parameters. Results CG group attenuated the MIS (Pre: 5.57 ± 0.72 vs. Post: 3.85 ± 0.47 score, P = 0.003) compared with PG (Pre: 5.71 ± 0.97 vs. Post: 5.36 ± 0.95 score, P = 0.317) (supplement × time P = 0.017, effect size: 0.964). The change of LBM was greater in CG than in PG (CG: Δ0.95 vs PG: Δ0.13 kg). At post‐intervention, 28.6% of PG patients presented LBM loss and 71.4% remain stable. In contrast, 14.4% of CG patients had LBM loss, 42.8% remain stable and 42.8% gained. Food intake and quality of life did not change. CG increased the BMI and gait speed in post‐compared to pre‐moment, but no difference among the groups. Conclusion In HD patients, four weeks of creatine supplementation may alleviate the MIS as well as attenuate the LBM loss compared to placeboCapes, Brazi

A data mining approach for classifying DNA repair genes into ageing-related or non-ageing-related

<p>Abstract</p> <p>Background</p> <p>The ageing of the worldwide population means there is a growing need for research on the biology of ageing. DNA damage is likely a key contributor to the ageing process and elucidating the role of different DNA repair systems in ageing is of great interest. In this paper we propose a data mining approach, based on classification methods (decision trees and Naive Bayes), for analysing data about human DNA repair genes. The goal is to build classification models that allow us to discriminate between ageing-related and non-ageing-related DNA repair genes, in order to better understand their different properties.</p> <p>Results</p> <p>The main patterns discovered by the classification methods are as follows: (a) the number of protein-protein interactions was a predictor of DNA repair proteins being ageing-related; (b) the use of predictor attributes based on protein-protein interactions considerably increased predictive accuracy of attributes based on Gene Ontology (GO) annotations; (c) GO terms related to "response to stimulus" seem reasonably good predictors of ageing-relatedness for DNA repair genes; (d) interaction with the XRCC5 (Ku80) protein is a strong predictor of ageing-relatedness for DNA repair genes; and (e) DNA repair genes with a high expression in T lymphocytes are more likely to be ageing-related.</p> <p>Conclusions</p> <p>The above patterns are broadly integrated in an analysis discussing relations between Ku, the non-homologous end joining DNA repair pathway, ageing and lymphocyte development. These patterns and their analysis support non-homologous end joining double strand break repair as central to the ageing-relatedness of DNA repair genes. Our work also showcases the use of protein interaction partners to improve accuracy in data mining methods and our approach could be applied to other ageing-related pathways.</p

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

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

The yeast P5 type ATPase, Spf1, regulates manganese transport into the endoplasmic reticulum

The endoplasmic reticulum (ER) is a large, multifunctional and essential organelle. Despite intense research, the function of more than a third of ER proteins remains unknown even in the well-studied model organism Saccharomyces cerevisiae. One such protein is Spf1, which is a highly conserved, ER localized, putative P-type ATPase. Deletion of SPF1 causes a wide variety of phenotypes including severe ER stress suggesting that this protein is essential for the normal function of the ER. The closest homologue of Spf1 is the vacuolar P-type ATPase Ypk9 that influences Mn2+ homeostasis. However in vitro reconstitution assays with Spf1 have not yielded insight into its transport specificity. Here we took an in vivo approach to detect the direct and indirect effects of deleting SPF1. We found a specific reduction in the luminal concentration of Mn2+ in ∆spf1 cells and an increase following it’s overexpression. In agreement with the observed loss of luminal Mn2+ we could observe concurrent reduction in many Mn2+-related process in the ER lumen. Conversely, cytosolic Mn2+-dependent processes were increased. Together, these data support a role for Spf1p in Mn2+ transport in the cell. We also demonstrate that the human sequence homologue, ATP13A1, is a functionally conserved orthologue. Since ATP13A1 is highly expressed in developing neuronal tissues and in the brain, this should help in the study of Mn2+-dependent neurological disorders

Digital Mazes and Spatial Reasoning: Using Colour and Movement to Explore the 4th Dimension

This chapter focuses on innovative developments of four-dimensional digital mazes, examining how these mazes tap into the ideas of mathematician and fiction writer Charles Hinton (1853-1907) who wrote extensively on perception of a 4th geometric dimension. Hinton treats mathematical objects as physical and material movements, and draws on non-Euclidean geometry to argue for a virtual dimension to matter. I discuss recent attempts to build digital mazes that develop spatial sense in four dimensions, and show how these are directly linked to Hinton’s ideas. I focus on how colour and movement in digital environments are used to develop a distinctive kind of spatial sense. This chapter sheds light on innovative uses of digital software for developing student spatial sense. My aim is to explicate the new materialism of Charles Hinton, contribute to discussions about the nature of spatial sense and spatial reasoning, and to point to possible directions for future research on inventive approaches to geometry