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Garlic, an Allium vegetable, contains rich flavonoids organosulfur compounds (OSCs) that have potent anticancer properties. The aim of the review is to provide an overview of the different types of garlic, their active compounds, and the potential anticancer benefits with a focus on antioxidant activity. Animal and cell line studies have provided convincing evidence that garlic and its organosulfur compounds inhibit carcinogenesis through a number of events including induction of apoptosis, inhibiting cellular proliferation, scavenging radical oxygen species (ROS), increasing the activities of enzymes such as glutathione S-transferase, and reducing tumor size. Epidemiological studies showed compelling evidence that garlic consumption is associated with decreased risk of colorectal cancer, but inconsistent evidence for stomach, breast, and prostate cancers. Studies also suggest that the presence and potency of garlic OSCs varies with respect to the preparation and form of garlic. Further epidemiological studies with information on garlic form consumed or preparation methods and molecular studies regarding its antioxidant mechanisms, such as increasing enzymatic and nonenzymatic antioxidants levels, are warranted.

Background Mathematical models of biological networks can provide important predictions and insights into complex disease. Constraint-based models of cellular metabolism and probabilistic models of gene regulatory networks are two distinct areas that have progressed rapidly in parallel over the past decade. In principle, gene regulatory networks and metabolic networks underly the same complex phenotypes and diseases. However, systematic integration of these two model systems remains a fundamental challenge. Results In this work, we address this challenge by fusing probabilistic models of gene regulatory networks into constraint-based models of metabolism. The novel approach utilizes probabilistic reasoning in BN models of regulatory networks serves as the “glue” that enables a natural interface between the two systems. Probabilistic reasoning is used to predict and quantify system-wide effects of perturbation to the regulatory network in the form of constraints for flux variability analysis. In this setting, both regulatory and metabolic networks inherently account for uncertainty. Applications leverage constraint-based metabolic models of brain metabolism and gene regulatory networks parameterized by gene expression data from the hippocampus to investigate the role of the HIF-1 pathway in Alzheimer’s disease. Integrated models support HIF-1A as effective target to reduce the effects of hypoxia in Alzheimer’s disease. However, HIF-1A activation is far less effective in shifting metabolism when compared to brain metabolism in healthy controls. Conclusions The direct integration of probabilistic regulatory networks into constraint-based models of metabolism provides novel insights into how perturbations in the regulatory network may influence metabolic states. Predictive modeling of enzymatic activity can be facilitated using probabilistic reasoning, thereby extending the predictive capacity of the network. This framework for model integration is generalizable to other systems. Electronic supplementary material The online version of this article (10.1186/s12859-019-2872-8) contains supplementary material, which is available to authorized users.

The intracardiac synthesis of anthracycline alcohol metabolites (e.g., daunorubicinol) contributes to the pathogenesis of anthracycline-related cardiotoxicity. Cancer patients with Down syndrome (DS) are at increased risk for anthracycline-related cardiotoxicity. We profiled the expression of anthracycline metabolizing enzymes in hearts from donors with- and without- DS.Cardiac expression of CBR1, CBR3, AKR1A1, AKR1C3 and AKR7A2 was examined by quantitative real time PCR, quantitative immunoblotting, and enzyme activity assays using daunorubicin. The CBR1 polymorphism rs9024 was investigated by allelic discrimination with fluorescent probes. The contribution of CBRs/AKRs proteins to daunorubicin reductase activity was examined by multiple linear regression.CBR1 was the most abundant transcript (average relative expression; DS: 81%, non-DS: 58%), and AKR7A2 was the most abundant protein (average relative expression; DS: 38%, non-DS: 35%). Positive associations between cardiac CBR1 protein levels and daunorubicin reductase activity were found for samples from donors with- and without- DS. Regression analysis suggests that sex, CBR1, AKR1A1, and AKR7A2 protein levels were significant contributors to cardiac daunorubicin reductase activity. CBR1 rs9024 genotype status impacts on cardiac CBR1 expression in non-DS hearts.CBR1, AKR1A1, and AKR7A2 protein levels point to be important determinants for predicting the synthesis of cardiotoxic daunorubicinol in heart.

The advent and availability of technology has brought us closer than ever through social networks. Consequently, there is a growing emphasis on mining social networks to extract information for knowledge and discovery. However, methods for social network analysis (SNA) have not kept pace with the data explosion. In this review, we describe directed and undirected probabilistic graphical models (PGMs), and highlight recent applications to social networks. PGMs represent a flexible class of models that can be adapted to address many of the current challenges in SNA. In this work, we motivate their use with simple and accessible examples to demonstrate the modeling and connect to theory. In addition, recent applications in modern SNA are highlighted, including the estimation and quantification of importance, propagation of influence, trust (and distrust), link and profile prediction, privacy protection, and news spread through microblogging. Applications are selected to demonstrate the flexibility and predictive capabilities of PGMs in SNA. Finally, we conclude with a discussion of challenges and opportunities for PGMs in social networks.

Networks exhibit different behaviors, which govern patterns of connectivity. Predicting new links is a fundamental and challenging problem. This work examines the link prediction problem in different scale-free networks and in a Facebook community. Feature-based methods, which can be used on large-scale networks, are used to predict novel links in the graphs. Results show that predictive accuracy is largely dependent on the underlying dynamic process and connectivity pattern. A simulation engine and testing paradigm are described as resources for testing novel link prediction methods in a natural dynamic context, and guide for the selection of appropriate technique for applications.