Objective:To investigate the relationship between the serum levels of vascular endothelial growth factor (VEGF), matrix metalloproteinase-9 (MMP-9), S100 calcium binding protein B (S100B) with glycolipid metabolism, pregnancy outcome in pregnant women with gestational diabetes.Methods:The clinical data of 153 pregnant women with gestational diabetes (research group) and 153 healthy pregnant women (control group) in the Second Affiliated Hospital of Xi ′an Medical University from January 2020 to October 2023 were retrospectively analyzed. The serum levels of VEGF, MMP-9 and S100B were measured by enzyme linked immunosorbent assay, and the fasting blood glucose, triglyceride, total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), fasting insulin and glycated hemoglobin were measured, and the homeostasis model assessment insulin resistance index (HOMA-IR) was calculated. The adverse outcomes of pregnant women with gestational diabetes were recorded. Pearson method was used to analyze the correlation between glycolipid metabolism indexes and VEGF, MMP-9, S100B in pregnant women with gestational diabetes. Multivariate Logistic regression was used to analyze the independent risk factors of adverse pregnancy outcome in pregnant women with gestational diabetes. Receiver operating characteristic (ROC) curve was drawn to analyze the predictive value of VEGF, MMP-9 and S100B on adverse pregnancy outcome in pregnant women with gestational diabetes. Results:The fasting blood glucose, fasting insulin, glycated hemoglobin, HOMA-IR, triglyceride, total cholesterol, LDL-C, VEGF, MMP-9 and S100B in research group were significantly higher than those in control group: (9.42 ± 0.65) mmol/L vs. (4.13 ± 0.46) mmol/L, (16.58 ± 2.37) mU/L vs. (13.41 ± 2.05) mU/L, (7.28 ± 0.46)% vs. (4.35 ± 0.39)%, 4.83 ± 0.42 vs. 2.71 ± 0.37, (3.41 ± 0.67) mmol/L vs. (2.85 ± 0.63) mmol/L, (5.54 ± 1.56) mmol/L vs. (5.12 ± 1.50) mmol/L, (3.14 ± 0.97) mmol/L vs. (2.86 ± 0.93) mmol/L, (184.02 ± 30.25) ng/L vs. (156.33 ± 26.41) ng/L, (45.78 ± 7.56) μg/L vs. (29.36 ± 5.03) μg/L and (117.51 ± 25.12) ng/L vs. (89.74 ± 22.46) ng/L, the HDL-C was significantly lower than that in control group: (1.34 ± 0.27) mmol/L vs. (1.42 ± 0.30) mmol/L, and there were statistical differences ( P<0.01 or <0.05). Pearson correlation analysis result showed that VEGF, MMP-9, S100B in pregnant women with gestational diabetes were positively correlated with fasting blood glucose, fasting insulin, glycated hemoglobin, HOMA-IR, triglyceride, total cholesterol and LDL-C ( P<0.01), negatively correlated with HDL-C ( P<0.01). Among 153 pregnant women with gestational diabetes, 49 had adverse pregnancy outcome, and 104 had good pregnancy outcome. The VEGF, MMP-9 and S100B in pregnant women with adverse pregnancy outcome were significantly higher than those in pregnant women with good pregnancy outcome: (212.75 ± 28.63) ng/L vs. (170.49 ± 26.58) ng/L, (52.37 ± 7.14) μg/L vs. (42.68 ± 6.35) μg/L and (136.83 ± 23.62) ng/L vs. (108.41 ± 21.35) ng/L, and there were statistical differences ( P<0.01). Multivariate Logistic regression analysis result showed that VEGF, MMP-9 and S100B were independent risk factors for adverse pregnancy outcome in pregnant women with gestational diabetes ( OR = 7.013, 5.382 and 6.129; 95% CI 5.206 to 9.447, 3.449 to 8.398 and 3.520 to 10.673; P<0.01). ROC curve analysis result showed that the area under the curve of VEGF, MMP-9 combined S100B in predicting adverse pregnancy outcome in pregnant women with gestational diabetes was significantly larger than that of VEGF, MMP-9 and S100B alone (0.945 vs. 0.863, 0.847 and 0.801; P<0.05 or <0.01), with sensitivity of 89.80% and specificity of 91.30%. Conclusions:The high serum levels of VEGF, MMP-9 and S100B are associated with abnormal glycolipid metabolism and adverse pregnancy outcome in pregnant women with gestational diabetes, and the combination of the three indexes has a high predictive value for adverse pregnancy outcome.

Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

Objective To analyze the multi-target mechanism of Paeoniflorin in the intervention of oral submucosal fibrosis(OSF)systematically,based on molecular dynamics simulation and network pharmacology.Methods Identify potential targets of Paeoniflorin were predicted by using database.OSF-related disease targets and identified drug-disease intersecting targets were screened.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis were conducted to validate the molecular binding capabilities between Paeoniflorin and core targets.Finally,molecular dynamics simulations were performed to verify binding stability.Results A total of 20 overlapping targets were identified,including key genes such as transforming growth factor(TGF)-β1,interleukin(IL)-6,and tumor necrosis factor(TNF)-α.TGF-β1,IL-6,and TNF formed the core hub.The enrichment analysis revealed that the target molecules were significantly enriched in the TGF-β1,phosphatidylinositol 3-kinease-actin(PI3K-Akt),and nuclear factor κB(NF-κB)signaling pathways.Molecular docking confirmed high affinity binding of Paeoniflorin to targets including TGF-β1,while molecular dynamics simulations verified stable interactions between Paeoniflorin and both TGF-β1 and B-cell lymphoma-2 targets.Conclusion This study revealed that Paeoniflorin inhibits the inflammatory-fibrotic cascade of OSF through synergistic regulation of TGF-β1/Smad,PI3K-Akt and NF-κB pathways.

OBJECTIVE To explore the mechanism by which Gongfa Static Training regulates mitophagy through the PTEN-in-duced kinase 1(PINK1)/Parkin pathway in skeletal muscle insulin resistance in type 2 diabetes mellitus(T2DM).METHODS T2DM mouse model was established using a high-fat diet combined with streptozotocin(STZ)intraperitoneal injection.Mice were ran-domly divided into a model group,a metformin group,an aerobic exercise group,and a Gongfa Static Training group.The intervention effects of Gongfa Static Training were evaluated by measuring fasting blood glucose,Homeostatic Model Assessment of Insulin Resist-ance(HOMA-IR),glycated hemoglobin(HbA1c),lipid metabolism indicators,mitochondrial function in the gastrocnemius muscle,and the expression of PINK1 and Parkin-related genes and proteins.RESULTS Gongfa Static Training significantly reduced fasting blood glucose,HbA1c,and insulin resistance index in T2DM mice,improved lipid metabolism,and enhanced insulin sensitivity.It improved the structure and function of mitochondria in the gastrocnemius muscle by upregulating the mRNA and protein expression of PINK1 and Parkin.CONCLUSION Gongfa Static Training improves mitochondrial function and insulin resistance in the skeletal muscle of T2DM mice by regulating the PINK1/Parkin pathway.

OBJECTIVE To construct ensemble learning models for predicting high-use days of budesonide based on meteorological factors， thereby providing reference for hospital pharmacy management. METHODS Meteorological data for 2024 and outpatient budesonide usage data from the jurisdiction of Sanming Hospital of Integrated Traditional Chinese and Western Medicine were collected. High-use days were defined as the 75th percentile of outpatient budesonide usage， and a corresponding dataset was established. The prediction task was formulated as a classification problem， and three ensemble learning models were developed： Random Forest， Extreme Gradient Boosting （XGBoost）， and Histogram-based Gradient Boosting Classifier. Model performance was evaluated using accuracy， precision， recall， F1-score， and log-loss. Model interpretability was analyzed using Shapley Additive Explanations （SHAP）. RESULTS The Histogram-based Gradient Boosting Classifier achieved the best performance （accuracy＝0.75， F1-score＝0.48）， followed by XGBoost （accuracy＝0.74， F1-score＝0.43） and Random Forest （accuracy＝0.72， F1-score＝0.22）. SHAP results suggested that the prediction results of the last two models have the highest correction. CONCLUSIONS Ensemble learning models can effectively predict high-use days of budesonide， with the Histogram- based Gradient Boosting Classifier demonstrating the best predictive performance. Low temperature， high humidity， and low atmospheric pressure show significant positive impacts on the prediction of daily budesonide usage.

Objective To analyze the multi-target mechanism of Paeoniflorin in the intervention of oral submucosal fibrosis(OSF)systematically,based on molecular dynamics simulation and network pharmacology.Methods Identify potential targets of Paeoniflorin were predicted by using database.OSF-related disease targets and identified drug-disease intersecting targets were screened.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis were conducted to validate the molecular binding capabilities between Paeoniflorin and core targets.Finally,molecular dynamics simulations were performed to verify binding stability.Results A total of 20 overlapping targets were identified,including key genes such as transforming growth factor(TGF)-β1,interleukin(IL)-6,and tumor necrosis factor(TNF)-α.TGF-β1,IL-6,and TNF formed the core hub.The enrichment analysis revealed that the target molecules were significantly enriched in the TGF-β1,phosphatidylinositol 3-kinease-actin(PI3K-Akt),and nuclear factor κB(NF-κB)signaling pathways.Molecular docking confirmed high affinity binding of Paeoniflorin to targets including TGF-β1,while molecular dynamics simulations verified stable interactions between Paeoniflorin and both TGF-β1 and B-cell lymphoma-2 targets.Conclusion This study revealed that Paeoniflorin inhibits the inflammatory-fibrotic cascade of OSF through synergistic regulation of TGF-β1/Smad,PI3K-Akt and NF-κB pathways.

Objective:To investigate the relationship between the serum levels of vascular endothelial growth factor (VEGF), matrix metalloproteinase-9 (MMP-9), S100 calcium binding protein B (S100B) with glycolipid metabolism, pregnancy outcome in pregnant women with gestational diabetes.Methods:The clinical data of 153 pregnant women with gestational diabetes (research group) and 153 healthy pregnant women (control group) in the Second Affiliated Hospital of Xi ′an Medical University from January 2020 to October 2023 were retrospectively analyzed. The serum levels of VEGF, MMP-9 and S100B were measured by enzyme linked immunosorbent assay, and the fasting blood glucose, triglyceride, total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), fasting insulin and glycated hemoglobin were measured, and the homeostasis model assessment insulin resistance index (HOMA-IR) was calculated. The adverse outcomes of pregnant women with gestational diabetes were recorded. Pearson method was used to analyze the correlation between glycolipid metabolism indexes and VEGF, MMP-9, S100B in pregnant women with gestational diabetes. Multivariate Logistic regression was used to analyze the independent risk factors of adverse pregnancy outcome in pregnant women with gestational diabetes. Receiver operating characteristic (ROC) curve was drawn to analyze the predictive value of VEGF, MMP-9 and S100B on adverse pregnancy outcome in pregnant women with gestational diabetes. Results:The fasting blood glucose, fasting insulin, glycated hemoglobin, HOMA-IR, triglyceride, total cholesterol, LDL-C, VEGF, MMP-9 and S100B in research group were significantly higher than those in control group: (9.42 ± 0.65) mmol/L vs. (4.13 ± 0.46) mmol/L, (16.58 ± 2.37) mU/L vs. (13.41 ± 2.05) mU/L, (7.28 ± 0.46)% vs. (4.35 ± 0.39)%, 4.83 ± 0.42 vs. 2.71 ± 0.37, (3.41 ± 0.67) mmol/L vs. (2.85 ± 0.63) mmol/L, (5.54 ± 1.56) mmol/L vs. (5.12 ± 1.50) mmol/L, (3.14 ± 0.97) mmol/L vs. (2.86 ± 0.93) mmol/L, (184.02 ± 30.25) ng/L vs. (156.33 ± 26.41) ng/L, (45.78 ± 7.56) μg/L vs. (29.36 ± 5.03) μg/L and (117.51 ± 25.12) ng/L vs. (89.74 ± 22.46) ng/L, the HDL-C was significantly lower than that in control group: (1.34 ± 0.27) mmol/L vs. (1.42 ± 0.30) mmol/L, and there were statistical differences ( P<0.01 or <0.05). Pearson correlation analysis result showed that VEGF, MMP-9, S100B in pregnant women with gestational diabetes were positively correlated with fasting blood glucose, fasting insulin, glycated hemoglobin, HOMA-IR, triglyceride, total cholesterol and LDL-C ( P<0.01), negatively correlated with HDL-C ( P<0.01). Among 153 pregnant women with gestational diabetes, 49 had adverse pregnancy outcome, and 104 had good pregnancy outcome. The VEGF, MMP-9 and S100B in pregnant women with adverse pregnancy outcome were significantly higher than those in pregnant women with good pregnancy outcome: (212.75 ± 28.63) ng/L vs. (170.49 ± 26.58) ng/L, (52.37 ± 7.14) μg/L vs. (42.68 ± 6.35) μg/L and (136.83 ± 23.62) ng/L vs. (108.41 ± 21.35) ng/L, and there were statistical differences ( P<0.01). Multivariate Logistic regression analysis result showed that VEGF, MMP-9 and S100B were independent risk factors for adverse pregnancy outcome in pregnant women with gestational diabetes ( OR = 7.013, 5.382 and 6.129; 95% CI 5.206 to 9.447, 3.449 to 8.398 and 3.520 to 10.673; P<0.01). ROC curve analysis result showed that the area under the curve of VEGF, MMP-9 combined S100B in predicting adverse pregnancy outcome in pregnant women with gestational diabetes was significantly larger than that of VEGF, MMP-9 and S100B alone (0.945 vs. 0.863, 0.847 and 0.801; P<0.05 or <0.01), with sensitivity of 89.80% and specificity of 91.30%. Conclusions:The high serum levels of VEGF, MMP-9 and S100B are associated with abnormal glycolipid metabolism and adverse pregnancy outcome in pregnant women with gestational diabetes, and the combination of the three indexes has a high predictive value for adverse pregnancy outcome.

Objective To understand the willingness to engage in the combine medical care with old-age care institutions of medical students in vocational colleges and analyze their influencing factors.Methods By convenient sampling method,1 266 medical students from 3 vocational colleges in Hunan Province were select-ed as the research objects,and their occupational cognition and occupational willingness were investigated and analyzed.Through objective sampling,20 medical students who were not willing to work in medical institu-tions were selected for semi-structured interviews.Results 62.1%of medical students intend to work in med-ical institutions after graduation;The results of multi-factor analysis showed that the experience of caring for the elderly,understanding of the nursing institution,participating in the volunteer service of the nursing insti-tution,and cognition score of the combination of medical care and nursing were the influencing factors of med-ical students'career intention(P＜0.05).The qualitative interview found that professional identity,job abili-ty,salary,social security and promotion were the main influencing factors.Conclusion Medical students in medical nursing institutions do not have strong willingness,so they should improve their professional cogni-tion of old-age service,enhance their professional identity and post competency,fully implement salary,social security and professional title promotion,attract more medical students to engage in the combined service in-dustry,and promote the combined development of medical care.