The c-Jun N-terminal kinase （JNK） signaling pathway， a key member of the mitogen-activated protein kinase （MAPK） family， plays a central role in the pathogenesis and progression of central nervous system （CNS） diseases by regulating core biological processes such as apoptosis， inflammatory responses， synaptic plasticity， and autophagy. This article sorts out and analyzes relevant literature published domestically and internationally in recent years， summarizing the mechanisms of action of the JNK signaling pathway in common CNS diseases and the research progress in traditional Chinese medicine （TCM） interventions in CNS diseases through the regulation of the JNK signaling pathway. Studies have shown that active components of TCM， such as berberine， paeoniflorin， and astragaloside Ⅳ， as well as compound formulations like Heixiaoyao san， Ditan tang， and Buyang huanwu tang， can exert neuroprotective effects in various CNS disorders， including Alzheimer’s disease， Parkinson’s disease， cerebral ischemia-reperfusion injury， and epilepsy， by inhibiting the aberrant activation of the JNK signaling pathway， thereby alleviating neuroinflammation， oxidative stress， and neuronal apoptosis， while improving synaptic function and cognitive behavioral deficits， regulating autophagy， and maintaining blood-brain barrier integrity.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

Alzheimer’s disease （AD） is an age-related neurodegenerative disorder. Marrow-sea insufficiency serves as the fundamental basis for the onset of AD. Early syndrome differentiation-based intervention helps to delay disease progression， and improve patients’ cognitive function. Qifu yin is a representative specialized prescription for AD with marrow-sea insufficiency syndrome. Studies demonstrate that Qifu yin exerts neuroprotective effects through multiple pathways， including inhibiting the abnormal deposition of amyloid β -protein and hyperphosphorylation of tau protein， alleviating neuroinflammation， regulating oxidative stress and mitochondrial dysfunction， modulating the cholinergic system， and improving synaptic plasticity. Qifu yin combined with Western medicine such as donepezil， memantine， and butylphthalide， or combined with external therapies such as acupuncture， can effectively improve cognitive function and activities of daily living in AD patients with favorable safety. Future research should focus on the core pathogenesis and key targets of AD with marrow-sea insufficiency syndrome， provide in-depth elucidation of the scientific connotation of Qifu yin’s “tonifying the kidney to produce marrow”， and further conduct high-quality clinical studies to provide scientific evidence for the prevention and treatment of AD with marrow-sea insufficiency syndrome.

ObjectiveBased on ultra performance liquid chromatography-quadrupole-electrostatic field orbital trap high-resolution mass spectrometry（UPLC-Q-Orbitrap-MS）， to identify the in vivo and in vitro chemical components of total phenolic acids in Gei Herba（TPAGH）， and to clarify the pharmacological effects and potential mechanisms of the effective part in promoting acute wound healing and inhibiting scar formation. MethodsUPLC-Q-Orbitrap-MS was used to identify the chemical components of TPAGH and ingredients absorbed in vivo after topical administration. A total of 120 ICR mice were randomly divided into the model group， recombinant human epidermal growth factor（rhEGF） group（4 mg·kg^-1）， and low， medium， and high dose groups of TPAGH（3.5， 7， 14 mg·kg^-1）， with 24 mice in each group. A full-thickness skin excision model was constructed， and each administration group was coated with the drug at the wound site， and the model group was treated with an equal volume of normal saline， the treatment was continued for 30 days， during which 8 mice from each group were sacrificed on days 6， 12， and 30. The healing of the wounds in the mice was observed， and histopathological changes in the skin tissues were dynamically observed by hematoxylin-eosin（HE）， Masson， and Sirius red staining， and enzyme-linked immunosorbent assay（ELISA） was used to dynamically measure the contents of interleukin-6（IL-6）， tumor necrosis factor-α（TNF-α）， vascular endothelial growth factor A（VEGFA）， matrix metalloproteinase（MMP）-3 and MMP-9 in skin tissues. Network pharmacology was used to predict the targets related to the promotion of acute wound healing and the inhibition of scar formation by TPAGH， and molecular docking of key components and targets was performed. Gene Ontology（GO） biological process analysis and Kyoto Encyclopedia of Genes and Genomes（KEGG） pathway enrichment analysis were carried out for the related targets， so as to construct a network diagram of herbal material-compound-target-pathway-pharmacological effect-disease for further exploring its potential mechanisms. ResultsA total of 146 compounds were identified in TPAGH， including 28 phenylpropanoids， 31 tannins， 23 triterpenes， 49 flavonoids， and 15 others， and 16 prototype components were found in the serum of mice. Pharmacodynamic results showed that， compared with the model group， the TPAGH groups showed a significant increase in relative wound healing rate and relative scar inhibition rate（P<0.05）， and the number of new capillaries， number of fibroblasts， number of new skin appendages， epidermal regeneration rate， collagen deposition ratio， and Ⅲ/Ⅰ collagen ratio in the tissue were significantly improved（P<0.05， 0.01）， the levels of IL-6， TNF-α， MMP-3 and MMP-9 in the skin tissues were reduced to different degrees， while the level of VEGFA was increased. Network pharmacology analysis screened 10 core targets， including tumor protein 53（TP53）， sarcoma receptor coactivator（SRC）， protein kinase B（Akt）1， signal transducer and activator of transcription 3（STAT3）， epidermal growth factor receptor（EGFR） and so on， participating in 75 signaling pathways such as advanced glycation end-products（AGE）-receptor for AGE（AGE/RAGE） signaling pathway， phosphatidylinositol 3-kinase（PI3K）/Akt signaling pathway， mitogen-activated protein kinase（MAPK） signaling pathway. Molecular docking confirmed that the key components genistein， geraniin， and casuariin had good binding ability to TP53， SRC， Akt1， STAT3 and EGFR. ConclusionThis study comprehensively reflects the chemical composition of TPAGH and the absorbed components after topical administration through UPLC-Q-Orbitrap-MS. TPAGH significantly regulates key indicators of skin healing and tissue reconstruction， thereby clarifying its role in promoting acute wound healing and inhibiting scar formation. By combining in vitro and in vivo component identification with network pharmacology， the study explores how key components may bind to targets such as TP53， Akt1 and EGFR， exerting therapeutic effects through related pathways such as immune inflammation and vascular regeneration.

BACKGROUND:Osteoporotic fracture is the most serious complication of osteoporosis.Previous studies have demonstrated that gut microbiota has a regulatory effect on skeletal tissue and that gut microbiota has an important relationship with osteoporotic fracture,but the causal relationship between the two is unclear. OBJECTIVE:To explore the causal relationship between gut microbiota and osteoporotic fractures using Mendelian randomization method. METHODS:The genome-wide association study(GWAS)datasets of gut microbiota and osteoporotic fracture were obtained from the IEU Open GWAS database and the Finnish database R9,respectively.Using gut microbiota as the exposure factor and osteoporotic fracture as the outcome variable,Mendelian randomization analyses with random-effects inverse variance weighted,MR-Egger regression,weighted median,simple model,and weighted model methods were performed to assess whether there is a causal relationship between gut microbiota and osteoporotic fracture.Sensitivity analyses were performed to test the reliability and robustness of the results.Reverse Mendelian randomization analyses were performed to further validate the causal relationship identified in the forward Mendelian randomization analyses. RESULTS AND CONCLUSION:The results of this Mendelian randomization analysis indicated a causal relationship between gut microbiota and osteoporotic fracture.Elevated abundance of Actinomycetales[odds ratio(OR)=1.562,95%confidence interval(CI):1.027-2.375,P=0.037),Actinomycetaceae(OR=1.561,95%CI:1.027-2.374,P=0.037),Actinomyces(OR=1.544,95%CI:1.130-2.110,P=0.006),Butyricicoccus(OR=1.781,95%CI:1.194-2.657,P=0.005),Coprococcus 2(OR=1.550,95%CI:1.068-2.251,P=0.021),Family ⅩⅢ UCG-001(OR=1.473,95%CI:1.001-2.168,P=0.049),Methanobrevibacter(OR=1.274,95%CI:1.001-1.621,P=0.049),and Roseburia(OR=1.429,95%CI:1.015-2.013,P=0.041)would increase the risk of osteoporotic fractures in patients.Elevated abundance of Bacteroidia(OR=0.660,95%CI:0.455-0.959,P=0.029),Bacteroidales(OR=0.660,95%CI:0.455-0.959,P=0.029),Christensenellacea(OR=0.725,95%CI:0.529-0.995,P=0.047),Ruminococcaceae(OR=0.643,95%CI:0.443-0.933,P=0.020),Enterorhabdus(OR=0.558,95%CI:0.395-0.788,P=0.001),Eubacterium rectale group(OR=0.631,95%CI:0.435-0.916,P=0.016),Lachnospiraceae UCG008(OR=0.738,95%CI:0.546-0.998,P=0.048),and Ruminiclostridium 9(OR=0.492,95%CI:0.324-0.746,P=0.001)would reduce the risk of osteoporotic fractures in patients.We identified 16 gut microbiota associated with osteoporotic fracture by the Mendelian randomization method.That is,using gut microbiota as the exposure factor and osteoporotic fracture as the outcome variable,eight gut microbiota showed positive causal associations with osteoporotic fracture and another eight gut microbiota showed negative causal associations with osteoporotic fracture.The results of this study not only identify new biomarkers for the early prediction of osteoporotic fracture and potential therapeutic targets in clinical practice,but also provide an experimental basis and theoretical basis for the study of improving the occurrence and prognosis of osteoporotic fracture through gut microbiota in bone tissue engineering.

Objective: To validate the analytical performance, operational performance, and process control measures of a domestic fully automatic nucleic acid testing (NAT) system, thereby ensuring an efficient and orderly blood screening workflow. Methods: The concordance rate and sensitivity of WanTag-Vortex Plus system were verified using WHO standard reference panels of HIV-1, HCV and HBV, while precision was assessed using weak positive samples of HIV-1, HCV and HBV. As for its operational performance evaluation, cross-contamination resistance was assessed using strong positive samples, and throughput and stress testing were conducted using negative samples. Reagent stability was verified using weak positive samples, and inter-system performance consistency was assessed using verification panels. In addition, the process control measures were verified using the laboratory quality control demand scale. Results: 1) Verification of concordance rate: The detection results of negative and positive samples of HIV-1, HCV and HBV by WanTag-Vortex Plus system were all consistent with expectations, and the concordance rate was 100%. 2) Precision verification: the repeatability and intermediate precision were extremely high, and the coefficient of variation was less than 5%. 3) Verification of analytical sensitivity: The detection limit of 95% for standard strains of HIV-1, HCV and HBV by WanTag-Vortex Plus system in our laboratory was consistent with the analytical sensitivity provided by reagent manufacturers. 4) Verification of cross-contamination resistance: Five strong positive samples and 87 negative samples were placed according to the actual working conditions and equipment operation design, and the test results were consistent with expectations, with no cross-contamination in the testing system. 5) Throughput and stress testing: Each system completed the individual donor-nucleic acid amplification testing (ID-NAT) of 276 samples in three batches within 12 hours, and successfully completed the ID-NAT test of 828 samples in three consecutive days. 6) Verification of reagent stability: After extreme storage (unsealed storage for 1 week with 4 freeze-thaw cycles), the reagents maintained 100% detection rate in the weak positive samples of HIV-1, HCV, and HBV, showing no significant differences from the control group (Kappa=1). 7) Verification of inter-system performance consistency: The system has stable operation performance, and the performance comparison results across the four devices were consistent (Kappa=1). 8) Process control measures: WanTag-Vortex Plus system software accurately controlled the equipment operation process with strict quality control measures, and correctly interpreted and safely reported the test results. Conclusion: The analytical and operational performance of the WanTag-Vortex Plus system complies with manufacturer design standards and essential laboratory workflow requirements. Integrated with laboratory information system (LIS), the system's control software meets standard process control requirements, yet requires further improvement.

Objective: To validate the analytical performance, operational performance, and process control measures of a domestic fully automatic nucleic acid testing (NAT) system, thereby ensuring an efficient and orderly blood screening workflow. Methods: The concordance rate and sensitivity of WanTag-Vortex Plus system were verified using WHO standard reference panels of HIV-1, HCV and HBV, while precision was assessed using weak positive samples of HIV-1, HCV and HBV. As for its operational performance evaluation, cross-contamination resistance was assessed using strong positive samples, and throughput and stress testing were conducted using negative samples. Reagent stability was verified using weak positive samples, and inter-system performance consistency was assessed using verification panels. In addition, the process control measures were verified using the laboratory quality control demand scale. Results: 1) Verification of concordance rate: The detection results of negative and positive samples of HIV-1, HCV and HBV by WanTag-Vortex Plus system were all consistent with expectations, and the concordance rate was 100%. 2) Precision verification: the repeatability and intermediate precision were extremely high, and the coefficient of variation was less than 5%. 3) Verification of analytical sensitivity: The detection limit of 95% for standard strains of HIV-1, HCV and HBV by WanTag-Vortex Plus system in our laboratory was consistent with the analytical sensitivity provided by reagent manufacturers. 4) Verification of cross-contamination resistance: Five strong positive samples and 87 negative samples were placed according to the actual working conditions and equipment operation design, and the test results were consistent with expectations, with no cross-contamination in the testing system. 5) Throughput and stress testing: Each system completed the individual donor-nucleic acid amplification testing (ID-NAT) of 276 samples in three batches within 12 hours, and successfully completed the ID-NAT test of 828 samples in three consecutive days. 6) Verification of reagent stability: After extreme storage (unsealed storage for 1 week with 4 freeze-thaw cycles), the reagents maintained 100% detection rate in the weak positive samples of HIV-1, HCV, and HBV, showing no significant differences from the control group (Kappa=1). 7) Verification of inter-system performance consistency: The system has stable operation performance, and the performance comparison results across the four devices were consistent (Kappa=1). 8) Process control measures: WanTag-Vortex Plus system software accurately controlled the equipment operation process with strict quality control measures, and correctly interpreted and safely reported the test results. Conclusion: The analytical and operational performance of the WanTag-Vortex Plus system complies with manufacturer design standards and essential laboratory workflow requirements. Integrated with laboratory information system (LIS), the system's control software meets standard process control requirements, yet requires further improvement.

Objective: To analyze the epidemiological characteristics and incidence trends of pulmonary tuberculosis (TB) in children aged 0-14 years in Shaanxi Province from 2010 to 2024, so as to provide a reference for optimizing child TB prevention and control strategies. Methods: Data on pulmonary TB cases in children aged 0-14 years and demographic information in Shaanxi Province from 2010 to 2024 were collected from Surveillance and Reporting Management System with Disease Prevention and Control Information Management System under the National Health Security Informatization Project Disease Prevention and Control Information System. A Joinpoint regression model was established to analyze the temporal, spatial, and population distribution trends of child pulmonary TB incidence. Results: A total of 2 954 cases of pulmonary TB in children aged 0-14 years were reported in Shaanxi Province from 2010 to 2024, accounting for 0.97% of all TB cases in the general population. The average annual reported incidence rate in children was 3.32 per 100 000. Among these cases, 804 were pathogenetically positive, showing a increasing trend ( χ 2 trend =420.94, P < 0.01 ). The overall reported incidence rate of pulmonary TB in children aged 0-14 years in Shaanxi Province showed a decreasing trend, dropping from 5.35 per 100 000 in 2010 to 2.41 per 100 000 in 2024. Joinpoint regression analysis identified three distinct phases for the reported incidence rate of TB:a rapid decline from 2010 to 2013 (APC=-20.02%, 95% CI = -33.64% to -10.42%), a slight increase from 2013 to 2017 (APC=11.18%, 95% CI =3.07%-24.17%) and a slight decline again from 2017 to 2024 (APC= -7.27 %, 95% CI =-12.73% to -4.30%) (all P <0.01). Among children aged 0-14 years, the age group with the highest average annual reported incidence rate was 10-14 years (8.02 per 100 000), followed by 5-9 years (1.44 per 100 000), and 0-4 years had the lowest rate (0.95 per 100 000). The difference in reported incidence rates among the three age groups was statistically significant ( χ 2= 51.91, P <0.01). The average annual reported incidence rate of TB was 3.25 per 100 000 in boys and 3.39 per 100 000 in girls, with no statistically significant difference ( χ 2=2.01, P >0.05). There was no obvious periodic variation in the annual case reporting. Among all cities in Shaanxi Province, Ankang City had the highest average annual reported incidence rate (5.16 per 100 000). Conclusions From 2010 to 2024, the reported incidence rate of pulmonary TB in children aged 0-14 years in Shaanxi Province showed an overall decreasing trend. However, it is still necessary to strengthen active surveillance, implement targeted measures in high incidence areas such as Ankang City, and maintain continuous attention to child TB prevention and control.

ObjectiveTo elucidate the potential mechanism of modified Sinisan （MSNS） in alleviating anxiety-like behaviors induced by chronic restraint stress （CRS） in mice at the metabolic level based on serum untargeted metabolomics and identify key metabolites and metabolic pathways regulated by MSNS. MethodsSeventy-two male C57BL/6 mice were randomly assigned into six groups： control， model， high-dose （2.4 g·kg^-1） MSNS， medium-dose （1.2 g·kg^-1） MSNS， low-dose （0.6 g·kg^-1） MSNS， and positive control （fluoxetine， 2.6 mg·kg^-1）. Except the control group， the other groups were subjected to CRS for the modeling of anxiety. Mice were administrated with corresponding agents by gavage 2 h before daily restraint for 14 days. Anxiety-like behaviors were evaluated by the open field test （OFT）， elevated plus maze （EPM） test， and light/dark box （LDB） test. Serum levels of corticotropin-releasing hormone （CRH）， adrenocorticotrophic hormone （ACTH）， and corticosterone （CORT） were measured via ELISA to assess stress levels. Ultra-performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS） was employed to detect 9 metabolites in the brain tissue and serum metabolites. Orthogonal partial least squares-discriminant analysis （OPLS-DA） was adopted to identify differential metabolites （VIP>1.0， P<0.05）. MetaboAnalyst 5.0 was used for metabolic pathway enrichment analysis of the differential metabolites. ResultsCompared with the control group， the model group showed reductions in the central activity time and central distance in the OFT （P<0.05）， the proportions of open-arm residence time and open-arm residence times in the EPM test （P<0.01）， and the proportions of open box activity time and open box activity distance in the LDB test （P<0.05）， which were increased in the medium- and high-dose MSNS groups compared with the model group （P<0.05）. Compared with the control group， the model group showed elevated levels of CRH， ACTH， and CORT in the serum （P<0.01）， and the elevations were diminished in the medium- and high-dose MSNS groups （P<0.05）. UPLC-MS results indicated that compared with the control group， the model group presented declined DA， GABA， 5-HIAA， 5-HT， and 5-HT/Trp levels （P<0.05， P<0.01） and raised Glu， NE， Kyn， and Kyn/Trp levels （P<0.05）. Compared with the model group， high-dose MSNS increased the GABA， 5-HIAA， and 5-HT/Trp levels （P<0.05） and lowered the Glu and Kyn/Trp levels （P<0.05）. Untargeted metabolomics identified that 16 CRS-induced metabolic disturbances were reversed by MSNS. KEGG pathway analysis indicated that MSNS primarily modulated eight core pathways including alanine/aspartate/glutamate metabolism， butyrate metabolism， arginine-proline metabolism， TCA cycle， unsaturated fatty acid biosynthesis， and tryptophan metabolism. The mechanisms involved multidimensional biological processes， including neurotransmitter homeostasis regulation， TCA cycle energy metabolism optimization， and inflammatory response suppression. ConclusionMSNS alleviates CRS-induced anxiety-like behaviors in mice by mitigating hypothalamic-pituitary-adrenal axis hyperactivity， improving hippocampal neurotransmitter and tryptophan metabolic pathways， and regulating alanine/aspartate/glutamate metabolism， butyrate metabolism， arginine-proline metabolism， and TCA cycle.