ObjectiveTo investigate the protective effects and mechanisms of Bushen Zhuyun prescription （BSZY） on abortion rats with kidney deficiency-corpus luteum inhibition syndrome. MethodsAn abortion rat model with kidney deficiency-corpus luteum inhibition syndrome was constructed. Pregnant mice aged 8-10 weeks were randomly divided into a control group （Control）， a model group （Model）， low-dose BSZY （BSZY-L）， medium-dose BSZY （BSZY-M）， and high-dose BSZY （BSZY-H） groups （2.57， 5.14， 10.28 g·kg^-¹）， and a Zishen Yutai Pill （ZSYT） group （1.575 g·kg^-¹）. Hematoxylin-eosin （HE） staining was used to evaluate histopathological changes in ovarian and decidual tissue of rats in each group. Enzyme-linked immunosorbent assay （ELISA） was employed to measure levels of estrogen （E₂）， progesterone （P）， luteinizing hormone （LH）， prolactin （PRL）， and follicle-stimulating hormone （FSH） in serum. The candidate targets of BSZY were obtained from the Traditional Chinese Medicine System Pharmacology Platform （TCMSP） and Integrative Pharmacology-based Research Platform of Traditional Chinese Medicine （TCMIP） v2.0 databases， while disease targets for recurrent spontaneous abortion （RSA） were retrieved from GeneCards， DrugBank， Online Mendelian Inheritance in Man （OMIM）， and Therapeutic Target Database （TTD）. The intersection targets were identified by the Venny 2.1.0 platform. Pathway enrichment analysis was conducted based on the Metascape database to predict the potential mechanisms of BSZY. Additionally. Western blot was used to verify the effects of BSZY on the expression of estrogen receptor （ERα）， phosphatidylinositol 3-kinase （PI3K）， and protein kinase B （Akt） and explore its protective mechanism on RSA rats. ResultsCompared with the control group， the model group exhibited significantly decreased uterine， ovarian， and embryonic wet weights （P<0.05， P<0.01）， with an abortion rate of 57.18%. The ovarian tissue showed varying degrees of reduction in primordial follicles， primary follicles， mature follicles， and corpora lutea， along with a large number of atretic follicles. The endometrium was thinner， and decidual tissue exhibited cellular edema and disorganized arrangement. In contrast， compared with the model group， the BSZY groups at all doses and the ZSYT group demonstrated increased uterine， ovarian， and embryonic wet weights， along with a reduced abortion rate. The number of primordial follicles， primary follicles， mature follicles， and corpora lutea increased， while atretic follicles decreased. The endometrium thickened， and decidual tissue displayed normal cellular structure with tight arrangement. Additionally， the model group showed significantly decreased levels of E₂， P， PRL， and FSH in serum （P<0.05， P<0.01）， along with a decreasing trend in LH level. In contrast， the BSZY groups at all doses exhibited significantly elevated levels of E₂， P， LH， PRL， and FSH in serum （P<0.05， P<0.01）. Network pharmacology predictions suggested that BSZY may exert protective effects against abortion in rats by activating the ERα/PI3K/Akt signaling pathway. Western blot results confirmed that BSZY significantly upregulated the expression of ERα， PI3K， and p-Akt proteins （P<0.05， P<0.01）. ConclusionBSZY has a protective effect on the abortion rats with kidney deficiency-corpus luteum inhibition syndrome， possibly by activating the ERα/PI3K/Akt signaling pathway to reduce ovarian apoptosis and regulate endocrine function， thereby lowering the abortion rate.

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

ObjectiveThis study aimed to investigate the intervention effect of Renqing Mangjue on the malignant transformation of gastric mucosal epithelial cells induced by N-methyl-N′-nitro-N-nitrosoguanidine （MNNG） and to explore its molecular mechanism in preventing precancerous lesions of gastric cancer based on the cyclic guanosine monophosphate （cGMP）/protein kinase G （PKG）/mitogen-activated protein kinase （MEK）/extracellular signal-regulated kinase （ERK） signaling pathway. MethodsHuman gastric mucosal epithelial cells （GES-1） were initially induced by MNNG to establish a precancerous cell model （MC cells）. The effective concentration of MNNG for inducing malignant transformation in GES-1 cells was screened using the cell proliferation activity decection （CCK-8） assay， and the effective concentration of Renqing Mangjue for inhibiting the proliferation of transformed GES-1 cells was also determined. GES-1 cells were divided into a blank control group， a model group， and treatment groups with Renqing Mangjue at concentrations of 1， 3， 10， and 30 mg·L^-1. Furthermore， the effects of Renqing Mangjue on the migratory ability and epithelial-mesenchymal transition （EMT） characteristics of GES-1 malignant transformed cells were evaluated using Transwell migration assays， wound healing assays， and real-time quantitative reverse transcription polymerase chain reaction （Real-time PCR）. Additionally， candidate chemical components and target sites of Renqing Mangjue were obtained from the TCMIP v2.0 database， and disease targets at various stages of gastric cancer precursors were sourced from the Gene Expression Omnibus （GEO） database. Pathway enrichment analysis was performed using the Metascape database to predict the potential mechanisms of action of Renqing Mangjue. Finally， the protective mechanism of Renqing Mangjue against gastric cancer precursors was validated through Western blot analysis. ResultsAt a concentration of 20 μmol·L^-1， MNNG exhibited an inhibition rate of approximately 50% on GES-1 cells （P<0.01）， and at this concentration， the GES-1 cells displayed biological characteristics indicative of malignant transformation. In contrast， Renqing Mangjue had no significant effect on the proliferation of normal GES-1 cells， but significantly inhibited the proliferation of MC cells （P<0.01） and markedly reduced their migratory capacity （P<0.01）. Moreover， it also increased the mRNA expression level of E-cadherin during the EMT process （P<0.05）， while inhibiting the expression of both N-cadherin and the transcription factor Snail mRNA （P<0.05， P<0.01）. Network predictions suggested that Renqing Mangjue may prevent gastric cancer precursors through modulating the cGMP/PKG and MAPK/ERK signaling pathways. Furthermore， Western blot results indicated that Renqing Mangjue upregulated the expression of PKG and NPRB （B-type natriuretic peptide receptor） proteins in the cGMP/PKG pathway （P<0.01）， while downregulating the expression of the downstream proteins MEK and ERK （P<0.05， P<0.01）. ConclusionIn summary， Renqing Mangjue can prevent gastric cancer precursors by inhibiting the proliferation and migration of malignant transformed GES-1 cells， thereby delaying the EMT process. The underlying mechanisms may be related to the activation of the cGMP/PKG pathway and the inhibition of the MEK/ERK signaling pathway.

Objective:To investigate the clinical and genetic characteristics of a Henan Han family with pontine autosomal dominant microangiopathy and leukoencephalopathy (PADMAL), aiming to enhance understanding of this disease.Methods:The proband was first admitted to the Department of Neurology, Henan Provincial People′s Hospital, Fuwai Central China Cardiovascular Hospital in December 2019 due to cerebral infarction and unilateral limb numbness and weakness. Detailed medical history collection, pedigree mapping, whole-exome sequencing screening, and Sanger sequencing validation were performed for the proband and family members. The patients′ clinical manifestations, imaging features, neuropsychological scale assessment results, and pathological changes were summarized, and genetic analysis was conducted on the gene variant site. Relevant literature was reviewed to summarize the characteristics of PADMAL.Results:The proband was a 47-year-old female, with 3 generations of family members affected, including 7 patients, 3 of whom had died. The clinical features of the patients were similar, with the first stroke occurring around the age of 40, without vascular risk factors such as hypertension or diabetes. The main clinical manifestation was unilateral limb numbness and weakness. The proband and her niece sought medical attention due to stroke symptoms. Brain magnetic resonance imaging revealed acute infarct lesions located in the pons, accompanied by multiple oval infarct foci (the "raisin bread sign") and white matter hyperintensity changes. Genetic testing showed that 4 patients carried a heterozygous c. *34GT mutation in the 3′-untranslated region (3′-UTR) of the COL4A1 gene, while the other 4 unaffected family members did not carry this variant, consistent with genotype- phenotype co-segregation in the family. Conclusions:PADMAL is an extremely rare monogenic cerebral small vessel disease caused by pathogenic variants in the 3′-UTR of the COL4A1 gene. The "raisin bread sign" in the pons is a relatively specific imaging feature that distinguishes it from other cerebral small vessel diseases. For patients with this sign, genetic testing for PADMAL should be considered.

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.

The diagnosis of hematological disorders is currently established from the combined results of different tests, including those assessing morphology (M), immunophenotype (I), cytogenetics (C), and molecular biology (M) (collectively known as the MICM classification). In this workflow, most of the results are interpreted manually (i.e., by a human, without automation), which is expertise-dependent, labor-intensive, time-consuming, and with inherent interobserver variability. Also, with advances in instruments and technologies, the data is gaining higher dimensionality and throughput, making additional challenges for manual analysis. Recently, artificial intelligence (AI) has emerged as a promising tool in clinical hematology to ensure timely diagnosis, precise risk stratification, and treatment success. In this review, we summarize the current advances, limitations, and challenges of AI models and raise potential strategies for improving their performance in each sector of the MICM pipeline. Finally, we share perspectives, highlight future directions, and call for extensive interdisciplinary cooperation to perfect AI with wise human-level strategies and promote its integration into the clinical workflow.

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.

Clinical research on rare diseases has always faced multiple challenges in clinical research design and implementation due to small sample sizes of patients,high heterogeneity,and limited research resources.The rapid development of digital intelligence technology has provided innovative solutions for rare disease research.This article systematically explores the current status and response strategies of clinical research on rare diseases in the digital intelligence age.On the one hand,the efficiency of rare disease research has been optimized through adaptive design,mixed trial mode,and precision medicine stratification methods.On the other hand,solutions based on digital technology have been proposed to address the practical challenges of recruitment difficulties and underrepresentation of rare disease clinical research patients,data management and technical barriers,and insufficient coverage of natural medical history and baseline databases through digital intelligence technology.By combining international collaboration,intelligent screening,and remote experiments,a multidisciplinary collaboration and international cooperation,adaptive design,digital data platform,and patient-centered remote research model have been constructed as the core implementation strategies.Typical cases demonstrate that digital intelligence technology not only effectively shortens the drug development cycle,but also significantly enhances patient benefits,providing a replicable practical paradigm for global rare disease research.The practice of digital platforms represented by the International Rare Disease Research Alliance and the China Rare Disease Diagnosis and Treatment Collaboration Network has further verified the feasibility and promotional value of the digitalization path.In summary,digital intelligence technology has shown considerable promise in overcoming the clinical research challenges of rare diseases and accelerating the development of treatment plans,providing systematic references for researchers,regulatory agencies,and patient organizations.It is expected to drive the clinical research of rare diseases towards a more efficient and accurate future.

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.