Variational graph auto-encoders for miRNA-disease association prediction.

Cumulative experimental studies have demonstrated the critical roles of microRNAs (miRNAs) in the diverse fundamental and important biological processes, and in the development of numerous complex human diseases. Thus, exploring the relationships between miRNAs and diseases is helpful with understanding the mechanisms, the detection, diagnosis, and treatment of complex diseases. As the identification of miRNA-disease associations via traditional biological experiments is time-consuming and expensive, an effective computational prediction method is appealing.

Ensemble disease gene prediction by clinical sample-based networks.

Background: Disease gene prediction is a critical and challenging task. Many computational methods have been developed to predict disease genes, which can reduce the money and time used in the experimental validation. Since proteins (products of genes) usually work together to achieve a specific function, biomolecular networks, such as the protein-protein interaction (PPI) network and gene co-expression networks, are widely used to predict disease genes by analyzing the relationships between known disease genes and other genes in the networks.

Enhancing the prediction of disease-gene associations with multimodal deep learning.

Motivation: Computationally predicting disease genes helps scientists optimize the in-depth experimental validation and accelerates the identification of real disease-associated genes. Modern high-throughput technologies have generated a vast amount of omics data, and integrating them is expected to improve the accuracy of computational prediction. As an integrative model, multimodal deep belief net (DBN) can capture cross-modality features from heterogeneous datasets to model a complex system.

Two new phenolic glycosides from the seeds of .

Two new phenolic glycosides, named lanatusosides C () and D (), together with four known compounds (-), were isolated from the seeds of . Among them, compounds and were isolated from Cucurbitaceae for the first time, and compound was reported from this plant for the first time. Their structures were elucidated by means of extensive spectral analysis, including HR-ESI-MS, H and C NMR techniques.

Disease Gene Prediction by Integrating PPI Networks, Clinical RNA-Seq Data and OMIM Data.

Disease gene prediction is a challenging task that has a variety of applications such as early diagnosis and drug development. The existing machine learning methods suffer from the imbalanced sample issue because the number of known disease genes (positive samples) is much less than that of unknown genes which are typically considered to be negative samples. In addition, most methods have not utilized clinical data from patients with a specific disease to predict disease genes.

Circulating T lymphocyte subsets, cytokines, and immune checkpoint inhibitors in patients with bipolar II or major depression: a preliminary study.

This study aimed to investigate the less known activation pattern of T lymphocyte populations and immune checkpoint inhibitors on immunocytes in patients with bipolar II disorder depression (BD) or major depression (MD). A total of 23 patients with BD, 22 patients with MD, and 20 healthy controls (HCs) were recruited. The blood cell count of T lymphocyte subsets and the plasma level of cytokines (IL-2, IL-4, IL-6, IL-10, TNF-α, and IFN-γ) were selectively investigated.

Protective effects of seed melon extract on CCl-induced hepatic fibrosis in mice.

Ethnopharmacological Relevance: Citrullus lanatus ssp. vulgaris var. megalaspermus Lin et Chao, was also known as watermelon belongs to family Cucurbitaceae, variously used as healthy food and in the treatment of liver and lungs problems.

[Chemical constituents of ethyl acetate fraction from seed melon (Citrulluslanatus ssp. vulgaris var. megalaspermus)].

In this paper, the chemical composition of ethyl acetate parts of seed melon were studied by using ethanol re-flux method, extraction method, and isolated by column chromatography oversilica gel and Sephadex LH-20 and HPLC. The structures of the separated compounds were identified by physical-chemical methods and spectral data such as MS, ¹H-NMR, ¹³C-NMR, etc. 12 compounds were got from the plant including one new compound, 4-hydroxymet-hyl-2-methoxyphenyl 1-O-β-D-[6'-O-(4″-hydroxybenzoyl)-glucopyranoside] (1) and 11 known compounds, uracil (2), thymine (3), 2'-deoxyuridine (4), 7,8-dimethylalloxazine (5), indole-3-carboxylic acid (6), β-adenosine (7), 4-hydroxybenzoic acid (8), p-coumaric acid (9), cucumegastigmanesⅠ (10), 3'-methoxyl-quercetin-7-O-β-D-glucopyranoside (11) and 3,3'-dimethyloxy-4,4'-dihydroxy-9,9'-monoepoxy lignan (12).

State observer design for delayed genetic regulatory networks.

Genetic regulatory networks are dynamic systems which describe the interactions among gene products (mRNAs and proteins). The internal states of a genetic regulatory network consist of the concentrations of mRNA and proteins involved in it, which are very helpful in understanding its dynamic behaviors. However, because of some limitations such as experiment techniques, not all internal states of genetic regulatory network can be effectively measured.

Influence of adriamycin on changes in Nanog, Oct-4, Sox2, ARID1 and Wnt5b expression in liver cancer stem cells.

Aim: To determine the influence of Adriamycin (ADM) on the changes in Nanog, Oct4, Sox2, as well as, in ARID1 and Wnt5b expression in liver cancer stem cells.

Methods: The MHCC97-L and HCCLM3 liver cancer cell lines were selected as the cell models in this study, and were routinely cultured. The 50% lethal dose (LD50) in the cell lines was detected by the MTT assay.

[Prospective study on comparing two methods: for dynamic monitoring of minimal residual disease in acute promyelocytic leukemia].

This study was purposed to comparatively analyse the value of PCR and FCM for dynamic monitoring minimal residual disease (MRD) of acute promyelocytic leukemia. The patients with acute promyelocytic leukemia hospitalized in our hospital from January 2011 to December 2012 were observed and all achieved complete remission after remission induction therapy. Before the chemotherapy, the bone marrow cell morphology examination, polymerase-chain reaction (PCR) and multi-parameter flow cytometry (FCM) were performed for each patient.

Nonlinear-model-based analysis methods for time-course gene expression data.

Microarray technology has produced a huge body of time-course gene expression data and will continue to produce more. Such gene expression data has been proved useful in genomic disease diagnosis and drug design. The challenge is how to uncover useful information from such data by proper analysis methods such as significance analysis and clustering analysis.

Complexity analysis and parameter estimation of dynamic metabolic systems.

A metabolic system consists of a number of reactions transforming molecules of one kind into another to provide the energy that living cells need. Based on the biochemical reaction principles, dynamic metabolic systems can be modeled by a group of coupled differential equations which consists of parameters, states (concentration of molecules involved), and reaction rates. Reaction rates are typically either polynomials or rational functions in states and constant parameters.

M-matrix-based stability conditions for genetic regulatory networks with time-varying delays and noise perturbations.

Stability is essential for designing and controlling any dynamic systems. Recently, the stability of genetic regulatory networks has been widely studied by employing linear matrix inequality (LMI) approach, which results in checking the existence of feasible solutions to high-dimensional LMIs. In the previous study, the authors present several stability conditions for genetic regulatory networks with time-varying delays, based on M-matrix theory and using the non-smooth Lyapunov function, which results in determining whether a low-dimensional matrix is a non-singular M-matrix.

Robust and global delay-dependent stability for genetic regulatory networks with parameter uncertainties.

The study of stability is essential for designing or controlling genetic regulatory networks, which can be described by nonlinear differential equations with time delays. Much attention has been paid to the study of delay-independent stability of genetic regulatory networks and as a result, many sufficient conditions have been derived for delay-independent stability. Although it might be more interesting in practice, delay-dependent stability of genetic regulatory networks has been studied insufficiently.

Estimating parameters in genetic regulatory networks with SUM logic.

Many methods for inferring genetic regulatory networks have been proposed. However inferred networks can hardly be used to analyze the dynamics of genetic regulatory networks. Recently nonlinear differential equations are proposed to model genetic regulatory networks.

Stability and bifurcation analysis of models for zebrafish somitogenesis.

Notch-Delta signaling is indispensable for somitogenesis, which controls the vertebrate segmentation during embryonic development. Several theoretical models have been proposed to explain this interesting process. In zebrafish somitogenesis, genes her1, her7, delta, and their proteins plays the important roles.

Nonlinear model-based method for clustering periodically expressed genes.

Clustering periodically expressed genes from their time-course expression data could help understand the molecular mechanism of those biological processes. In this paper, we propose a nonlinear model-based clustering method for periodically expressed gene profiles. As periodically expressed genes are associated with periodic biological processes, the proposed method naturally assumes that a periodically expressed gene dataset is generated by a number of periodical processes.

Parameter estimation method for improper fractional models and its application to molecular biological systems.

Derived from biochemical principles, molecular biological systems can be described by a group of differential equations. Generally these differential equations contain fractional functions plus polynomials (which we call improper fractional model) as reaction rates. As a result, molecular biological systems are nonlinear in both parameters and states.

[Dynamic QTL and correlated characters of tomato soluble solid content.].

Two hundred and six F2:3 families from the cross between TD22 and HT-1-1-1-1 were used for dynamic QTL research of tomato soluble solid content and correlative traits, and correlation analysis of soluble solid content (SSC) with fruit weight (FW), fruit shape index (FSI), soluble sugar, vitamin C (VC), and organic acid at three different development stages. The results showed that there were differences in QTL loci for soluble solid content during the three stages of tomato fruit development. Four and eight QTLs were detected in green ripe stage and red ripe stage, respectively.

[Clinical analysis of 14 patients with lymphoma associated hemophagocytic syndrome].

The study was aimed to investigate the clinical characteristics, diagnosis and therapy of patients with lymphoma associated hemophagocytic syndrome (LAHS) so as to provide the clinical basis for improving its recognition and giving effective therapy. The clinical data of 14 patients with LAHS in Beijing Friendship Hospital, Capital Medical University during the period from June 2005 to May 2008 were collected, the informations including primary diseases, clinical manifestations, laboratory findings, therapy and outcome were analyzed retrospectively, the coincidence of each diagnostic index was compared before and after diagnosis. All 14 patients were given therapeutic regimens containing fludarabine, methylprednisolone and gammaglobulin (FDIg) after final diagnosis.

[Significance of hemophagocytosis in diagnosis of hemophagocytic lymphohistiocytosis].

The study was aimed to explore the diagnostic significance of hemophagocytosis in the patients with hemophagocytic lymphohistiocytosis (HLH). 61 suspected HLH patients from June 2005 to October 2008 were enrolled in the study. The suspected HLH patients were divided into confirmed group (43 out of 61) and excluded group (18 out of 61) according to HLH-2004 diagnostic criteria.

[The early diagnosis and clinical analysis of 57 cases of acquired hemophagocytic lymphohistiocytosis].

Objective: To explore the significance of NK cell activity, interleukin-2 receptors (sCD(25)) and glycosylated ferritin in the early diagnostic of acquired hemophagocytic lymphohistiocytosis (HLH).

Methods: 57 patients suspected of HLH from June 2005 to May 2008 and 25 healthy subjects were enrolled in the study. The patients suspected of HLH were divided into three groups i.

[Diagnostic significance of NK cell activity in the patients with hemophagocytic syndrome].

This study was aimed to explore the level of NK cell activity in the patients with secondary hemophagocytic syndrome (HPS) and its significance for early diagnosis of this disease. 16 suspected HPS patients and 25 healthy subjects were enrolled in this study. The activity of NK cells in peripheral blood was detected by a released LDH assay.