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      Identifying Critical State of Complex Diseases by Single-Sample-Based Hidden Markov Model

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          Abstract

          The progression of complex diseases is generally divided as a normal state, a pre-disease state or tipping point, and a disease state. Developing individual-specific method that can identify the pre-disease state just before a catastrophic deterioration, is critical for patients with complex diseases. However, with only a case sample, it is challenging to detect a pre-disease state which has little significant differences comparing with a normal state in terms of phenotypes and gene expressions. In this study, by regarding the tipping point as the end point of a stationary Markov process, we proposed a single-sample-based hidden Markov model (HMM) approach to explore the dynamical differences between a normal and a pre-disease states, and thus can signal the upcoming critical transition immediately after a pre-disease state. Using this method, we identified the pre-disease state or tipping point in a numerical simulation and two real datasets including stomach adenocarcinoma and influenza infection, which demonstrate the effectiveness of the method.

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          Most cited references11

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          Self-organized patchiness in asthma as a prelude to catastrophic shifts.

          Asthma is a common disease affecting an increasing number of children throughout the world. In asthma, pulmonary airways narrow in response to contraction of surrounding smooth muscle. The precise nature of functional changes during an acute asthma attack is unclear. The tree structure of the pulmonary airways has been linked to complex behaviour in sudden airway narrowing and avalanche-like reopening. Here we present experimental evidence that bronchoconstriction leads to patchiness in lung ventilation, as well as a computational model that provides interpretation of the experimental data. Using positron emission tomography, we observe that bronchoconstricted asthmatics develop regions of poorly ventilated lung. Using the computational model we show that, even for uniform smooth muscle activation of a symmetric bronchial tree, the presence of minimal heterogeneity breaks the symmetry and leads to large clusters of poorly ventilated lung units. These clusters are generated by interaction of short- and long-range feedback mechanisms, which lead to catastrophic shifts similar to those linked to self-organized patchiness in nature. This work might have implications for the treatment of asthma, and might provide a model for studying diseases of other distributed organs.
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            Heterogeneous nuclear ribonucleoprotein C1/C2 controls the metastatic potential of glioblastoma by regulating PDCD4.

            MicroRNAs (miRNAs) have been implicated in the pathogenesis and progression of brain tumors. miR-21 is one of the most highly overexpressed miRNAs in glioblastoma multiforme (GBM), and its level of expression correlates with the tumor grade. Programmed cell death 4 (PDCD4) is a well-known miR-21 target and is frequently downregulated in glioblastomas in accordance with increased miR-21 expression. Downregulation of miR-21 or overexpression of PDCD4 can inhibit metastasis. Here, we investigate the role of heterogeneous nuclear ribonucleoprotein C1/C2 (hnRNPC) in the metastatic potential of the glioblastoma cell line T98G. hnRNPC bound directly to primary miR-21 (pri-miR-21) and promoted miR-21 expression in T98G cells. Silencing of hnRNPC lowered miR-21 levels, in turn increasing the expression of PDCD4, suppressing Akt and p70S6K activation, and inhibiting migratory and invasive activities. Silencing of hnRNPC reduced cell proliferation and enhanced etoposide-induced apoptosis. In support of a role for hnRNPC in the invasiveness of GBM, highly aggressive U87MG cells showed higher hnRNPC expression levels and hnRNPC abundance in tissue arrays and also showed elevated levels as a function of brain tumor grade. Taken together, our data indicate that hnRNPC controls the aggressiveness of GBM cells through the regulation of PDCD4, underscoring the potential usefulness of hnRNPC as a prognostic and therapeutic marker of GBM.
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              Prediction of epileptic seizures: are nonlinear methods relevant?

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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                04 April 2019
                2019
                : 10
                : 285
                Affiliations
                [1] 1School of Mathematics, South China University of Technology , Guangzhou, China
                [2] 2Shanghai Jiao Tong University Affiliated Sixth People's Hospital , Shanghai, China
                [3] 3School of Computer Science and Engineering, South China University of Technology , Guangzhou, China
                Author notes

                Edited by: Tao Huang, Shanghai Institutes for Biological Sciences (CAS), China

                Reviewed by: Huanfei Ma, Soochow University, China; Ling-Yun Wu, Academy of Mathematics and Systems Science (CAS), China

                *Correspondence: Pei Chen chenpei@ 123456scut.edu.cn

                This article was submitted to Bioinformatics and Computational Biology, a section of the journal Frontiers in Genetics

                Article
                10.3389/fgene.2019.00285
                6458292
                31019526
                8e998171-79a8-4dbc-a0d6-7a782f19cb9f
                Copyright © 2019 Liu, Zhong, Yu, Li and Chen.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 19 December 2018
                : 15 March 2019
                Page count
                Figures: 6, Tables: 0, Equations: 9, References: 31, Pages: 10, Words: 5740
                Funding
                Funded by: National Natural Science Foundation of China 10.13039/501100001809
                Award ID: 11771152
                Award ID: 91530320
                Award ID: 61803360
                Award ID: 11871456
                Award ID: 61872150
                Categories
                Genetics
                Original Research

                Genetics
                hidden markov process,single-sample-based diagnosis,dynamical network biomarker (dnb),pre-disease state,critical transition,early-warning signal

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