OBJECTIVE: To compare the protective effects of electroacupuncture (EA) and transcutaneous electrical acupoint stimulation (TEAS) during pregnancy on maternal immune activation (MIA)-induced adverse pregnancy outcomes, fetal developmental defects, and neuropsychological behavior abnormalities in offspring mice. METHODS: Eighty pregnant C57BL/6 mice were randomly divided into 5 groups: control, model, EA, TEAS, and sham-stimulation groups, 16 mice in each group. MIA models were replicated on the day 12.5 of pregnancy via tail intravenous injection with polyinosinic-polycytidylic acid. On the second day of modeling success, in the EA and TEAS groups, the interventions were delivered at bilateral "Zusanli" (ST36), with a frequency of 2 Hz, a current of 0.5 mA, and for 20 min each day in the pregnant mice; and the interventions lasted 6 days. Body mass and fertility indexes of pregnant mice, and the development indexes of offspring mice were recorded. Liquid phase suspension chip technology was used to detect the levels of cytokines and chemotactic factors in the serum of pregnant mice and and fetal brain of offspring mice. Flow cytometry was adopted to detect the proportion of the subgroups and subtypes of spleen T lymphocytes and macrophages in pregnant mice. Using the open field test, prepulse inhibition (PPI) test and Morris water maze, the spatial learning and memory were assessed in offspring mice. Immunofluorescence staining was used to detect microglial count in the medial prefrontal cortex (mPFC) in offspring mice. RESULTS: Compared with the control group, the model group showed a reduced body mass of pregnancy mice (P<0.01), smaller litter size and fewer live births (P<0.01, P<0.05), the increase in dead birth and the decrease in offspring survival rate (P<0.05, P<0.01). When compared with model group, in the EA group and the TEAS group, the body mass of pregnancy mice rose (P<0.05), litter size and live births increased (P<0.05, P<0.01), the dead birth was reduced and the offspring survival rate higher (P<0.05). In comparison with the control group, the model group showed the increase in the levels of monocyte chemotactic protein-1 (MCP-1), interleukin-6 (IL-6), γ-interferon (IFN-γ) in the serum of pregnant mice, and spleen M1 macrophage proportion (P<0.01, P<0.05), and the decrease in spleen M2 macrophages of pregnant mice (P<0.01); and the increase in MCP-1 and IL-6 in fetal brain of offspring mice (P<0.05). Compared with the model group, the EA group and the TEAS group showed the decrease in MCP-1, IL-6 and IFN-γ, and spleen M1 macrophage proportion (P<0.01, P<0.05), and the increase in spleen M2 macrophages of pregnant mice (P<0.01, P<0.05) ; and the decrease in MCP-1 and IL-6 in fetal brain of offspring mice (P<0.05). Compared with the control group, in the model group, the total movement distance, escape incubation were extended (P<0.05, P<0.01), the frequency of entering the central area and crossing the platform decreased, and the activity duration in central area was shortened (P<0.05, P<0.01), the average speed rose (P<0.05), PPI%, the percentage of target quadrant swimming time in the total time and that of target quadrant swimming distance in the total distance were reduced (P<0.05, P<0.01) in offspring mice. When compared with the model group, in the EA group and TEAS group, the total movement distance and escape incubation were shortened, the average speed was reduced (P<0.05), PPI% and the frequency of crossing the platform increased (P<0.05, P<0.01); the percentage of target quadrant swimming time in the total time and that of target quadrant swimming distance in the total distance rose (P<0.05, P<0.01) in the offspring mice. In the EA group, the frequency of entering the central area and the activity duration in central area were higher (P<0.05, P<0.01); and in the the TEAS group, the activity duration in central area were longer (P<0.05). When compared with the control group, in the model group, microglial count in mPFC was elevated in offspring mice (P<0.05). In comparison with the model group, the EA group and the TEAS group showed the decrease of microglial count in mPFC (P<0.05). CONCLUSION EA and TEAS at "Zusanli" (ST36) during pregnancy effectively improve in the pregnancy outcomes and fetal brain developmental abnormalities induced by infection, and attenuate neurodevelopmental defects and mental disorders of offspring mice through inhibiting inflammatory activation of microglia in mPFC.
Animals ; Female ; Pregnancy ; Electroacupuncture ; Acupuncture Points ; Mice ; Mice, Inbred C57BL ; Humans ; Male

Objective To analyze the long-term trend in the disease burden of congenital birth defects(CBDs)among neonates in China from 1990 to 2021,compare the trend with global patterns,and identify key subtypes along with their association with socioeconomic status to provide evidence for public health interventions.Methods Utilizing data from the Global Burden of Disease Study 2021(GBD 2021),we extracted indicators including disability-adjusted life years(DALYs),mortality,and prevalence for the neonatal period(＜28 days)in China,encompassing ten major CBD subtypes.Joinpoint regression analysis was employed to calculate annual percent changes and estimate annual percent changes(EAPC),with comparisons of subtype composition between 1990 and 2021.Nonlinear regression was used to assess the relationship between DALYs rates and the Socio-demographic Index(SDI).Results From 1990 to 2021,DALYs rates for neonatal CBDs declined significantly both globally and in China,with China's EAPC at-4.67％[95％CI:(—5.06,—4.28)],substantially exceeding the global average of-1.70％[95％CI:(—1.75,—1.64)].Congenital heart anomalies remained the primary burden,while neural tube defects and orofacial clefts in China showed notable reductions(EAPCs of-7.25％and-11.22％,respectively).However,DALYs rates for congenital musculoskeletal and limb anomalies exceeded global expected levels.A resurgence in the prevalence was observed post-2015,with higher burdens in males.DALYs rates exhibited a negative correlation with SDI.Conclusion China has achieved significant reductions in the neonatal CBDs burden,surpassing global trends;yet challenges persist in managing congenital heart anomalies and musculoskeletal defects.Future efforts should focus on enhancing early screening,surgical interventions,and regional equity to align with global health objectives.

Objective To develop and validate a machine learning-based predictive model to assess the in-hospital mortality risk of patients with myocardial infarction(MI)complicated by heart failure(HF)undergoing percutaneous coronary intervention(PCI).Methods This retrospective study analyzed MI patients with HF who underwent PCI at The First Affiliated Hospital of Xinjiang Medical University from January 2019 to January 2023.Patient data,including demographic characteristics,vital signs,laboratory test results,imaging parameters and medication use,were collected and randomly divided into a training set(70％)and a validation set(30％).The extreme gradient boosting(XGBoost)model was used to identify variables significantly associated with in-hospital mortality,and the Shapley additive explanations(SHAP)model was applied to assess feature importance.A predictive model was then constructed using univariate and multivariate Logistic regression analyses.Model performance was evaluated using receiver operating characteristic(ROC)curves,area under the curve(AUC)values,calibration curves,and decision curve analysis.Finally,a nomogram was developed for intuitive risk assessment.Results A total of 1 214 MI patients with HF were included in the study,with a median age of 64 years.The in-hospital mortality rate was 7.41％(90 deaths).XGBoost feature selection identified ten key predictive variables:age,myoglobin,albumin,fasting blood glucose,N-terminal pro-B-type natriuretic peptide(NT-proBNP),diabetes mellitus,creatinine,cystatin C,procalcitonin,and left ventricular ejection fraction.Based on these variables,a Logistic regression model was developed,with seven final predictors:age,diabetes mellitus,creatinine,fasting blood glucose,cystatin C,NT-proBNP,and albumin.The model demonstrated high predictive accuracy,with AUC value of 0.869(95％CI:0.84-0.89)in the training set and 0.827(95％CI:0.79-0.85)in the validation set.The calibration curve indicated that the predicted probabilities were consistent with the actual observed outcomes,and decision curve analysis showed that the model had a high net benefit across various decision thresholds.Conclusion This study developed a machine learning-based predictive model incorporating Logistic regression to assess the in-hospital mortality risk of MI patients with HF undergoing PCI.The model demonstrated high predictive performance and clinical utility.The nomogram derived from this model provides an intuitive tool for individualized risk assessment,aiding clinicians in the early identification of high-risk patients,optimizing intervention strategies,and improving patient outcomes.

Objective To develop and validate a machine learning-based predictive model to assess the in-hospital mortality risk of patients with myocardial infarction(MI)complicated by heart failure(HF)undergoing percutaneous coronary intervention(PCI).Methods This retrospective study analyzed MI patients with HF who underwent PCI at The First Affiliated Hospital of Xinjiang Medical University from January 2019 to January 2023.Patient data,including demographic characteristics,vital signs,laboratory test results,imaging parameters and medication use,were collected and randomly divided into a training set(70％)and a validation set(30％).The extreme gradient boosting(XGBoost)model was used to identify variables significantly associated with in-hospital mortality,and the Shapley additive explanations(SHAP)model was applied to assess feature importance.A predictive model was then constructed using univariate and multivariate Logistic regression analyses.Model performance was evaluated using receiver operating characteristic(ROC)curves,area under the curve(AUC)values,calibration curves,and decision curve analysis.Finally,a nomogram was developed for intuitive risk assessment.Results A total of 1 214 MI patients with HF were included in the study,with a median age of 64 years.The in-hospital mortality rate was 7.41％(90 deaths).XGBoost feature selection identified ten key predictive variables:age,myoglobin,albumin,fasting blood glucose,N-terminal pro-B-type natriuretic peptide(NT-proBNP),diabetes mellitus,creatinine,cystatin C,procalcitonin,and left ventricular ejection fraction.Based on these variables,a Logistic regression model was developed,with seven final predictors:age,diabetes mellitus,creatinine,fasting blood glucose,cystatin C,NT-proBNP,and albumin.The model demonstrated high predictive accuracy,with AUC value of 0.869(95％CI:0.84-0.89)in the training set and 0.827(95％CI:0.79-0.85)in the validation set.The calibration curve indicated that the predicted probabilities were consistent with the actual observed outcomes,and decision curve analysis showed that the model had a high net benefit across various decision thresholds.Conclusion This study developed a machine learning-based predictive model incorporating Logistic regression to assess the in-hospital mortality risk of MI patients with HF undergoing PCI.The model demonstrated high predictive performance and clinical utility.The nomogram derived from this model provides an intuitive tool for individualized risk assessment,aiding clinicians in the early identification of high-risk patients,optimizing intervention strategies,and improving patient outcomes.

Objective To analyze the long-term trend in the disease burden of congenital birth defects(CBDs)among neonates in China from 1990 to 2021,compare the trend with global patterns,and identify key subtypes along with their association with socioeconomic status to provide evidence for public health interventions.Methods Utilizing data from the Global Burden of Disease Study 2021(GBD 2021),we extracted indicators including disability-adjusted life years(DALYs),mortality,and prevalence for the neonatal period(＜28 days)in China,encompassing ten major CBD subtypes.Joinpoint regression analysis was employed to calculate annual percent changes and estimate annual percent changes(EAPC),with comparisons of subtype composition between 1990 and 2021.Nonlinear regression was used to assess the relationship between DALYs rates and the Socio-demographic Index(SDI).Results From 1990 to 2021,DALYs rates for neonatal CBDs declined significantly both globally and in China,with China's EAPC at-4.67％[95％CI:(—5.06,—4.28)],substantially exceeding the global average of-1.70％[95％CI:(—1.75,—1.64)].Congenital heart anomalies remained the primary burden,while neural tube defects and orofacial clefts in China showed notable reductions(EAPCs of-7.25％and-11.22％,respectively).However,DALYs rates for congenital musculoskeletal and limb anomalies exceeded global expected levels.A resurgence in the prevalence was observed post-2015,with higher burdens in males.DALYs rates exhibited a negative correlation with SDI.Conclusion China has achieved significant reductions in the neonatal CBDs burden,surpassing global trends;yet challenges persist in managing congenital heart anomalies and musculoskeletal defects.Future efforts should focus on enhancing early screening,surgical interventions,and regional equity to align with global health objectives.