Abstract: The present invention relates to a system for predicting gestational diabetes mellitus (GDM) of pregnant individuals, wherein the system comprises an operation module, and the operation model comprises a support vector regression model, and the system is used to predict the plasma glucose levels at 1 hour and/or 2 hours of oral glucose tolerance test (OGTT) by using a support vector regression developed prediction model generated by substituting the concentration of the biomarkers in fasting blood samples of pregnant individuals. The present invention provides biomarkers and biomarker-based diagnostic models for differential diagnosis of gestational diabetes mellitus (GDM), which can be applied to diagnosis or prediction of GDM in early stage and are of great significance to the prevention or treatment of GDM.

Abstract: The present disclosure provides a biomarker for detecting colorectal cancer and a use thereof. A metabolomics method is used to analyze metabolites with significant differences in urine of patients with colorectal cancer and normal people, such that a series of biomarkers capable of early predicting an occurrence risk of colorectal cancer are screened out, a group of biomarkers are further screened to construct a diagnostic model for colorectal cancer, and the model can be used for conveniently, non-invasively and effectively predicting whether an individual suffers from colorectal cancer, and meets clinical needs.

Abstract: The present invention relates to a system for predicting gestational diabetes mellitus (GDM) of pregnant individuals, wherein the system comprises an operation module, and the operation model comprises a support vector regression model, and the system is used to predict theplasma glucose levels at 1 hour and/or 2 hours of oral glucose tolerance test (OGTT) by using a support vector regression developed prediction model generated bysubstituting the concentration of the biomarkers in fasting blood samples of pregnant individuals. The present invention provides biomarkers and biomarker-based diagnostic models for differential diagnosis of gestational diabetes mellitus (GDM), which can be applied to diagnosis or prediction of GDM in early stage and are of great significance to the prevention or treatment of GDM.

Abstract: Provided is a model for evaluating the degree of liver fibrosis constructed based on bile acids. A plurality of bile acids are simultaneously detected by a liquid chromatography-tandem mass spectrometer to further improve the accuracy in combination with other liver indicators; moreover, a multiple regression analysis method is applied to establish a grading diagnosis model for the degree of liver fibrosis caused by a chronic liver disease, which can significantly improve the sensitivity and specificity of the existing non-invasive diagnosis of liver fibrosis. When the model is used to evaluate the degree of liver fibrosis of a patient, the highest AUC is up to 0.9278; the sensitivity is up to 86.79%; and the specificity is up to 89.01%. The detection results are completely consistent with the pathological results of clinical liver biopsy. Therefore, patients need not receive a liver biopsy.