Objective To explore the optimal HbAlc diagnostic cutpoint in different glucose tolerance populations in the plateau region.Methods (1) 472 diabetes mellitus (DM) patients and highrisk groups accepting diabetes screening in the First Affiliated Hospital of Kunming Medical College (217 males and 255 females,≥20 years old,median age 54 years old) were collected,oral glucose tolerance test (OGTT) and HbAlc were tested.(2) the research subjects were divided into normal glucose adjustment group (NGT),Impaired fasting glucose group (IFG) and (or) Impaired glucose tolerance IGT group and diabetes mellitus (DM) group.The receiver-operating characteristic curve (ROC) was explored to determine the optimal HbA1c diagnostic cut point for IFG,IGT and DM status respectively.Results The average HbA1 c values of NGT,IFG and (or) IGT,DM groups were (6.06 ± 0.11) ％,(6.63 ± 0.11) ％,(8.70 ± 2.08)％ respectively,for IFG and IGT groups,the optimal HbA1c diagnostic cut points were 6.7％ and 6.6％,respectively; If use either FBG or 2 h PG to diagnose DM,the corresponding optimal HbA1 c diagnostic cut point was 7.1％ ; If use anyone of FBG or 2hPG to diagnose DM,the corresponding optimal HbA1c diagnostic cut point was 7.0％ ; If both FBG and 2hPG were used to diagnose DM,the corresponding optimal HbA1 c diagnostic cut point was 7.1％.Conclusion Preliminarily confirm the optimal HbA1c diagnostic cut point in different glucose tolerance populations in the plateau region of Kunming,and provide the evidence for further clinical application of HbA1c.

Objective:To investigate the value of ADC derived from DWI combined with texture analysis derived from T 2WI fat suppressed images in distinguishing benign and malignant soft tissue tumors. Methods:The MRI and DWI images of 94 patients with soft tissue tumors (44 cases with malignant and 50 cases with benign) confirmed by pathology were analyzed retrospectively in the First Affiliated Hospital of USTC West District. ADC values of solid components were measured at GE ADW4.6 workstation. The texture features were extracted by manually drawing the ROI on the maximum level of the T 2WI fat suppressed images; the ADC values and texture parameters between the two groups were statistically analyzed by SPSS17.0, and the multivariate logistic regression model were conducted to analyze and calculate the diagnostic performance. Results:ADC value of benign and malignant soft tissue tumors was (1.6±0.3)×10 -3 mm 2/s, (1.2±0.5)×10 -3 mm 2/s, respectively, and the difference was statistically significant( t=-5.382, P<0.05). Taking 1.28×10 -3 mm 2/s as the critical value, the area under curve (AUC) for the diagnosis of benign and malignant soft tissue tumors was 0.783, the sensitivity was 92.00%, and the specificity was 65.91%. Among the texture features, the AUC of frequency size, skewness, Inertia All Direction_offset7, Inverse Difference Moment angle0_offset1, Inverse Difference Moment angle0_offset7 and Haralick Correlation All Direction_offset4_SD distinguishing benign and malignant soft tissue tumors were 0.825, 0.739, 0.826, 0.816, 0.820 and 0.783, respectively. The AUC, sensitivity and specificity of the best predictive model distinguishing benign and malignant soft tissue tumors were 0.930, 88.00% and 86.36% respectively using multivariate logistic regression analysis. Conclusion:ADC combined with texture analysis is of great value in preoperative differentiation of benign and malignant soft tissue tumors.