Objective To explore the influencing factors for macrosomia in pregnant women with gestational diabetes mellitus(GDM)who had isolated elevated postprandial blood glucose.Methods A total of 1188 GDM pregnant women with isolated elevated postprandial blood glucose who underwent regular prenatal examination and gave birth at the First People's Hospital of Kunshan City were enrolled in this study from January 2019 to December 2021.The age,pre-pregnancy body mass index(BMI),weight gain during pregnancy,times of pregnancy,times of delivery,fasting plasma glucose(FPG)during first trimester,glycosylated hemoglobin A1c(HbA1c)during first trimester,results of oral glucose tolerance test(OGTT),area under the curve(AUC)of OGTT time-blood glucose curve in the second trimester and neonatal birth weight were analyzed in all the participants.All the newborns were divided into macrosomia group(n=167)and non-macrosomia group(n=1021)according to the birth weight.Logistic analysis was conducted to investigate the influencing factors for macrosomia delivery in GDM pregnant women with isolated elevated postprandial blood glucose.Results The pregnancy weight gain,1,2 hPG,OGTT time blood glucose AUC,OGTT abnormal incidence,and multiparous women rate were higher in macrosomia group than in non-macrosomia group(P＜0.05).Logistic regression analysis showed that gestational weight gain,OGTT time blood glucose AUC,and multiparous women were the influencing factors for delivery of macrosomia in GDM pregnant women with isolated elevated postprandial blood glucose.Conclusions Weight gain during pregnancy,AUC of OGTT time-blood glucose curve and pregnant women are the risk factors for predicting macrosomia in GDM pregnant women with isolated elevated postprandial blood glucose.

Objective To explore the influencing factors for macrosomia in pregnant women with gestational diabetes mellitus(GDM)who had isolated elevated postprandial blood glucose.Methods A total of 1188 GDM pregnant women with isolated elevated postprandial blood glucose who underwent regular prenatal examination and gave birth at the First People's Hospital of Kunshan City were enrolled in this study from January 2019 to December 2021.The age,pre-pregnancy body mass index(BMI),weight gain during pregnancy,times of pregnancy,times of delivery,fasting plasma glucose(FPG)during first trimester,glycosylated hemoglobin A1c(HbA1c)during first trimester,results of oral glucose tolerance test(OGTT),area under the curve(AUC)of OGTT time-blood glucose curve in the second trimester and neonatal birth weight were analyzed in all the participants.All the newborns were divided into macrosomia group(n=167)and non-macrosomia group(n=1021)according to the birth weight.Logistic analysis was conducted to investigate the influencing factors for macrosomia delivery in GDM pregnant women with isolated elevated postprandial blood glucose.Results The pregnancy weight gain,1,2 hPG,OGTT time blood glucose AUC,OGTT abnormal incidence,and multiparous women rate were higher in macrosomia group than in non-macrosomia group(P＜0.05).Logistic regression analysis showed that gestational weight gain,OGTT time blood glucose AUC,and multiparous women were the influencing factors for delivery of macrosomia in GDM pregnant women with isolated elevated postprandial blood glucose.Conclusions Weight gain during pregnancy,AUC of OGTT time-blood glucose curve and pregnant women are the risk factors for predicting macrosomia in GDM pregnant women with isolated elevated postprandial blood glucose.

Objective To develop deep learning-based auxiliary diagnostic models for diverse pulmonary diffuse cystic diseases,and subsequently evaluate their classification performance to identify the optimal model for clinical diagnosis.Methods A total of 288 patients diagnosed with idiopathic pulmonary fibrosis(IPF),pulmonary lymphangioleiomyomatosis(PLAM),and pulmonary Langerhans cell histiocytosis(PLCH)were prospectively enrolled from the First Affiliated Hospital of Guangzhou Medical University between January 2010 and October 2022,comprising 76 cases of IPF,179 cases of PLAM,and 33 cases of PLCH.A total of 877 CT cases were collected,comprising 232 cases of IPF,557 cases of PLAM,and 88 cases of pulmonary PLCH.Based on the cutoff date of December 31,2019,the CT scans were divided into two datasets:dataset A consisted of 500 CT scans including 185 IPF cases,265 PLAM cases,and 50 PLCH cases;while dataset B comprised 377 CT scans with a distribution of 47 IPFcases,292 PLAMcases,and 38 PLCH cases.The Dataset A was randomly partitioned into training set,validation set,and test set in a ratio of 7∶1∶2.Subsequently,six distinct deep learning neural networks were employed for training after preprocessing and data augmentation.Receiver operating characteristic curves were generated to assess the model performance using metrics such as area under the curve(AUC),accuracy,sensitivity,specificity,and F1 score in order to identify the optimal model.Furthermore,a test set B comprising 30 randomly selected cases from dataset B for each disease type was utilized to evaluate the trained optimal model by employing the same aforementioned metrics.Results In test A,six well-established diagnostic models demonstrated superior classification performance for IPF and LAM,with an AUC greater than 0.9.For LCH,EfficientNet exhibited low classification efficiency with an AUC between 0.6 and 0.7,while Vgg11 showed an AUC between 0.8 and 0.9;the other four models displayed excellent classification efficiency with an AUC greater than 0.9.Except for Inception V3,the remaining five diagnostic models performed poorly in identifying and classifying LCH lesions.Considering multiple indicators,the InceptionV3 model showcased optimal comprehensive performance among the six models,achieving high evaluation parameters such as overall accuracy(94.90%),precision(93.49%),recall(90.84%),and specificity(96.91%).TestB was conducted using the trained InceptionV3 model resulting in an accuracy of 81%,precision of 82%,recall of 81%,and specificity of 90%.Conclusions Six recognition and classification models,developed using deep learning technology in conjunction with pulmonary CT images,demonstrate effective discrimination between LAM,LCH,and IPF.Notably,the model constructed utilizing the InceptionV3 neural network exhibits superior efficiency in accurately recognizing and classifying IPF and LAM.

AIM: To investigate the effects of fibroblast growth factor 10 ( FGF10 ) on lipopolysaccharide ( LPS)-induced microglial activation .METHODS:Mouse BV2 microglial cells were maintained in DMEM in a humidified incubator with 95%/5%( V/V) mixture of air and CO 2 at 37℃.The medium was changed every 1 or 2 d.The cells were digested and passaged every 4 or 5 d.The BV2 microglial cells were first pretreated with FGF 10 (1 mg/L) for 30 min and then stimulated with LPS (500 μg/L).The medium and the cells were collected at different time points .The morphologi-cal changes of microglia were visualized under microscope .To evaluate the microglial activation , the transcription and pro-duction of proinflammatory factor tumor necrosis factor-α( TNF-α) were examined by real-time quantitative polymerase chain reaction (RT-qPCR) and enzyme-linked immunosorbent assay (ELISA), respectively.RESULTS:The morphology of control BV2 microglia showed circular or oval shape .After exposure to LPS for 24 h, the microglia revealed spindle shaped or multipolar morphology , and the percentage of activated cells was significantly increased compared with control group.Pretreatment with FGF10 successfully inhibited the morphological change from normal to activated shape .LPS sti-mulation for 6 h significantly increased the transcription of TNF-α, while FGF10 pretreatment remarkably reversed the effect.In addition, the production of TNF-αincreased in the presence of LPS stimulation for 24 h compared with control group.Pretreatment with FGF10 suppressed LPS-induced TNF-αexpression.CONCLUSION: Pretreatment with FGF10 inhibits the morphological change from normal to activated shape , and remarkably suppressed the transcription and produc-tion of TNF-α.FGF10 successfully suppresses LPS-induced BV2 microglial activation , indicating that FGF10 is a therapeu-tic agent for the treatment of glia-mediated neuroinflammatory diseases .

r diagnosing the disease to combine pathology, immunohistochemistry and SYT-SSX gene detection.