Comparison of various prediction models in the effect of laparoscopic sleeve gastrectomy on type 2 diabetes mellitus in the Chinese population 5 years after surgery

Background:The effect of bariatric surgery on type 2 diabetes mellitus(T2DM)control can be assessed based on predictive models of T2DM remission.Various models have been externally verified internationally.However,long-term validated results after laparoscopic sleeve gastrectomy(LSG)surgery are lacking.The best model for the Chinese population is also unknown.Methods:We retrospectively analyzed Chinese population data 5 years after LSG at Beijing Shijitan Hospital in China between March 2009 and December 2016.The independent t-test,Mann–Whitney U test,and chi-squared test were used to compare characteristics between T2DM remission and non-remission groups.We evaluated the predictive efficacy of each model for longterm T2DM remission after LSG by calculating the area under the curve(AUC),sensitivity,specificity,Youden index,positive predictive value(PPV),negative predictive value(NPV),and predicted-to-observed ratio,and performed calibration using Hosmer–Lemeshow test for 11 prediction models.Results:We enrolled 108 patients,including 44(40.7%)men,with a mean age of 35.5 years.The mean body mass index was 40.3±9.1 kg/m^(2),the percentage of excess weight loss(%EWL)was(75.9±30.4)%,and the percentage of total weight loss(%TWL)was(29.1±10.6)%.The mean glycated hemoglobin A1c(HbA1c)level was(7.3±1.8)%preoperatively and decreased to(5.9±1.0)%5 years after LSG.The 5-year postoperative complete and partial remission rates of T2DM were 50.9%[55/108]and 27.8%[30/108],respectively.Six models,i.e.,"ABCD",individualized metabolic surgery(IMS),advanced-DiaRem,DiaBetter,Dixon et al’s regression model,and Panunzi et al’s regression model,showed a good discrimination ability(all AUC>0.8).The"ABCD"(sensitivity,74%;specificity,80%;AUC,0.82[95%confidence interval[CI]:0.74–0.89]),IMS(sensitivity,78%;specificity,84%;AUC,0.82[95%CI:0.73–0.89]),and Panunzi et al’s regression models(sensitivity,78%;specificity,91%;AUC,0.86[95%CI:0.78–0.92])showed good discernibility.In the Hosmer–Lemeshow goodness-of-fit test,except for DiaRem(P<0.01),DiaBetter(P<0.01),Hayes et al(P=0.03),Park et al(P=0.02),and Ramos-Levi et al’s(P<0.01)models,all models had a satifactory fit results(P>0.05).The P values of calibration results of the"ABCD"and IMS were 0.07 and 0.14,respectively.The predicted-to-observed ratios of the"ABCD"and IMS were 0.87 and 0.89,respectively.Conclusion:The prediction model IMS was recommended for clinical use because of excellent predictive performance,good statistical test results,and simple and practical design features.

An ESIPT-based NIR-fluorescent probe for exosome labeling and in situ imaging

Exosomes play significant roles in physiological and tumorigenic processes and it is desirable to visualize and track the exosomes.Herein,a novel amphiphilic fluorescent probe HBT-Exo based on excited-state intramolecular proton transfer(ESIPT)mechanism is reported for exosome-labeling.Its ESIPT characteristics were confirmed by both theory calculation and experimental observation,which enable the probe to show a large Stokes shift as well as near-infrared(NIR)keto-form emission.HBT-Exo displayed excellent biocompatibility and remarkable efficiency for exosome-labeling in gastric cancer cells.Furthermore,the labeled exosomes were successfully applied for the real-time in situ imaging in mouse models.