34 research outputs found

    Subclinical Hypothyroidism and Type 2 Diabetes: A Systematic Review and Meta-Analysis

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    <div><p>Background</p><p>Abundant evidence suggests an association between subclinical hypothyroidism (SCH) and type 2 diabetes mellitus (T2DM), but small sample sizes and inconclusive data in the literature complicate this assertion.</p><p>Objective</p><p>We measured the prevalence of SCH in T2DM population, and investigated whether T2DM increase the risk of SCH and whether SCH was associated with diabetic complications.</p><p>METHODS</p><p>We conducted a meta-analysis using PubMed, EMBASE, Web of Science, Wan Fang, CNKI and VIP databases for literature search. We obtained studies published between January 1, 1980 to December 1, 2014. The studies were selected to evaluate the prevalence of SCH in T2DM subjects, compare the prevalence of SCH in T2DM subjects with those non-diabetics, and investigate whether diabetic complications were more prevalent in SCH than those who were euthyroid. Fixed and random effects meta-analysis models were used, and the outcome was presented as a pooled prevalence with 95% confidence interval (95% CI) or a summary odds ratio (OR) with 95% CI.</p><p>RESULTS</p><p>Through literature search, 36 articles met the inclusion criteria and these articles contained a total of 61 studies. Funnel plots and Egger’s tests showed no publication bias in our studies, except for the pooled prevalence of SCH in T2DM (<i>P</i> = 0.08) and OR for SCH in T2DM (<i>P</i> = 0.04). Trim and fill method was used to correct the results and five potential missing data were replaced respectively. The adjusted pooled prevalence of SCH in T2DM patients was 10.2%, meanwhile, T2DM was associated with a 1.93-fold increase in risk of SCH (95% CI: 1.66, 2.24). Furthermore, SCH might affect the development of diabetic complications with an overall OR of 1.74 (95% CI: 1.34, 2.28) for diabetic nephropathy, 1.42 (95% CI: 1.21, 1.67) for diabetic retinopathy, 1.85 (95% CI: 1.35, 2.54) for peripheral arterial disease, and 1.87 (95% CI: 1.06, 3.28) for diabetic peripheral neuropathy.</p><p>Conclusions</p><p>T2DM patients are more likely to have SCH when compared with healthy population and SCH may be associated with increased diabetic complications. It is necessary to screen thyroid function in patients with T2DM, and appropriate individualized treatments in addition to thyroid function test should be given to T2DM patients with SCH as well.</p></div

    Additional file 1 of Echinococcus granulosus ubiquitin-conjugating enzymes (E2D2 and E2N) promote the formation of liver fibrosis in TGFβ1-induced LX-2 cells

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    Additional file 1: Figure S1. The HE staining of samples. (A): Sterile cyst, fertile cyst, PSC, and 25-day strobilated worm (HE × 200); (B): Fertile cyst and surrounding liver tissue (HE × 400); (C): Healthy liver tissue (HE × 200). Abbreviations: G, germinal layer; H, hooks; S, suckers; Sc, scolex

    A New Supervised Over-Sampling Algorithm with Application to Protein-Nucleotide Binding Residue Prediction

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    <div><p>Protein-nucleotide interactions are ubiquitous in a wide variety of biological processes. Accurately identifying interaction residues solely from protein sequences is useful for both protein function annotation and drug design, especially in the post-genomic era, as large volumes of protein data have not been functionally annotated. Protein-nucleotide binding residue prediction is a typical imbalanced learning problem, where binding residues are extremely fewer in number than non-binding residues. Alleviating the severity of class imbalance has been demonstrated to be a promising means of improving the prediction performance of a machine-learning-based predictor for class imbalance problems. However, little attention has been paid to the negative impact of class imbalance on protein-nucleotide binding residue prediction. In this study, we propose a new supervised over-sampling algorithm that synthesizes additional minority class samples to address class imbalance. The experimental results from protein-nucleotide interaction datasets demonstrate that the proposed supervised over-sampling algorithm can relieve the severity of class imbalance and help to improve prediction performance. Based on the proposed over-sampling algorithm, a predictor, called TargetSOS, is implemented for protein-nucleotide binding residue prediction. Cross-validation tests and independent validation tests demonstrate the effectiveness of TargetSOS. The web-server and datasets used in this study are freely available at <a href="http://www.csbio.sjtu.edu.cn/bioinf/TargetSOS/" target="_blank">http://www.csbio.sjtu.edu.cn/bioinf/TargetSOS/</a>.</p></div

    Characteristics of the studies included in the meta-analysis for the summarized prevalence of SCH in T2DM patients.

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    <p>a, Mean ages were expressed in mean ± SD or mean.</p><p>b, These original articles did not provide the mean age of the total population, so age data was extracted according to the age characteristics mentioned in the articles included.</p><p>c, adjusted pooled prevalence.</p><p>Abbreviation: F, female; M, male; C, subjects without SCH; S, subjects with SCH; TSH, thyroid stimulating hormone; CC, case control study; CSS, cross-sectional study.</p><p>Characteristics of the studies included in the meta-analysis for the summarized prevalence of SCH in T2DM patients.</p
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