Objective:To establish a prediction model for larger for gestational age(LGA)infants in pregnant women with gestational diabetes mellitus(GDM)in order to improve pregnancy outcomes.Methods:A retro-spective analysis was performed on the clinical data of 338 pregnant women with GDM who underwent routine prenatal examinations and were hospitalized for delivery in the First Affiliated Hospital of Nanjing Medical Universi-ty from January 1,2018 to December 31,2023.Pregnant women with complete HbAlc data during pregnancy were divided into a training set of 241 cases and a validation set of 97 cases.Lasso and Logistic regression analysis and variable screening combined with previous clinical experience were used to construct a nomogram model,and its degree of differentiation and calibration were evaluated.Result:①By Lasso regression analysis,age,family histo-ry of type 2 diabetes,body mass index(BMI),gestational weight gain(GWG),fasting blood glucose(FBG),postprandial 1-hour blood glucose(1h PBG),HbAlc,free triiodothyronine(FT3),free thyroxine(FT4)and insulin treatment were important predictors of LGA.②Multivariate Logistic regression analysis showed that GWG and HbAlc were independent risk factors for LGA in pregnant women with GDM(OR＞1,P＜0.05).③Combined with Lasso and Logistic regression analysis,previous literature reports and clinical experience,BMI,GWG,FBG,1h PBG,HbAlc and FT3 were selected as independent variables,and LGA as dependent variable.A nomogram pre-diction model was constructed in the training set,and the C-index of 0.71.ROC curve analysis showed that the AUC values of the training set and the validation set were 0.709 and 0.700,respectively,and the discriminative a-bility of the model was acceptable.The calibration curve of the model was close to the ideal curve,and the clinical decision curve suggested that the model showed a positive net benefit at the threshold of 10％to 50％.Conclu-sion:The predictive model has certain value in predicting the occurrence of LGA in pregnant women with GDM,and provides help for early diagnosis,treatment and clinical intervention of GDM and its complications,in order to improve perinatal and long-term adverse outcomes.

Objective:To establish a prediction model for larger for gestational age(LGA)infants in pregnant women with gestational diabetes mellitus(GDM)in order to improve pregnancy outcomes.Methods:A retro-spective analysis was performed on the clinical data of 338 pregnant women with GDM who underwent routine prenatal examinations and were hospitalized for delivery in the First Affiliated Hospital of Nanjing Medical Universi-ty from January 1,2018 to December 31,2023.Pregnant women with complete HbAlc data during pregnancy were divided into a training set of 241 cases and a validation set of 97 cases.Lasso and Logistic regression analysis and variable screening combined with previous clinical experience were used to construct a nomogram model,and its degree of differentiation and calibration were evaluated.Result:①By Lasso regression analysis,age,family histo-ry of type 2 diabetes,body mass index(BMI),gestational weight gain(GWG),fasting blood glucose(FBG),postprandial 1-hour blood glucose(1h PBG),HbAlc,free triiodothyronine(FT3),free thyroxine(FT4)and insulin treatment were important predictors of LGA.②Multivariate Logistic regression analysis showed that GWG and HbAlc were independent risk factors for LGA in pregnant women with GDM(OR＞1,P＜0.05).③Combined with Lasso and Logistic regression analysis,previous literature reports and clinical experience,BMI,GWG,FBG,1h PBG,HbAlc and FT3 were selected as independent variables,and LGA as dependent variable.A nomogram pre-diction model was constructed in the training set,and the C-index of 0.71.ROC curve analysis showed that the AUC values of the training set and the validation set were 0.709 and 0.700,respectively,and the discriminative a-bility of the model was acceptable.The calibration curve of the model was close to the ideal curve,and the clinical decision curve suggested that the model showed a positive net benefit at the threshold of 10％to 50％.Conclu-sion:The predictive model has certain value in predicting the occurrence of LGA in pregnant women with GDM,and provides help for early diagnosis,treatment and clinical intervention of GDM and its complications,in order to improve perinatal and long-term adverse outcomes.

Objective To investigate the epidemic of severe acute respiratory symdrome(SARS) in Beijing blood donors and make guidance for assuring blood safety during SARS epidemic.Methods Using SARSCoV Ab ELISA Kits, specimens from 2357 donors from Beijing during SARS epidemic phase,1079 preserved samples from Beijing donors collected well before the SARS epidemic,1183 donors from Shandong and Hunan provinces where no SARS had been reported were screed for IgG,IgM,and total antibodies against SARS coronavirus.Donors with reactive samples were followed up,RT PCR were performed to detect the SARS CoV RNA.Results There was no significant difference between the 3 groups of specimens and there was no SARS epidemic or subclinical SARS infections among Beijing blood donors.Conclusion Instead of blood SARS CoV Ab screening, we should focus on the donors inquiry,physical examination and education to prevent SARS transmission by transfusion.