Determining the sex-specific distributions of average daily alcohol consumption using cluster analysis: is there a separate distribution for people with alcohol dependence?
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01.12.2021 |
Jiang H.
Lange S.
Tran A.
Imtiaz S.
Rehm J.
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Population Health Metrics |
10.1186/s12963-021-00261-4 |
0 |
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Background: It remains unclear whether alcohol use disorders (AUDs) can be characterized by specific levels of average daily alcohol consumption. The aim of the current study was to model the distributions of average daily alcohol consumption among those who consume alcohol and those with alcohol dependence, the most severe AUD, using various clustering techniques. Methods: Data from Wave 1 and Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions were used in the current analyses. Clustering algorithms were applied in order to group a set of data points that represent the average daily amount of alcohol consumed. Gaussian Mixture Models (GMMs) were then used to estimate the likelihood of a data point belonging to one of the mixture distributions. Individuals were assigned to the clusters which had the highest posterior probabilities from the GMMs, and their treatment utilization rate was examined for each of the clusters. Results: Modeling alcohol consumption via clustering techniques was feasible. The clusters identified did not point to alcohol dependence as a separate cluster characterized by a higher level of alcohol consumption. Among both females and males with alcohol dependence, daily alcohol consumption was relatively low. Conclusions: Overall, we found little evidence for clusters of people with the same drinking distribution, which could be characterized as clinically relevant for people with alcohol use disorders as currently defined.
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In silico design, building and gas adsorption of nano-porous graphene scaffolds
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22.01.2021 |
Bellucci L.
Delfino F.
Tozzini V.
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Nanotechnology |
10.1088/1361-6528/abbe57 |
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© 2020 The Author(s). Published by IOP Publishing Ltd Printed in the UK Graphene-based nano-porous materials (GNM) are potentially useful for all those applications needing a large specific surface area (SSA), typical of the bidimensional graphene, yet realized in the bulk dimensionality. Such applications include for instance gas storage and sorting, catalysis and electrochemical energy storage. While a reasonable control of the structure is achieved in micro-porous materials by using nano-micro particles as templates, the controlled production or even characterization of GNMs with porosity strictly at the nano-scale still raises issues. These are usually produced using dispersion of nano-flakes as precursors resulting in little control on the final structure, which in turn reflects in problems in the structural model building for computer simulations. In this work, we describe a strategy to build models for these materials with predetermined structural properties (SSA, density, porosity), which exploits molecular dynamics simulations, Monte Carlo methods and machine learning algorithms. Our strategy is inspired by the real synthesis process: starting from randomly distributed flakes, we include defects, perforation, structure deformation and edge saturation on the fly, and, after structural refinement, we obtain realistic models, with given structural features. We find relationships between the structural characteristics and size distributions of the starting flake suspension and the final structure, which can give indications for more efficient synthesis routes. We subsequently give a full characterization of the models versus H2 adsorption, from which we extract quantitative relationship between the structural parameters and the gravimetric density. Our results quantitatively clarify the role of surfaces and edges relative amount in determining the H2 adsorption, and suggest strategies to overcome the inherent physical limitations of these materials as adsorbers. We implemented the model building and analysis procedures in software tools, freely available upon request.
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Risk assessments in nanotoxicology: bioinformatics and computational approaches
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01.02.2020 |
Pikula K.
Zakharenko A.
Chaika V.
Kirichenko K.
Tsatsakis A.
Golokhvast K.
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Current Opinion in Toxicology |
10.1016/j.cotox.2019.08.006 |
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© 2019 Elsevier B.V. A massive-scale production of engineered nanoparticles (ENPs) becomes one of the most important environmental issues. The mechanisms of ENPs' (eco)toxic action are not fully understood, and the estimation of those mechanisms is a complicated task because even slight changes in particle characteristics could dramatically change their toxicity. As a result of continuous manufacturing of ENPs with specific functionality and different physicochemical properties, conventional methods of in vivo and in vitro testing would not be able to fill the existing knowledge gap in nanotoxicology. The objectives of this review are to overlook the current achievements based on the new approaches of ENPs' risk assessment, such as bioinformatics approaches and machine learning tools. These methods confirmed their ability to reliable prediction and evaluation of ENPs' behavior and their toxic endpoints. Databases and projects based on these methods and approaches would be highly useful in addressing the problem of ENPs’ regulation.
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Quantitation of Molecular Pathway Activation Using RNA Sequencing Data
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01.01.2020 |
Borisov N.
Sorokin M.
Garazha A.
Buzdin A.
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Methods in Molecular Biology |
10.1007/978-1-0716-0138-9_15 |
1 |
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© Springer Science+Business Media, LLC, part of Springer Nature 2020. Intracellular molecular pathways (IMPs) control all major events in the living cell. IMPs are considered hotspots in biomedical sciences and thousands of IMPs have been discovered for humans and model organisms. Knowledge of IMPs activation is essential for understanding biological functions and differences between the biological objects at the molecular level. Here we describe the Oncobox system for accurate quantitative scoring activities of up to several thousand molecular pathways based on high throughput molecular data. Although initially designed for gene expression and mainly RNA sequencing data, Oncobox is now also applicable for quantitative proteomics, microRNA and transcription factor binding sites mapping data. The Oncobox system includes modules of gene expression data harmonization, aggregation and comparison and a recursive algorithm for automatic annotation of molecular pathways. The universal rationale of Oncobox enables scoring of signaling, metabolic, cytoskeleton, immunity, DNA repair, and other pathways in a multitude of biological objects. The Oncobox system can be helpful to all those working in the fields of genetics, biochemistry, interactomics, and big data analytics in molecular biomedicine.
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