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find Author "YAN Bin" 4 results
  • Current status and future perspectives of stomach-targeted endoscopic bariatrics

    Endoscopic bariatric treatment (EBT) is an effective method for the treatment of obesity. The principle of weight loss is similar to metabolic bariatric surgery. It can be classified as a food restriction (stomach-targeted) and malabsorption (small intestine-targeted). At present, a lot of EBT devices had been cleared by the US Food and Drug Administration to treat obesity, while the EBT in China lagged behind Western countries. Hence, we reviewed the current stomach-targeted EBT, aiming to provide a reference for the supplement of obesity treatment methods and the development of EBT in China.

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  • A Comparative Study on the Results of Liver Function between Olympus AU1000 and Backman CX7 Detection Systems

    Objective To study the comparability of liver function results between the two detection systems. Methods Based on the NCCLS EP9-A document, 8 fresh serum samples were collected daily for the assay of 10 routine liver function parameters by utilizing the Olympus AU1000 and Backman CX7 detection systems respectively. The results were recorded over 5 days consecutively. Linearity equation and relative deviations were calculated. The comparability between the results obtained from different detection systems was evaluated according to the systematic error at medical decision level of CLIA, 88. Results The paired t-test showed that the results of the fresh serum samples had no significant difference between the two detection systems (Pgt;0.05). Except that the systematic error of albumin at low medical decision level exceeded the allowable error, all the other systematic errors at medical decision level were acceptable. Conclusion The results of liver function are comparable between the two detection systems, and the systematic errors between the two detection systems are clinically acceptable.

    Release date:2016-08-25 03:34 Export PDF Favorites Scan
  • Research development of real-time functional magnetic resonance imaging neuro-feedback technology based on brain network connectivity

    The emergence of real-time functional magnetic resonance imaging (rt-fMRI) has provided foundations for neurofeedback based on brain hemodynamics and has given the new opportunity and challenge to cognitive neuroscience research. Along with the study of advanced brain neural mechanisms, the regulation goal of rt-fMRI neurofeedback develops from the early specific brain region activity to the brain network connectivity more accordant with the brain functional activities, and the study of the latter may be a trend in the area. Firstly, this paper introduces basic principle and development of rt-fMRI neurofeedback. Then, it specifically discusses the current research status of brain connectivity neurofeedback technology, including research approaches, experimental methods, conclusions, and so on. Finally, it discusses the problems in this field in the future development.

    Release date:2017-06-19 03:24 Export PDF Favorites Scan
  • Quantitative analysis of hepatocellular carcinomas pathological grading in non-contrast magnetic resonance images

    In order to solve the pathological grading of hepatocellular carcinomas (HCC) which depends on biopsy or surgical pathology invasively, a quantitative analysis method based on radiomics signature was proposed for pathological grading of HCC in non-contrast magnetic resonance imaging (MRI) images. The MRI images were integrated to predict clinical outcomes using 328 radiomics features, quantifying tumour image intensity, shape and text, which are extracted from lesion by manual segmentation. Least absolute shrinkage and selection operator (LASSO) were used to select the most-predictive radiomics features for the pathological grading. A radiomics signature, a clinical model, and a combined model were built. The association between the radiomics signature and HCC grading was explored. This quantitative analysis method was validated in 170 consecutive patients (training dataset: n = 125; validation dataset, n = 45), and cross-validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Through the proposed method, AUC was 0.909 in training dataset and 0.800 in validation dataset, respectively. Overall, the prediction performances by radiomics features showed statistically significant correlations with pathological grading. The results showed that radiomics signature was developed to be a significant predictor for HCC pathological grading, which may serve as a noninvasive complementary tool for clinical doctors in determining the prognosis and therapeutic strategy for HCC.

    Release date:2019-08-12 02:37 Export PDF Favorites Scan
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