Objective:To explore the application of Allostatic Load (AL) in evaluation the effect of management for patients with type 2 diabetes.Methods:According to the control objectives of each indicator in the Guidelines for the Prevention and Treatment of Type 2 Diabetes in China, the P75 and P80 of the subjects before intervention, AL was calculated, which were AL1-1, AL1-2 and AL1-3 respectively. On the basis of the above three indicators, the number of complications was taken as the numerical sum to obtain AL2-1, AL2-2 and AL2-3. Study participants were from a one-year randomized controlled intervention trial in patients with type 2 diabetes. Patients in the control group were treated with the integrated diabetes management mode used bg the tier-three medical institutions, while the intervention group added the intervention based on " Your Doctor" App on the basis of the above strategy. The changes of 6 types of AL and HbA 1C in the two groups before and after intervention were compared. Two groups of patients were treated with propensity score matching method to ensure the balanced distribution of covariates between groups. Multiple regression analysis was used to explore the factors influencing the effect of AL intervention. Two independent samples t-test, paired t-test and chi-square test were used to analyze the efficacy of the intervention. Results:After propensity score matching, the control group and the intervention group each included 483 patients. Among the six types of AL, the score of AL2-1 was the highest, while the score of AL1-3 was the lowest. Compared with baseline, all other indicators in the two groups decreased after intervention except ALL-3 in control group ( P<0.05).Patients who were residing in rural areas, having less co-habitants and more diabetic complications, and lower annual household income, shown higher AL ( P<0.05). The number of complications and the situation of urban or rural areas were the factors influencing the effect of AL intervention. Conclusions:More research is needed on AL as an early evaluation index of the effect of comprehensive management of diabetes.

Objective:To investigate the value of baseline 18F-fluorodeoxyglucose (FDG) PET/CT radiomics features in predicting the prognosis of non-small cell lung cancer (NSCLC) before treatment. Methods:From January 2016 to August 2018, a total of 300 patients with solitary NSCLC (189 males, 111 females, age (62.3±9.0) years) who underwent 18F-FDG PET/CT imaging before treatment in Fourth Hospital of Hebei Medical University were retrospectively analyzed. According to the ratio of 7∶3 (R language), 300 patients were randomly divided into training group ( n=210) and validation group ( n=90). LIFEx software package was used to extract the PET and CT radiomics features of primary focus in 300 NSCLC patients. The least absolute shrinkage and selection operator (LASSO) algorithm combined with Cox proportional hazard regression model were used to select radiomics features and clinical features for predicting overall survival (OS) and progression-free survival (PFS) in training group. Then radiomics model, clinical model and complex model which integrated the two were established and the radiomics score (Rad-score), clinical score and complex score of each patient were calculated. Data of validation group was used to validate each training model. Efficiencies of each model in predicting the prognosis of patients with NSCLC were further evaluated by the concordance index (C-index), and a nomogram was developed based on the best prediction model. Results:In training group, the C-indices of predicting OS and PFS in NSCLC patients of radiomics model were 0.762 and 0.724 respectively, which were 0.834 and 0.780 respectively in clinical model, and were 0.842 and 0.787 respectively in complex model. Cox multivariate analysis showed that both Rad-score and complex score were independent prognostic factors for OS (hazard ratio ( HR): 1.804, 9.996, 95% CI: 1.023-3.184, 4.582-21.808, both P<0.05) and PFS ( HR: 1.771, 5.627, 95% CI: 1.138-2.756, 3.429-9.234, both P<0.05). Conclusions:Pre-treatment 18F-FDG PET/CT radiomics can predict OS and PFS of NSCLC patients. The complex model based on the combination of radiomics and clinical model is effective in predicting the prognosis of NSCLC patients, and the nomogram of complex model is simple and convenient to assist clinical decision-making.