ObjectiveTo investigate the relationship between the non-fasting triglyceride and glucose （TyG） index and hyperglycemia in pregnancy during the third trimester in high altitudes. MethodsThis study selected clinical and laboratory data of 774 Tibetan singleton pregnant women who delivered at Chaya People's Hospital of Qamdo city in Xizang autonomous region， from January 2023 to April 2025. The non-fasting TyG index was calculated from non-fasting triglyceride （TG） and random plasma glucose （PG）. Based on the tertiles of the non-fasting TyG index values， the individuals were split into three groups （corresponding to non-fasting TyG index of 8.89 and 9.21， respectively）. The baseline clinical characteristics， lipid levels and the occurrence of developing hyperglycemia in pregnancy were compared among the three groups. Statistical analyses were performed using ANOVA， Kruskal-Wallis H test， Chi-square test， or Fisher exact test and the relationship between the non-fasting TyG index and hyperglycemia in pregnancy were examined using multivariate logistic regression models and curve fitting. ResultsA total of 774 Tibetan singleton pregnant women were included， with a average age of 27.3 ± 6.1 years， a pre-delivery body mass index （Pre-BMI） of （25.2±2.3）kg/m² ， a proportion of 26.7% （207/774） primigravid women， the mean non-fasting TyG index was 9.1 ± 0.4。Thirty pregnant women were diagnosed with hyperglycemia in pregnancy， with a detection rate of 3.9% （30/774）. Statistically significant differences in serum total cholesterol （TC）， TG， low-density lipoprotein cholesterol （LDL-C） and high-density lipoprotein cholesterol （HDL-C） levels were identified when comparing different non-fasting TyG groups （all P values <0.05）. Subsequent trend test analysis indicated that the levels of TC， TG， LDL-C， and PG gradually increased with elevated the non-fasting TyG index （ F_{trend TC}=95.61， P<0.001； F_{trend TG}=1 051.91， P<0.001； F_{trend LDL-C} = 97.20， P < 0.001； F_{trend TG}=195.20； P<0.001）. After adjustment for maternal age， pre-delivery BMI， altitude， TC， LDL-C， and HDL-C， multivariate Logistic regression models revealed independent positive associations between non-fasting TyG index and hyperglycemia in pregnancy （Model 1： OR=2.72， 95% CI： 1.13-6.53， P=0.026； Model 2： OR=2.56， 95% CI： 1.01-6.50， P=0.048； Model 3： OR=2.72， 95% CI： 1.06-6.97， P=0.037； Model 4： OR=4.02， 95% CI： 1.42-11.40， P=0.009） and the incident of hyperglycemia in pregnancy showed an increasing tendency as increasing with the non-fasting TyG index， however， this association did not statistical significance （P _trend >0.05）. Curve fitting by restricted cubic splines （RCS） were used to assess linearity between non-fasting TyG and hyperglycemia in pregnancy， and there was a linear dose-response relationship between non-fasting TyG and hyperglycemia in pregnancy （P _{for non-linear} = 0.515）. ConclusionNon-fasting TyG index in the third trimester is a risk factor for hyperglycemia in pregnancy among the Tibetan singleton pregnant women at high altitudes and there was a possible linear dose-response relationship between the non-fasting TyG index and hyperglycemia in pregnancy.

Objective To investigate the effect of blood flow restriction combined with low-intensity plyometric jump training(LI-PJT+BFR)on lower limb dynamic postural control of functional ankle instability(FAI)in college students. Methods From March to May,2023,40 FAI college students were recruited from Xi'an Physical Education University,and randomly divided into high-intensity plyometric jump training(HI-PJT,n = 14)group,low-intensity plyomet-ric jump training(LI-PJT,n = 13)group and LI-PJT+BFR group(n = 13).All the groups finished the six-week corresponding training.The maximum voluntary isometric contraction(MVIC)of tibialis anterior,peroneus lon-gus,lateral head of gastrocnemius,gluteus maximus,vastus lateralis,biceps femoris and semitendinosus were measured,and the root mean square(RMS)of electromyography of these muscles was measured during the sin-gle-leg landing(SLL),using wireless surface electromyography before and after intervention.Moreover,they were assessed with Y-balance test and Cumberland Ankle Instability Tool(CAIT). Results MVIC and RMS of the target muscles improved after intervention in all the groups(t>2.218,P<0.05),except MVIC and RMS of peroneus longus,gluteus maximus,biceps femoris and semitendinosus in LI-PJT group,and RMS of peroneus longus in LI-PJT+BFR group;and MVIC and RMS of the target muscles were the least in LI-PJT group(F>3.262,P<0.05),except those of peroneus longus.The extension scores of Y-balance test and the total score improved after intervention(t>2.485,P<0.05),and they were the least in LI-PJT group(F>5.042,P<0.05).The CAIT score improved after intervention(t>5.227,P<0.001),and it was the least in LI-PJT group(F = 4.640,P<0.05). Conclusion LI-PJT+BFR could improve lower limb dynamic postural control of FAI college students,which is similar to HI-PJT.

Objective:To investigate the predictive value of serum uric acid/albumin ratio (sUAR) for acute kidney injury (AKI) after cardiac valve surgery.Methods:The clinical data of adult patients undergoing cardiac valve surgery under cardiopulmonary bypass from January 2021 to December 2021 from the Heart Center of Henan Provincial People's Hospital were collected retrospectively, and the sUAR was calculated. All patients were divided into AKI group and non-AKI group according to whether AKI occurred within 7 days after cardiac valve surgery, and the differences of clinical data between the two groups were compared. Multivariate logistic regression model was used to analyze the independent correlation factors of AKI after cardiac valve surgery. The receiver operating characteristic (ROC) curve was used to evaluate the performance of relevant indicators.Results:A total of 422 patients were enrolled, including 194 females (46.0%), 141 hypertension patients (33.4%) and 172 atrial fibrillation patients (40.8%). They were 57 (50, 65) years old. Their sUAR was 8.13 (6.57, 9.54) μmol/g, and hemoglobin was 135 (125, 145) g/L. There were 142 cases in AKI group and 280 cases in non-AKI group, and the incidence of AKI after cardiac valve surgery was 33.6%. Age, atrial fibrillation rate, baseline serum creatinine, N terminal pro B type natriuretic peptide, serum urea,serum uric acid, blood glucose and sUAR were higher in the AKI group than those in the non-AKI group (all P<0.05), and estimated glomerular filtration rate, lymphocyte count,hemoglobin and serum albumin were lower in the AKI group than those in the non-AKI group (all P<0.05). The median cardiopulmonary bypass time of patients in the AKI group was slightly longer than that in the non-AKI group, but the difference was not statistically significant [159 (125, 192) min vs. 151 (122, 193) min, Z=-0.797, P=0.426], and there were no statistically significant differences in other indicators between the two groups. The results of multivariate logistic regression analysis showed that sUAR ( OR=1.467, 95% CI 1.308-1.645, P<0.001), age ( OR=1.045, 95% CI 1.020-1.072, P<0.001), atrial fibrillation ( OR=2.520, 95% CI 1.580-4.020, P<0.001), hemoglobin ( OR=0.984, 95% CI 0.971-0.997, P=0.015) were the independent correlation factors. ROC curve analysis showed that the area under the curve ( AUC) of sUAR predicting AKI after cardiac valve surgery was 0.710 (95% CI 0.659-0.760, P<0.001) with a sensitivity of 85.2% and specificity of 45.0% for the sUAR cut-off point of 7.28 μmol/g. The AUC for the diagnosis of AKI after cardiac valve surgery was 0.780 (95% CI 0.734-0.825, P<0.001) with a sensitivity of 72.5% and specificity of 71.8% for the combination of sUAR with age, hemoglobin and atrial fibrillation. Conclusions:For patients undergoing cardiac valve surgery under cardiopulmonary bypass, preoperative high sUAR is an independent risk factor for postoperative AKI, and sUAR has a certain predictive value for postoperative AKI.

Objective:To explore the impact of baseline remnant cholesterol levels at a single time point and cumulative remnant cholesterol exposure on the progression trajectories of arterial stiffness.Methods:This prospective cohort study included 2 401 eligible participants from the Beijing Health Management Cohort who consecutively attended health examinations in 2010-2011, 2012-2013, and 2014-2015. The remnant cholesterol value measured in 2014-2015 served as the baseline remnant cholesterol level at a single time point. The cumulative exposure indices were calculated based on remnant cholesterol values from three health examinations from 2010 to 2015, including cumulative exposure, cumulative exposure burden, and duration of high remnant cholesterol exposure. Arterial stiffness was assessed by brachial-ankle pulse wave velocity (baPWV). The follow-up continued until December 31, 2019, with annual check-ups. During the follow-up period, a group-based trajectory model was employed to construct the progression trajectories of baPWV. The associations between the baseline remnant cholesterol level, cumulative exposure indices of remnant cholesterol and baPWV trajectories were examined using ordinal logistic regression models, adjusting for traditional cardiovascular risk factors and low-density lipoprotein cholesterol (LDL-C) levels.Results:The age of the 2 401 participants was 61 (54, 69) years, with 1 801 (75.01%) being male. The group-based trajectory model indicated that the best-fit model categorized the participants into three subgroups: low-rising group (1 036 individuals, 43.15%), moderate-rising group (1 137 individuals, 47.36%), and high-rising group (228 individuals, 9.50%). After adjusting for traditional cardiovascular risk factors, baseline remnant cholesterol levels at a single point ( OR=1.170, 95% CI: 1.074-1.274), cumulative remnant cholesterol exposure ( OR=1.194, 95% CI: 1.096-1.303), cumulative remnant cholesterol exposure burden ( OR=1.270, 95% CI: 1.071-1.507), and high-remnant cholesterol exposure duration (6 years: OR=1.351, 95% CI: 1.077-1.695) were significantly associated with the risk of developing a poor baPWV progression trajectory. These results remained significant after adjusting for cumulative average LDL-C levels. The association between baseline remnant cholesterol levels and baPWV progression became insignificant after adjusting for cumulative remnant cholesterol levels ( OR=1.053, 95% CI: 0.923-1.197), while the association between cumulative remnant cholesterol exposure and baPWV progression remained significant after adjusting for baseline remnant cholesterol levels ( OR=1.145, 95% CI: 1.008-1.305). Conclusions:Higher levels of baseline remnant cholesterol and cumulative remnant cholesterol are independent risk factors for the progression of arterial stiffness. These associations remain significant even after adjusting for traditional cardiovascular risk factors and LDL-C levels. Furthermore, the effect of cumulative remnant cholesterol levels on the progression of arterial stiffness was stronger than the effect of baseline remnant cholesterol levels.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.