76 research outputs found

    Identifying and analyzing novel epilepsy-related genes using random walk with restart algorithm.

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    As a pathological condition, epilepsy is caused by abnormal neuronal discharge in brain which will temporarily disrupt the cerebral functions. Epilepsy is a chronic disease which occurs in all ages and would seriously affect patients' personal lives. Thus, it is highly required to develop effective medicines or instruments to treat the disease. Identifying epilepsy-related genes is essential in order to understand and treat the disease because the corresponding proteins encoded by the epilepsy-related genes are candidates of the potential drug targets. In this study, a pioneering computational workflow was proposed to predict novel epilepsy-related genes using the random walk with restart (RWR) algorithm. As reported in the literature RWR algorithm often produces a number of false positive genes, and in this study a permutation test and functional association tests were implemented to filter the genes identified by RWR algorithm, which greatly reduce the number of suspected genes and result in only thirty-three novel epilepsy genes. Finally, these novel genes were analyzed based upon some recently published literatures. Our findings implicate that all novel genes were closely related to epilepsy. It is believed that the proposed workflow can also be applied to identify genes related to other diseases and deepen our understanding of the mechanisms of these diseases

    A new combined endpoint using U-statistic in analysis in clinical trials

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    Common endpoints can be divided into two categories. One is dichotomous endpoints which take only fixed values (most of the time two values). The other is continuous endpoints which can be any real number between two specified values. Choices of primary endpoints are critical in clinical trials. If we only use dichotomous endpoints, the power could be underestimated. If only continuous endpoints are chosen, we may not obtain expected sample size due to occurrence of some significant clinical events. Combined endpoints are used in clinical trials to give additional power. However, current combined endpoints or composite endpoints in cardiovascular disease clinical trials or most clinical trials are endpoints that combine either dichotomous endpoints (total mortality + total hospitalization), or continuous endpoints (risk score). Our present work applied U-statistic to combine one dichotomous endpoint and one continuous endpoint, which has three different assessments and to calculate the sample size and test the hypothesis to see if there is any treatment effect. It is especially useful when some patients cannot provide the most precise measurement due to medical contraindication or some personal reasons. Results show that this method has greater power then the analysis using continuous endpoints alone

    Deep Learning Strategies in Media Teaching System Based on ADABoost Algorithm

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    Role of Melatonin in Bovine Reproductive Biotechnology

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    Melatonin has profound antioxidant activity and numerous functions in humans as well as in livestock and poultry. Additionally, melatonin plays an important role in regulating the biological rhythms of animals. Combining melatonin with scientific breeding management has considerable potential for optimizing animal physiological functions, but this idea still faces significant challenges. In this review, we summarized the beneficial effects of melatonin supplementation on physiology and reproductive processes in cattle, including granulosa cells, oocytes, circadian rhythm, stress, inflammation, testicular function, spermatogenesis, and semen cryopreservation. There is much emerging evidence that melatonin can profoundly affect cattle. In the future, we hope that melatonin can not only be applied to cattle, but can also be used to safely and effectively improve the efficiency of animal husbandry

    Identification of genes related to immune enhancement caused by heterologous ChAdOx1–BNT162b2 vaccines in lymphocytes at single-cell resolution with machine learning methods

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    The widely used ChAdOx1 nCoV-19 (ChAd) vector and BNT162b2 (BNT) mRNA vaccines have been shown to induce robust immune responses. Recent studies demonstrated that the immune responses of people who received one dose of ChAdOx1 and one dose of BNT were better than those of people who received vaccines with two homologous ChAdOx1 or two BNT doses. However, how heterologous vaccines function has not been extensively investigated. In this study, single-cell RNA sequencing data from three classes of samples: volunteers vaccinated with heterologous ChAdOx1–BNT and volunteers vaccinated with homologous ChAd–ChAd and BNT–BNT vaccinations after 7 days were divided into three types of immune cells (3654 B, 8212 CD4+ T, and 5608 CD8+ T cells). To identify differences in gene expression in various cell types induced by vaccines administered through different vaccination strategies, multiple advanced feature selection methods (max-relevance and min-redundancy, Monte Carlo feature selection, least absolute shrinkage and selection operator, light gradient boosting machine, and permutation feature importance) and classification algorithms (decision tree and random forest) were integrated into a computational framework. Feature selection methods were in charge of analyzing the importance of gene features, yielding multiple gene lists. These lists were fed into incremental feature selection, incorporating decision tree and random forest, to extract essential genes, classification rules and build efficient classifiers. Highly ranked genes include PLCG2, whose differential expression is important to the B cell immune pathway and is positively correlated with immune cells, such as CD8+ T cells, and B2M, which is associated with thymic T cell differentiation. This study gave an important contribution to the mechanistic explanation of results showing the stronger immune response of a heterologous ChAdOx1–BNT vaccination schedule than two doses of either BNT or ChAdOx1, offering a theoretical foundation for vaccine modification.</jats:p

    Structure Characterization and Treatment Effect of Zingiber officinale Polysaccharide on Dextran Sulfate Sodium-Induced Ulcerative Colitis

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    Background: Ulcerative colitis (UC) is on the rise all over the world. Zingiber officinale polysaccharide (ZOP-1) has good anti-inflammatory and antioxidant effects, but the therapeutic effect and mechanism of ZOP-1 on UC are still unclear. Methods: ZOP-1 obtained by water extraction and alcohol precipitation was analyzed by methylation and NMR. At the same time, the mechanism of ZOP-1 in the treatment of UC was clarified by hematoxylin-eosin (HE) staining, metagenomics, immunohistochemistry, and protein blot (Wb). Results: ZOP-1 was the structure of the by &rarr;4,6)-&beta;-Glcp-1&rarr; and &rarr;3,6)-&alpha;-Galp-(1&rarr; constitute the main chain, there were two branched chain by &rarr;4)-&beta;-Glcp(1&rarr;, and &alpha;-Araf(1&rarr; as the end group. ZOP-1 significantly improved the shortening and thickening of the colon, changed the index of immune organs, inhibited the production of inflammatory factors in mice with ulcerative colitis, changed the intestinal flora of mice, increased the content of short-chain fatty acids (SCFAs) in the intestine, and controlled the TLR4/NF-&kappa;B/MAPK signaling pathway, thus preventing and treating DSS-induced ulcerative colitis in mice. Conclusions: ZOP-1 alleviated UC by controlling the expression of cytokines, thereby reducing intestinal inflammation and oxidative stress, enhancing intestinal integrity, modulating intestinal flora, and regulating the levels of SCFAs
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