ObjectiveTo investigate somatization symptoms in adolescents during frequent earthquakes in Hefei， and to explore their correlation with earthquake experiences. MethodsA cross-sectional survey was used to select 324 adolescents in Hefei as the survey objects. The self-rating scale of somatization symptoms （SSS） and the fatigue intensity scale （FIS） were used to evaluate the somatization symptoms and fatigue degree of middle school students， and multivariate Logistic regression analysis was used to explore the related factors of somatization symptoms and fatigue among middle school students. ResultsA total of 324 adolescents were included， and the overall detection rate of somatization symptoms was 6.5%， and the detection rate of moderate or above fatigue was 20.1%. The results of regression analysis showed that adolescents who were concerned about the earthquake for a longer time （≥1 h） had a higher risk of somatization symptoms （OR=5.430， 95%CI： 1.547-19.058）， and adolescents who received pre-earthquake training had a lower degree of fatigue （OR=0.535， 95%CI： 0.292-0.981） （P<0.05）. ConclusionDuring the frequent earthquakes， adolescents have more somatization symptoms and fatigue. Therefore， it is crucial to enhance health education， reduce the emphasis on event-related reports， and implement earthquake prevention and disaster reduction training to improve the physical and mental health of adolescents.

OBJECTIVE To investigate the improving effect and mechanism of Wenyang huayu formula on cerebral ischemia-reperfusion injury in rats based on nuclear factor-erythroid 2-related factor 2 （Nrf2）/glutathione peroxidase 4 （GPX4） signaling pathway and mitochondrial ferroptosis pathway. METHODS SD rats were randomly divided into sham operation group， model group，Nimodipine tablet group （10.8 mg/kg ）， and Wenyang huayu formula group （28 g/kg）， with 24 rats in each group. Except for the sham operation group， rats in other groups were all subjected to middle cerebral artery occlusion model by Longa thread occlusion method. After successful modeling， rats in each administration group were intragastrically gavaged with corresponding liquid for 7 days or 14 days， while rats in sham operation group and model group were given equal volume of normal saline once a day. At 7 and 14 days after administration， neurological deficit scores of rats were calculated； the ultrastructure of neuronal mitochondria in ischemic brain tissue of rats was observed；the contents of malondialdehyde （MDA）， glutathione （GSH） and Fe 2+ ， as well as the protein and mRNA expression levels of Nrf2， solute carrier family 7 member 11 （SLC7A11） and GPX4 in ischemic brain tissue of rats were detected. RESULTS At 7 and 14 days after administration， compared with the sham operation group， the neuronal mitochondria in ischemic brain tissue of rats in the model group showed typical changes of ferroptosis， and the injury continued to worsen over time； the neurological deficit scores， the contents of MDA and Fe 2+ were significantly increased （ P ＜0.05），while the content of GSH and the protein and mRNA expression levels of Nrf2， SLC7A11 and GPX4 were significantly decreased （ P ＜0.05）. Compared with the model group， the morphology of neuronal mitochondria in ischemic brain tissue of rats in Nimodipine tablet group and Wenyang huayu formula group was gradually improved over time， and the above quantitative indicators were significantly reversed （ P ＜0.05）；moreover， the improvement effect of most indicators in Wenyang huayu formula group was significantly better than that in Nimodipine tablet group （ P ＜0.05）. CONCLUSIONS Wenyang huayu formula can improve cerebral ischemia-reperfusion injury in rats， and its mechanism may be related to activating Nrf2/GPX4 signaling pathway and inhibiting mitochondrial ferroptosis.

OBJECTIVE To explore the mechanism of the active ingredient compatibility（quercetin， quercitrin and kaempferol at a mass ratio of 2∶9∶3）of Dimocarpus longan Lour. leaves（abbreviated as CDL） on ameliorating glucose and lipid metabolism disorders in type 2 diabetes mellitus （T2DM） rats. METHODS SD rats were randomly divided into blank control group， model group， metformin hydrochloride group （100 mg/kg）， and CDL high-， medium- and low-dose groups （280， 140， 75 mg/kg）， with 10 rats in each group. Rats in the blank control group were fed with standard chow， while rats in the other groups were given high-sugar and high-fat diet combined with intraperitoneal injection of streptozotocin to establish the T2DM rat model. After successful modeling， rats in each administration group were given corresponding drug solution， and rats in the blank control group and model group were intragastrically administered with equal volume of pure water， once a day， for consecutive 4 weeks. Fasting blood glucose （FBG） was detected at fixed time every week. The curves of oral glucose tolerance test （OGTT） and intraperitoneal insulin tolerance test （IPITT） were plotted， and the area under curve （AUC） was calculated. The pancreatic islet function indexes [fasting insulin （FINS）， homeostasis model assessment of insulin resistance （HOMA-IR）， insulin sensitivity index （ISI）]，blood lipid indexes [total cholesterol （TC）， triglyceride （TG）， low-density lipoprotein cholesterol （LDL-C）， high-density lipoprotein cholesterol （HDL-C）] and hepatic glycogen content were determined. The pathological morphological changes of liver and pancreatic tissues were observed. The protein and mRNA expression levels of molecules related to phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt） signaling pathway in liver tissues were detected. RESULTS Compared with the blank control group， the FBG， AUC of IPITT curve， AUC of OGTT curve， HOMA-IR， the levels of FINS， TC， TG and LDL-C， as well as the protein and mRNA expression of phosphatase and tensin homolog， forkhead box protein O1 and glycogen synthase kinase-3β in liver tissues were significantly increased in the model group （ P ＜0.05）. ISI， the levels of HDL-C and hepatic glycogen content， along with the protein and mRNA expression of PI3K， insulin receptor substrate-1， Akt and protein expression of phosphorylated Akt in liver tissues were markedly decreased （ P ＜0.05）. In model group rats， the arrangement of hepatocytes was irregular， the overall structure of pancreatic lobules was disordered， and a large number of inflammatory cell infiltration was observed. Compared with the model group， most of the above quantitative indexes were significantly reversed in the CDL high-dose group （ P ＜0.05）， and the pathological lesions of liver and pancreas were obviously alleviated. CONCLUSIONS CDL can regulate glucose and lipid metabolism disorders， elevate insulin sensitivity and relieve insulin resistance in T2DM rats. Its mechanism may be related to the activation of the PI3K/Akt signaling pathway.

γδ T cells possess both innate and adaptive immune characteristics. Their antigen recognition is independent of major histocompatibility complex presentation， and they can directly recognize abnormal tumor metabolism， showing unique application potential in tumors with low immune infiltration. This paper systematically sorts out the biological characteristics and antitumor immune functions of γδ T cells， and mainly reviews the relevant mechanisms by which active ingredients of traditional Chinese medicine regulate γδ T cells to exert antitumor immune functions. Glycyrrhiza polysaccharide and Astragalus polysaccharide can enhance the antitumor immune function of γδ T cells by promoting the expression of various cytokines， regulating intestinal flora and alleviating immunosuppressive state. Quercetin and puerarin can strengthen γδ T cell-mediated tumor cell killing by regulating the proliferation of γδ T cells， the expression of cytotoxic effector molecules and related signaling pathways. Toosendanin can potentiate γδ T cell-induced tumor cell killing by down-regulating the expression of anti-apoptotic protein in tumor cells. Artesunate can boost γδ T cell-mediated antitumor immune responses by enhancing the effector function of γδ T cells and weakening tumor immune escape. Future research shall focus on exploring the synergistic effects and molecular mechanisms of the combined application of active ingredients of traditional Chinese medicine and γδ T cell immunotherapy， and identifying the key targets for γδ T cells to exert antitumor immune functions.

OBJECTIVE To explore the mechanism of the active ingredient compatibility（quercetin， quercitrin and kaempferol at a mass ratio of 2∶9∶3）of Dimocarpus longan Lour. leaves（abbreviated as CDL） on ameliorating glucose and lipid metabolism disorders in type 2 diabetes mellitus （T2DM） rats. METHODS SD rats were randomly divided into blank control group， model group， metformin hydrochloride group （100 mg/kg）， and CDL high-， medium- and low-dose groups （280， 140， 75 mg/kg）， with 10 rats in each group. Rats in the blank control group were fed with standard chow， while rats in the other groups were given high-sugar and high-fat diet combined with intraperitoneal injection of streptozotocin to establish the T2DM rat model. After successful modeling， rats in each administration group were given corresponding drug solution， and rats in the blank control group and model group were intragastrically administered with equal volume of pure water， once a day， for consecutive 4 weeks. Fasting blood glucose （FBG） was detected at fixed time every week. The curves of oral glucose tolerance test （OGTT） and intraperitoneal insulin tolerance test （IPITT） were plotted， and the area under curve （AUC） was calculated. The pancreatic islet function indexes [fasting insulin （FINS）， homeostasis model assessment of insulin resistance （HOMA-IR）， insulin sensitivity index （ISI）]，blood lipid indexes [total cholesterol （TC）， triglyceride （TG）， low-density lipoprotein cholesterol （LDL-C）， high-density lipoprotein cholesterol （HDL-C）] and hepatic glycogen content were determined. The pathological morphological changes of liver and pancreatic tissues were observed. The protein and mRNA expression levels of molecules related to phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt） signaling pathway in liver tissues were detected. RESULTS Compared with the blank control group， the FBG， AUC of IPITT curve， AUC of OGTT curve， HOMA-IR， the levels of FINS， TC， TG and LDL-C， as well as the protein and mRNA expression of phosphatase and tensin homolog， forkhead box protein O1 and glycogen synthase kinase-3β in liver tissues were significantly increased in the model group （ P ＜0.05）. ISI， the levels of HDL-C and hepatic glycogen content， along with the protein and mRNA expression of PI3K， insulin receptor substrate-1， Akt and protein expression of phosphorylated Akt in liver tissues were markedly decreased （ P ＜0.05）. In model group rats， the arrangement of hepatocytes was irregular， the overall structure of pancreatic lobules was disordered， and a large number of inflammatory cell infiltration was observed. Compared with the model group， most of the above quantitative indexes were significantly reversed in the CDL high-dose group （ P ＜0.05）， and the pathological lesions of liver and pancreas were obviously alleviated. CONCLUSIONS CDL can regulate glucose and lipid metabolism disorders， elevate insulin sensitivity and relieve insulin resistance in T2DM rats. Its mechanism may be related to the activation of the PI3K/Akt signaling pathway.

γδ T cells possess both innate and adaptive immune characteristics. Their antigen recognition is independent of major histocompatibility complex presentation， and they can directly recognize abnormal tumor metabolism， showing unique application potential in tumors with low immune infiltration. This paper systematically sorts out the biological characteristics and antitumor immune functions of γδ T cells， and mainly reviews the relevant mechanisms by which active ingredients of traditional Chinese medicine regulate γδ T cells to exert antitumor immune functions. Glycyrrhiza polysaccharide and Astragalus polysaccharide can enhance the antitumor immune function of γδ T cells by promoting the expression of various cytokines， regulating intestinal flora and alleviating immunosuppressive state. Quercetin and puerarin can strengthen γδ T cell-mediated tumor cell killing by regulating the proliferation of γδ T cells， the expression of cytotoxic effector molecules and related signaling pathways. Toosendanin can potentiate γδ T cell-induced tumor cell killing by down-regulating the expression of anti-apoptotic protein in tumor cells. Artesunate can boost γδ T cell-mediated antitumor immune responses by enhancing the effector function of γδ T cells and weakening tumor immune escape. Future research shall focus on exploring the synergistic effects and molecular mechanisms of the combined application of active ingredients of traditional Chinese medicine and γδ T cell immunotherapy， and identifying the key targets for γδ T cells to exert antitumor immune functions.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. 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, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Aim To investigate the effect of triptolide(TP)on corticosterone(CORT)-induced depression-like behaviors in mice and explore the antidepressant mechanism of TP based on the CREB/BDNF/TrkB sig-naling pathway.Methods Sixty 8-week-old male C57BL/6J mice were randomly divided into five groups:control group,CORT group,TP groups of low and high doses(10,30 μg·kg-1),and fluoxetine(FLU)group(10 mg·kg-1).Except for the control group,the other groups received subcutaneous injec-tions of CORT for three consecutive weeks to establish the model of depression.During the last two weeks of modeling,normal saline,TP and FLU were adminis-tered via intraperitoneal injection respectively.After the administration,depression-like behaviors in mice were assessed using forced swimming test,tail suspen-sion test,and sucrose preference test.Biochemical methods were used to measure the levels of SOD and MDA in the hippocampus and prefrontal cortex(PFC).Cell apoptosis was detected by TUNEL meth-od.Immunohistochemistry,immunofluorescence,and Western blotting were employed to detect the expres-sion of apoptosis/autophagy-related proteins,synaptic structure markers,and proteins related to the CREB/BDNF/TrkB signaling pathway.Results TP signifi-cantly ameliorated CORT-induced depression-like be-haviors in mice,mainly manifested by reduced immo-bility time in the tail suspension test and forced swim-ming test,and increased sucrose preference rate.TP alleviated CORT-induced oxidative stress by increasing SOD levels and reducing MDA production in brain tis-sue.Additionally,TP also inhibited apoptosis and ex-cessive autophagy of neurons in the hippocampus and prefrontal cortex,maintained synaptic plasticity,and significantly upregulated the expression of p-CREB,BDNF,and TrkB.Conclusions TP exhibits potential antidepressant effect in mice by upregulating the CREB/BDNF/TrkB signaling pathway,reducing oxida-tive stress,inhibiting excessive neuronal apoptosis and autophagy,and improving synaptic plasticity.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. 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, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective To analyze the risk factors and effective predictive indicators for postpartum hemor-rhage(PPH)in pregnant women with severe pre-eclampsia(sPE)in singleton pregnancies.The findings will serve as a valuable reference for the clinical prevention and management of PPH in these patients.Methods A retrospective analysis was conducted on 932 pregnant women with sPE at two tertiary hospitals in Guangzhou from January 1,2016,to December 31,2022.Among these,95 cases were complicated by PPH.A comparative analysis was performed between the sPE group and the sPE with PPH group.Results(1)The incidence of assisted reproductive technology,intrapartum blood loss,placental abruption,elevated D-dimer levels,increased monocyte counts,and higher SIRI levels were significantly higher in the PPH group,whereas platelet counts were significantly lower(P<0.05).(2)The results indicated that intrapartum blood loss,D-dimer levels,and platelet counts were inde-pendently associated with PPH in pregnant women with sPE.(3)The area under the curve(AUC)for intrapartum blood loss,D-dimer,and platelet counts were 0.805,0.717,and 0.571,respectively.The optimal cutoff value for D-dimer was determined to be 2.295 μg/mL.The combined AUC for intrapartum blood loss and D-dimer was 0.859.(4)Intrapartum blood loss values were significantly higher in the PPH group for both vaginal delivery and cesarean section(P<0.001).The corresponding optimal cutoff values were 285 mL and 375 mL,respectively.Conclusions Intrapartum haemorrhage,D-dimer levels,and platelet count were identified as independent risk factors for PPH in pregnant women with sPE.Specifically,pregnant women with sPE who experienced blood loss exceeding 285 mL during vaginal delivery or 375 mL during caesarean section,along with a D-dimer level greater than 2.295 μg/mL,demonstrated an increased likelihood of developing PPH.Therefore,it is crucial to enhance clinical monitoring of these relevant indicators in high-risk populations.