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  • SAGE Publications  (2)
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  • SAGE Publications  (2)
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  • 1
    Online Resource
    Online Resource
    Evaluation of urinary biomarkers for prediction of diabetic kidney disease: a propensity score matching analysis
    SAGE Publications ; 2019
    In:  Therapeutic Advances in Endocrinology and Metabolism Vol. 10 ( 2019-01), p. 204201881989111-
    Details
    In: Therapeutic Advances in Endocrinology and Metabolism, SAGE Publications, Vol. 10 ( 2019-01), p. 204201881989111-
    Abstract: The aim of this study was to evaluate the diagnostic value of six urinary biomarkers for prediction of diabetic kidney disease (DKD). Methods: The cross-sectional study recruited 1053 hospitalized patients with type 2 diabetes mellitus (T2DM), who were categorized into the diabetes mellitus (DM) with normoalbuminuria (NA) group ( n = 753) and DKD group ( n = 300) according to 24-h urinary albumin excretion rate (24-h UAE). Data on the levels of six studied urinary biomarkers [transferrin (TF), immunoglobulin G (IgG), retinol-binding protein (RBP), β-galactosidase (GAL), N-acetyl-beta-glucosaminidase (NAG), and β2-microglobulin (β2MG)] were obtained. The propensity score matching (PSM) method was applied to eliminate the influences of confounding variables. Results: Patients with DKD had higher levels of all six urinary biomarkers. All indicators demonstrated significantly increased risk of DKD, except for GAL and β2MG. Single RBP yielded the greatest area under the curve (AUC) value of 0.920 compared with the other five markers, followed by TF (0.867) and IgG (0.867). However, GAL, NAG, and β2MG were shown to have a weak prognostic ability. The diagnostic values of the different combinations were not superior to the single RBP. Conclusions: RBP, TF, and IgG could be used as reliable or good predictors of DKD. The combined use of these biomarkers did not improve DKD detection.
    Type of Medium: Online Resource
    ISSN: 2042-0188 , 2042-0196
    URL: Article
    DOI: 10.1177/2042018819891110
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2019
    detail.hit.zdb_id: 2554822-0
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Fast convolution sparse filtering and its application on gearbox fault diagnosis
    SAGE Publications ; 2020
    In:  Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering Vol. 234, No. 9 ( 2020-08), p. 2291-2304
    Details
    In: Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, SAGE Publications, Vol. 234, No. 9 ( 2020-08), p. 2291-2304
    Abstract: Transmission, as a critical part of vehicles, is the hub of power transmission and the core of controlling speed change. Condition monitoring and diagnosis of transmissions have become an effective tool to ensure vehicle safety travelling. The intelligent fault diagnosis strategy using artificial intelligent methods has been studied and applied for gearbox fault diagnosis. However, most algorithms cannot guarantee both accuracy and training efficiency. In this paper, fast convolutional sparse filtering based on convolutional activation and feature normalization is proposed for gearbox fault diagnosis without any time-consuming preprocessing. In fast convolutional sparse filtering, the features of samples are optimized instead of local features, which could obviously reduce the dimension and construction time of the Hessian matrix. In addition, the output features are equally active to guarantee that all features have similar contributions. The l 2 -norm of the training features is recorded and used for pseudo-normalization of the test features. The proposed fast convolutional sparse filtering is validated by a bearing fault dataset and a planetary gear fault dataset. Verification results confirm that fast convolutional sparse filtering is a promising tool for fault diagnosis, which has obviously improved the diagnosis accuracy, training efficiency, and robustness and provides the greater advantage of handling large-scale datasets.
    Type of Medium: Online Resource
    ISSN: 0954-4070 , 2041-2991
    URL: Article
    DOI: 10.1177/0954407020907818
    RVK:
    ZO 4200
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2020
    detail.hit.zdb_id: 2032754-7
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