Metabolomics is a powerful new technology that allows for the assessment of global metabolic profiles in easily accessible biofluids and biomarker discovery in order to distinguish between diseased and nondiseased status information. Deciphering the molecular networks that distinguish diseases may lead to the identification of critical biomarkers for disease aggressiveness. However, current diagnostic methods cannot predict typical Jaundice syndrome (JS) in patients with liver disease and little is known about the global metabolomic alterations that characterize JS progression. Emerging metabolomics provides a powerful platform for discovering novel biomarkers and biochemical pathways to improve diagnostic, prognostication, and therapy. Therefore, the aim of this study is to find the potential biomarkers from JS disease by using a nontarget metabolomics method, and test their usefulness in human JS diagnosis. Multivariate data analysis methods were utilized to identify the potential biomarkers. Interestingly, 44 marker metabolites contributing to the complete separation of JS from matched healthy controls were identified. Metabolic pathways (Impact-value>0.10) including alanine, aspartate, and glutamate metabolism and synthesis and degradation of ketone bodies were found to be disturbed in JS patients. This study demonstrates the possibilities of metabolomics as a diagnostic tool in diseases and provides new insight into pathophysiologic mechanisms. Metabolomics, an omic science in systems biology, is the comprehensive profiling of metabolic changes occurring in living systems (1). It attempts to capture global changes and overall physiological status in biochemical networks and pathways in order to elucidate sites of perturbations, and has shown great promise as a means to identify biomarkers of diseases (2, 3). One area of considerable interest in the field of metabolomics is the detection of potential biomarkers associated with diseases, and the metabolic profiling could provide global changes of endogenous metabolites of patients. Metabolomics is the study of metabolic pathways and the measurement of unique biochemical molecules generated in a living system. It could facilitate biomarker discovery by distinguishing between diseased and nondiseased patients. Biomarker metabolites can also be therapeutic targets (4). Detecting changes in metabolite concentrations reveals the range of biochemical effects induced by a disease condition.

In this paper, we investigate the effect of supersymmetry on the symmetry classification of random matrix theory ensembles. We mainly consider the random matrix behaviors in the N = 1 supersymmetric generalization of Sachdev-Ye-Kitaev (SYK) model, a toy model for two-dimensional quantum black hole with supersymmetric constraint. Some analytical arguments and numerical results are given to show that the statistics of the supersymmetric SYK model could be interpreted as random matrix theory ensembles, with a different eight-fold classification from the original SYK model and some new features. The time-dependent evolution of the spectral form factor is also investigated, where predictions from random matrix theory are governing the late time behavior of the chaotic hamiltonian with supersymmetry.

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LLLI stimulates proliferation, increases growth factors secretion and facilitates myogenic differentiation of BMSCs. Therefore, LLLI may provide a novel approach for the preconditioning of BMSCs in vitro prior to transplantation.

BackgroundRecent studies indicated that some glycolytic enzymes are complicated, multifaceted proteins rather than simple components of the glycolytic pathway. FBP1 plays a vital role in glucose metabolism, but its role in gastric cancer tumorigenesis and metastasis has not been fully understood.MethodsThe prognostic value of FBP1 was first studied in The Cancer Genome Atlas (TCGA) database and validated in in-house database. The effect of FBP1 on cell proliferation and metastasis was examined in vitro. Nonparametric test and Log-rank test were used to evaluate the clinical significance of FBP1 expression.ResultsIn the TCGA cohort, FBP1 mRNA level were shown to be predictive of overall survival in gastric cancer (P = 0.029). In the validation cohort, FBP1 expression were inversely correlated with advanced N stage (P = 0.021) and lymphovascular invasion (P = 0.011). Multivariate Cox regression analysis demonstrated that FBP1 was an independent predictor for both overall survival (P = 0.004) and disease free survival (P<0.001). Functional studies demonstrated that ectopic FBP1 expression inhibited proliferation and invasion in gastric cancer cells, while silencing FBP1 expression had opposite effects (P<0.05). Mechanically, FBP1 serves as a tumor suppressor by inhibiting epithelial-mesenchymal transition (EMT).ConclusionsDownregulation of FBP1 promotes gastric cancer metastasis by facilitating EMT and acts as a potential prognostic factor and therapeutic target in gastric cancer.

Abstract:We study the implications of modular invariance on 2d CFT partition functions with abelian or non-abelian currents when chemical potentials for the charges are turned on, i.e. when the partition functions are "flavored". We begin with a new proof of the transformation law for the modular transformation of such partition functions. Then we proceed to apply modular bootstrap techniques to constrain the spectrum of charged states in the theory. We improve previous upper bounds on the state with the greatest "mass-tocharge" ratio in such theories, as well as upper bounds on the weight of the lightest charged state and the charge of the weakest charged state in the theory. We apply the extremal functional method to theories that saturate such bounds, and in several cases we find the resulting prediction for the occupation numbers are precisely integers. Because such theories sometimes do not saturate a bound on the full space of states but do saturate a bound in the neutral sector of states, we find that adding flavor allows the extremal functional method to solve for some partition functions that would not be accessible to it otherwise.

In interdependent networks, it is usually assumed, based on percolation theory, that nodes become nonfunctional if they lose connection to the network giant component. However, in reality, some nodes, equipped with alternative resources, together with their connected neighbors can still be functioning after disconnected from the giant component. Here, we propose and study a generalized percolation model that introduces a fraction of reinforced nodes in the interdependent networks that can function and support their neighborhood. We analyze, both analytically and via simulations, the order parameter-the functioning component-comprising both the giant component and smaller components that include at least one reinforced node. Remarkably, it is found that, for interdependent networks, we need to reinforce only a small fraction of nodes to prevent abrupt catastrophic collapses. Moreover, we find that the universal upper bound of this fraction is 0.1756 for two interdependent Erdős-Rényi (ER) networks: regular random (RR) networks and scale-free (SF) networks with large average degrees. We also generalize our theory to interdependent networks of networks (NONs). These findings might yield insight for designing resilient interdependent infrastructure networks.

Background There is growing evidence that tripartite motif-containing protein 44 (TRIM44) plays crucial role in tumor development. However, the underlying mechanism of this deubiquitinating enzyme remains unclear. Methods Large clinical samples were used to detect TRIM44 expression and its associations with clinicopathological features and prognosis. Gain- and loss-of-function experiments in cell lines and mouse xenograft models were performed to elucidate the function and underlying mechanisms of TRIM44 induced tumor progression. Co-immunoprecipitation (Co-IP) assays and mass spectrometric analyses were applied to verify the interacting proteins of TRIM44. Results We found that TRIM44 was commonly amplified in melanoma tissues compared with paratumoral tissues. TRIM44 expression also positively correlated with more aggressive clinicopathological features, such as Breslow depth ( p = 0.025), distant metastasis ( p = 0.012), and TNM stage ( p = 0.002). Importantly, we found that TRIM44 was an independent indicator of prognosis for melanoma patients. Functionally, overexpression of TRIM44 facilitated cell invasion, migration, apoptosis resistance and proliferation in vitro, and promoted lung metastasis and tumorigenic ability in vivo. Importantly, high level of TRIM44 induced melanoma cell epithelial-mesenchymal transition (EMT), which is one of the most important mechanisms for the promotion of tumor metastasis. Mechanistically, high levels of TRIM44 increased the levels of p-AKT (T308) and p-mTOR (S2448), and a specific AKT inhibitor inhibited TRIM44-induced tumor progression. Co-IP assays and mass spectrometric analyses indicated that TRIM44 overexpression induces cell EMT through activating AKT/mTOR pathway via directly binding and stabilizing TOLL-like receptor 4 (TLR4), and TLR4 interference impeded TRIM44 induced tumor progression. Moreover, we demonstrated that TRIM44 is the target of miR-26b-5p, which is significantly downregulated in melanoma tissues and may be responsible for the overexpression of TRIM44. Conclusions TRIM44, regulated by miR-26b-5p, promotes melanoma progression by stabilizing TLR4, which then activates the AKT/mTOR pathway. TRIM44 shows promise as a prognostic predictor and a therapeutic target for melanoma patients. Electronic supplementary material The online version of this article (10.1186/s13046-019-1138-7) contains supplementary material, which is available to authorized users.