Objective To analyze the prevalence characteristics and influencing factors of metabolic syndrome (MS) in people aged 35-75 years in Hubei Province. Methods The follow-up data from 2016 to 2022 in the early screening and comprehensive intervention project for high-risk cardiovascular population in Hubei Province were collected. SAS 9.4 software was used to conduct 2-test and multivariate logistic regression to analyze the prevalence of MS and its influencing factors. Results Among the 89 199 subjects, 24 757 were affected by MS, with a prevalence rate of 27.75% and a standardized rate of 23.55%. Among the various components of MS, the prevalence of abnormal blood pressure was the highest, at 70.88%, and the standardized rate was 59.32%. Secondly, abnormal blood glucose was 36.26%, and the standardized rate was 30.04%. Central obesity was 33.12%, and the standardized rate was 30.28%. Hypertriglyceridemia was 32.90%, and the standardized prevalence rate was 32.70%. The rate of low HDL-C syndrome was 10.25%, and the standardized rate was 11.67%. The results of multivariate logistic regression analysis showed that the risk of MS increased with age, and the risk of MS in urban residents was lower than that in rural residents (OR=0.835, 95%CI: 0.77-0.886). Administrative and professional workers had a higher risk of MS than farmers (OR=1.313, 95%CI:1.194-1.445). Overweight, obesity, central obesity, history of self-reported hypertension, history of self-reported diabetes, and history of self-reported dyslipidemia were associated with a higher risk of MS, and the differences were statistically significant (P < 0.001). Conclusion The prevalence of MS is high in people aged 35-75 years in Hubei Province. On the basis of comprehensive intervention, focus monitoring should be strengthened to control the risk factors of MS and reduce the risk of cardiovascular and cerebrovascular diseases.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Berberine is a kind of isoquinoline alkaloid extracted from the roots and rhizomes of many medicinal plants,such as Coptis chinensis of Ranunculus family,Phellodendron chinensis of rutaceae family,and Berberine Sanacanthus family.In recent years,with the deepening of research,berberine has shown re-markable prevention and treatment effect in a variety of neuro-psychiatric disease models.This paper summarizes the research progress of berberine in neuropsychiatric diseases and provides theoretical support for further clinical prevention and treatment of neuropsychiatric diseases.

AIM:To optimize the clinical dosage and administration regimen of a novel long-acting injectable aripiprazole microsphere(LZMT05)using plasma concentration data from two clinical trials.METHODS:Plasma concentrations were collected from 196 schizophrenia patients administered LZMT05,and a population pharmacokinetic(Pop-PK)model was developed.The therapeutic window was defined as the steady-state trough-to-peak concentration range(94.0-534 ng/mL)of oral ar-ipiprazole.Multiple clinical scenarios were simulat-ed to identify the optimal regimen.RESULTS:A one-compartment model with dual first-order ab-sorption and first-order elimination characterized LZMT05 pharmacokinetics.Covariates like sex and CYP2D6 genotype were integrated into the final model.Simulations demonstrated that switching from 10 mg oral aripiprazole to 350 mg LZMT05 ev-ery 4 weeks sustained concentrations within the therapeutic window with minimal peak-to-trough fluctuations.CONCLUSION:The PopPK-guided opti-mized LZMT05 regimen maintained drug exposure within the therapeutic window,suggesting favor-able efficacy and safety.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

AIM:To optimize the clinical dosage and administration regimen of a novel long-acting injectable aripiprazole microsphere(LZMT05)using plasma concentration data from two clinical trials.METHODS:Plasma concentrations were collected from 196 schizophrenia patients administered LZMT05,and a population pharmacokinetic(Pop-PK)model was developed.The therapeutic window was defined as the steady-state trough-to-peak concentration range(94.0-534 ng/mL)of oral ar-ipiprazole.Multiple clinical scenarios were simulat-ed to identify the optimal regimen.RESULTS:A one-compartment model with dual first-order ab-sorption and first-order elimination characterized LZMT05 pharmacokinetics.Covariates like sex and CYP2D6 genotype were integrated into the final model.Simulations demonstrated that switching from 10 mg oral aripiprazole to 350 mg LZMT05 ev-ery 4 weeks sustained concentrations within the therapeutic window with minimal peak-to-trough fluctuations.CONCLUSION:The PopPK-guided opti-mized LZMT05 regimen maintained drug exposure within the therapeutic window,suggesting favor-able efficacy and safety.

Berberine is a kind of isoquinoline alkaloid extracted from the roots and rhizomes of many medicinal plants,such as Coptis chinensis of Ranunculus family,Phellodendron chinensis of rutaceae family,and Berberine Sanacanthus family.In recent years,with the deepening of research,berberine has shown re-markable prevention and treatment effect in a variety of neuro-psychiatric disease models.This paper summarizes the research progress of berberine in neuropsychiatric diseases and provides theoretical support for further clinical prevention and treatment of neuropsychiatric diseases.

Cyclic GMP-AMP synthase(cGAS)is a congenital immune sensor that can recognize cytoplasm abnormal dsDNA.By catalyzing the second messenger cyclic GMP-AMP(cGAMP)formation,it activates stimulator of interferon genes(STING),releases type Ⅰ interferon and inflammatory cytokines,activates the host immune response,and participates in cerebral ischemia reperfusion injury(CIRI)cascade reaction.This article reviews the research progress of the mechanism of cGAS-STING signaling pathway participation in CIRI,hoping to provide ideas for its treatment.

Objective:This study applies an integrated SWOT-CLPV framework combined with stakeholder analysis to systematically assess the strengths,weaknesses,opportunities,and threats of China's disease control inspector system,while identifying its control factors,leverage points,key problems,and vulnerabilities.Methods:Drawing on literature review,policy document analysis,and expert interviews with seven public health professionals,we extracted and categorized SWOT elements.A CLPV interaction analysis was conducted alongside stakeholder mapping to evaluate internal dynamics and systemic risks.Results:The inspector system demonstrates strengths in policy innovation and medical-public health integration,with external opportunities stemming from rising public health awareness and digital health advancements.However,the system faces weak endogenous momentum,limited leverage,and prominent control constraints and problem-prone areas,especially among grassroots institutions and inspectors themselves.Cross-sectoral coordination barriers and uneven local implementation contribute to significant institutional vulnerabilities.Conclusion:To enhance implementation and resilience,the system requires capacity building for key actors,improved governance structures,incentive and evaluation reforms,and strengthened coordination mechanisms to support the sustained and adaptive development of public health supervision.

BACKGROUND:Preliminary research by our group suggests that targeting fibroblast growth factor receptor 1(FGFR1)may be an effective strategy for treating RA. OBJECTIVE:To investigate the effects of an FGFR1 inhibitor(PD173074)on bone destruction in rats with collagen-induced arthritis. METHODS:Twenty-five female Sprague-Dawley rats were randomly divided into five groups:normal control group,model group,methotrexate group,low-dose PD173074 group,and high-dose PD173074 group.Except for the normal control group,rat models of type Ⅱ collagen-induced arthritis were made in each group.After successful modeling,rats were injected intraperitoneally with sterile PBS in the normal and model groups,1.04 mg/kg methotrexate in the methotrexate group,and 5 and 20 mg/kg in the low-dose group and high-dose PD173074 groups,once a week.After 4 weeks of drug administration,clinical symptoms and joint swelling in rats were observed.Micro-CT was used for three-dimensional reconstruction and analysis of the ankle joints.Pathological changes in the ankle joints were observed.Periarticular angiogenesis and the expression of receptor activator of nuclear factor-Κb ligand were detected.The expression levels of p-FGFR1,vascular endothelial growth factor A,and tartrate-resistant acid phosphatase in the synovial membrane were measured.Pathological changes in the liver,spleen,and kidney were observed and liver,spleen,and kidney indices were calculated. RESULTS AND CONCLUSION:PD173074 could alleviate clinical symptoms and joint swelling,delay bone loss,improve bone structure,reduce synovial invasion and cartilage bone erosion,reduce the number of periarticular osteoclasts,inhibit angiogenesis in synovial tissues,reduce the expression of receptor activator of nuclear factor-Κb ligand,and inhibit the expression of FGFR1 phosphorylated protein,tartrate-resistant acid phosphatase and vascular endothelial growth factor A.Pathologic observation of the liver,spleen and kidney in rats showed no obvious toxic side effects after PD173074 treatment.To conclude,the FGFR1 inhibitor can delay the progression of joint inflammation and bone destruction and inhibit angiogenesis in the rat model of type Ⅱ collagen-induced arthritis.The therapeutic effect of PD173074 has been preliminarily validated in the type Ⅱ collagen-induced arthritis model and may act by inhibiting FGFR1 phosphorylation,which provides a direction for the search of new therapeutic targets for rheumatoid arthritis.