This paper outlines the development of artificial intelligence （AI） and its applications in traditional Chinese medicine （TCM） research， and elucidates the roles and advantages of large language models， knowledge graphs， and natural language processing in advancing syndrome identification， prescription generation， and mechanism exploration. Using spleen-stomach diseases as an example， it demonstrates the empowering effects of AI in classical literature mining， precise clinical syndrome differentiation， efficacy and safety prediction， and intelligent education， highlighting an upgraded research paradigm that evolves from data-driven and knowledge-driven approaches to intelligence-driven models. To address challenges related to privacy protection and regulatory compliance in cross-institutional data collaboration， a "trusted federated evidence-based analysis platform for TCM spleen-stomach diseases" is proposed， integrating blockchain-based smart contracts， federated learning， and secure multi-party computation. The deep integration of AI with privacy-preserving computing is reshaping research and clinical practice in TCM spleen-stomach diseases， providing feasible pathways and a technical framework for building a high-quality， trustworthy TCM big-data ecosystem and achieving precision syndrome differentiation.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

The study aims to explore the methods for preparing nanocomplexes of Prussian blue nanoparticles (PBNPs) with UNO peptide (UNO-PBNPs) and the functions of the nanocomplexes targeting M2-type macrophages in vitro. PBNPs were prepared by the hydrothermal synthesis method. Subsequently, the peptide UNO (CSPGAKVRC) targeting the mannose receptor was modified on their surface by a heterobifunctional coupling approach. The morphological characteristics of nanoparticles were observed by scanning and transmission electron microscopy. Additionally, their particle size, Zeta potential, and dispersion stability were assessed. The structural characteristics of nanoparticles were analyzed by X-ray diffraction and other techniques. The biological safety of the nanoparticles was evaluated by the CCK-8 assay and hemolysis experiments. Moreover, the targeting performance of UNO-PBNPs towards M2-type macrophages was assessed in vitro. The results showed that the synthesized UNO-PBNPs exhibited uniform cubic morphology, with an average particle size of (202.00±4.21) nm. They were negative charged, well dispersed, and stable. At concentrations ≤ 200 μg/mL, the synthesized UNO-PBNPs led to the hemolysis rate below 5%, demonstrating excellent biocompatibility. The laser confocal imaging results showed that after co-incubation with M2-type macrophages, the FITC-labeled UNO-PBNPs were effectively accumulated in the cells, presenting a distinct fluorescence signal. Quantitative analysis by flow cytometry showed that the intracellular mean fluorescence intensity (6 019.00±346.04) of UNO-PBNPs was higher than that (4 054.00±379.14) of unmodified PBNPs (P < 0.001). In summary, the UNO-PBNPs prepared in this study exhibited a targeting effect on M2-type macrophages, providing a potential method for targeted delivery of PBNPs in the tumor microenvironment and laying a foundation for the remodeling of the tumor immunosuppressive microenvironment.
Ferrocyanides/chemistry* ; Nanoparticles/chemistry* ; Macrophages/drug effects* ; Peptides/chemistry* ; Particle Size ; Animals ; Mannose Receptor ; Mice ; Lectins, C-Type ; Mannose-Binding Lectins ; Receptors, Cell Surface

Objective To investigate the distribution and antimicrobial resistance profiles of clinically isolated Haemophilus influenzae and Moraxella catarrhalis in hospitals across China from 2015 to 2021,and provide evidence for rational use of antimicrobial agents.Methods Data of H.influenzae and M.catarrhalis strains isolated from 2015 to 2021 in CHINET program were collected for analysis,and antimicrobial susceptibility testing was performed by disc diffusion method or automated systems according to the uniform protocol of CHINET.The results were interpreted according to the CLSI breakpoints in 2022.Beta-lactamases was detected by using nitrocefin disk.Results From 2015 to 2021,a total of 43 642 strains of Haemophilus species were isolated,accounting for 2.91％of the total clinical isolates and 4.07％of Gram-negative bacteria in CHINET program.Among the 40 437 strains of H.influenzae,66.89％were isolated from children and 33.11％were isolated from adults.More than 90％of the H.influenzae strains were isolated from respiratory tract specimens.The prevalence of β-lactamase was 53.79％in H.influenzae strains.The H.influenzae strains isolated from children showed higher resistance rate than the strains isolated from adults.Overall,779 strains of H.influenzae did not produce β-lactamase but were resistant to ampicillin(BLNAR).Beta-lactamase-producing strains showed significantly higher resistance rates to these antimicrobial agents than the β-lactamase-nonproducing strains.Of the 16 191 M.catarrhalis strains,80.06％were isolated from children and 19.94％isolated from adults.M.catarrhalis strains were mostly susceptible to both amoxicillin-clavulanic acid and cefuroxime,evidenced by resistance rate lower than 2.0％.Conclusions The emergence of antibiotic-resistant H.influenzae due to β-lactamase production poses a challenge for clinical anti-infective treatment.Therefore,it is very important to implement antibiotic resistance surveillance for H.influenzae and guide rational antibiotic use.All local clinical microbiology laboratories should actively improve antibiotic susceptibility testing and strengthen antibiotic resistance surveillance for H.influenzae.

Objective To understand the changing composition and antibiotic resistance of bacterial species in the clinical isolates from outpatient and emergency department(hereinafter referred to as outpatients)and inpatient children over time in various hospitals,and to provide laboratory evidence for rational antibiotic use.Methods The data on clinically isolated pathogenic bacteria and antimicrobial susceptibility of isolates from outpatients and inpatient children in the CHINET program from 2015 to 2021 were collected and analyzed.Results A total of 278 471 isolates were isolated from pediatric patients in the CHINET program from 2015 to 2021.About 17.1％of the strains were isolated from outpatients,primarily group A β-hemolytic Streptococcus,Escherichia coli,and Staphylococcus aureus.Most of the strains(82.9％)were isolated from inpatients,mainly SS.aureus,E.coli,and H.influenzae.The prevalence of methicillin-resistant S.aureus(MRSA)in outpatients(24.5％)was lower than that in inpatient children(31.5％).The MRSA isolates from outpatients showed lower resistance rates to the antibiotics tested than the strains isolated from inpatient children.The prevalence of vancomycin-resistant Enterococcus faecalis or E.faecium and penicillin-resistant S.pneumoniae was low in either outpatients or inpatient children.S.pneumoniae,β-hemolytic Streptococcus and S.viridans showed high resistance rates to erythromycin.The prevalence of erythromycin-resistant group A β-hemolytic Streptococcus was higher in outpatients than that in inpatient children.The prevalence of β-lactamase-producing H.influenzae showed an overall upward trend in children,but lower in outpatients(45.1％)than in inpatient children(59.4％).The prevalence of carbapenem-resistant Klebsiella pneumoniae(CRKpn),carbapenem-resistant Pseudomonas aeruginosa(CRPae)and carbapenem-resistant Acinetobacter baumannii(CRAba)was 14％,11.7％,47.8％in outpatients,but 24.2％,20.6％,and 52.8％in inpatient children,respectively.The prevalence of multidrug-resistant E.coli,K.pneumoniae,Proteus mirabilis,P.aeruginosa and A.baumannii strains was lower in outpatients than in inpatient children.The prevalence of fluoroquinolone-resistant E.coli,ESBLs-producing K.pneumoniae,ESBLs-producing P.mirabilis,carbapenem-resistant E.coli(CREco),CRKpn,and CRPae was lower in children in outpatients than in inpatient children,but the prevalence of CRAba in 2021 was higher than in inpatient children.Conclusions The distribution of clinical isolates from children is different between outpatients and inpatients.The prevalence of MRSA,ESBL,and CRO was higher in inpatient children than in outpatients.Antibiotics should be used rationally in clinical practice based on etiological diagnosis and antimicrobial susceptibility test results.Ongoing antimicrobial resistance surveillance and prevention and control of hospital infections are crucial to curbing bacterial resistance.

Objective To investigate the changing antibiotic resistance profiles of E.coli isolated from patients in the 52 hospitals participating in the CHINET program from 2015 to 2021.Methods Antimicrobial susceptibility was tested for clinical isolates of E.coli according to the unified protocol of CHINET program.WHONET 5.6 and SPSS 20.0 software were used for data analysis.Results Atotal of 289 760 nonduplicate clinical strains ofE.coli were isolated from 2015 to 2021,mainly from urine samples(44.7±3.2)％.The proportion of E.coli strains isolated from urine samples was higher in females than in males(59.0％vs 29.5％).The proportion of E.coli strains isolated from respiratory tract and cerebrospinal fluid samples was significantly higher in children than in adults(16.7％vs 7.8％,0.8％vs 0.1％,both P＜0.05).The isolates from internal medicine department accounted for the largest proportion(28.9±2.8)％with an increasing trend over years.Overall,the prevalence of ESBLs-producing E.coli and carbapenem resistant E.coli(CREco)was 55.9％and 1.8％,respectively during the 7-year period.The prevalence of ESBLs-producing E.coli was the highest in tertiary hospitals each year from 2015 to 2021 compared to secondary hospitals.The prevalence of CREco was higher in children's hospitals compared to secondary and tertiary hospitals each year from 2015 to 2021.The prevalence of ESBLs-producing E.coli in tertiary hospitals and children's hospitals and the prevalence of CREco in children's hospitals showed a decreasing trend over the 7-year period.The prevalence of CREco in secondary and tertiary hospitals increased slowly.Antibiotic resistance rates changed slowly from 2015 to 2021.Carbapenem drugs(imipenem,meropenem)were the most active drugs amongβ-lactams against E.coli(resistance rate≤2.1％).The resistance rates of E.coli to β-lactam/β-lactam inhibitor combinations(piperacillin-tazobactam,cefoperazone-sulbactam),aminoglycosides(amikacin),nitrofurantoin and fosfomycin(for urinary isolates only)were all less than 10％.The resistance rate of E.coli strains to antibiotics varied with the level of hospitals and the departments where the strains were isolated,especially for cefazolin and ciprofloxacin,to which the resistance rate of E.coli strains from children in non-ICU departments was significantly lower than that of the strains isolated from other departments(P＜0.05).The E.coli isolates from ICU showed higher resistance rate to most antimicrobial agents tested(excluding tigecycline)than the strains isolated from other departments.The E.coli strains isolated from tertiary hospitals showed higher resistance rates to the antimicrobial agents tested(excluding tigecycline,polymyxin B,cefepime and carbapenems)than the strains from secondary hospitals and children's hospitals.Conclusions E.coli is an important pathogen causing clinical infection.More than half of the clinical isolates produced ESBL.The prevalence of CREco is increasing in secondary and tertiary hospitals over the 7-year period even though the overall prevalence is still low.This is an issue of concern.

OBJECTIVE: To elucidate the effect of Huanglian-Renshen-Decoction (HRD) on ameliorating type 2 diabetes mellitus by maintaining islet β -cell identity through regulating paracrine and endocrine glucagon-like peptide-1 (GLP-1)/GLP-1 receptor (GLP-1R) in both islet and intestine. METHODS: The db/db mice were divided into the model (distilled water), low-dose HRD (LHRD, 3 g/kg), high-dose HRD (HHRD, 6 g/kg), and liraglutide (400 µ g/kg) groups using a random number table, 8 mice in each group. The db/m mice were used as the control group (n=8, distilled water). The entire treatment of mice lasted for 6 weeks. Blood insulin, glucose, and GLP-1 levels were quantified using enzyme-linked immunosorbent assay kits. The proliferation and apoptosis factors of islet cells were determined by immunohistochemistry (IHC) and immunofluorescence (IF) staining. Then, GLP-1, GLP-1R, prohormone convertase 1/3 (PC1/3), PC2, v-maf musculoaponeurotic fibrosarcoma oncogene homologue A (MafA), and pancreatic and duodenal homeobox 1 (PDX1) were detected by Western blot, IHC, IF, and real-time quantitative polymerase chain reaction, respectively. RESULTS: HRD reduced the weight and blood glucose of the db/db mice, and improved insulin sensitivity at the same time (P<0.05 or P<0.01). HRD also promoted mice to secrete more insulin and less glucagon (P<0.05 or P<0.01). Moreover, it also increased the number of islet β cell and decreased islet α cell mass (P<0.01). After HRD treatment, the levels of GLP-1, GLP-1R, PC1/3, PC2, MafA, and PDX1 in the pancreas and intestine significantly increased (P<0.05 or P<0.01). CONCLUSION HRD can maintain the normal function and identity of islet β cell, and the underlying mechanism is related to promoting the paracrine and endocrine activation of GLP-1 in pancreas and intestine.
Animals ; Glucagon-Like Peptide 1/metabolism* ; Diabetes Mellitus, Type 2/metabolism* ; Glucagon-Like Peptide-1 Receptor/metabolism* ; Insulin-Secreting Cells/pathology* ; Drugs, Chinese Herbal/pharmacology* ; Male ; Blood Glucose/metabolism* ; Insulin/blood* ; Mice ; Intestinal Mucosa/pathology* ; Apoptosis/drug effects* ; Cell Proliferation/drug effects* ; Islets of Langerhans/pathology*

OBJECTIVE: To identify the underlying molecular mechanism of Modified Hu-Lu-Ba-Wan (MHW) in alleviating renal lesions in mice with diabetic kidney disease (DKD). METHODS: The db/db mice were divided into model group and MHW group according to a random number table, while db/m mice were settled as the control group (n=8 per group). The control and model groups were gavaged daily with distilled water [10 mL/(kg·d)], and the MHW group was treated with MHW [17.8 g/(kg·d)] for 6 weeks. After MHW administration for 6 weeks, indicators associated with glucolipid metabolism and urinary albumin were tested. Podocytes were observed by transmission electron microscopy. Kidney transcriptomics was performed after confirming therapeutic effects of MHW on DKD mice. The relevant target of MHW' effect in DKD was further determined by enzyme-linked immunosorbent assay, Western blot analysis, immunohistochemistry, and immunofluorescence staining. RESULTS: Compared with the model group, MHW improved glucose and lipid metabolism (P<0.05), and reduced lipid deposition in the kidney. Meanwhile, MHW reduced the excretion of urinary albumin (P<0.05) and ameliorated renal damage. Transcriptomic analysis revealed that the inflammation response, particularly the interleukin-17 (IL-17) signaling pathway, may be responsible for the effect of MHW on DKD. Furtherly, our results found that MHW inhibited IL-17A and alleviated early fibrosis in the diabetic kidney. CONCLUSION MHW ameliorated renal damage in DKD via inhibiting IL-17A, suggesting a potential strategy for DKD therapy.
Animals ; Diabetic Nephropathies/genetics* ; Interleukin-17/antagonists & inhibitors* ; Drugs, Chinese Herbal/therapeutic use* ; Male ; Kidney/ultrastructure* ; Podocytes/metabolism* ; Mice ; Albuminuria ; Lipid Metabolism/drug effects* ; Mice, Inbred C57BL

OBJECTIVE: To investigate the effects of acupuncture on the neurotransmitter levels and gut microbiota in a mouse model of Tourette syndrome (TS). METHODS: Thirty-six male C57/BL6 mice were randomly divided into 4 groups using a random number table method: 3,3'-iminodipropionitrile (IDPN) group, control group, acupuncture group, and tiapride group, with 9 mice in each group. In the IDPN group, acupuncture group, and tiapride group, mice received daily intraperitoneal injections of IDPN (300 mg/kg body weight) for 7 consecutive days to induce stereotyped behaviors. Subsequently, in the acupuncture intervention group, standardized acupuncture treatment was administered for 14 consecutive days to IDPN-induced TS model mice. The selected acupoints included Baihui (DU 20), Yintang (DU 29), Waiguan (SJ 5), and Zulinqi (GB 41). In the tiapride group, mice were administered tiapride (50 mg/kg body weight) via oral gavage daily for 14 consecutive days. The control group, IDPN group, and acupuncture group received the same volume of saline orally for 14 consecutive days. Stereotypic behaviors were quantified through behavioral assessments. Neurotransmitter levels, including dopamine (DA), glutamate (Glu), and aspartate (ASP) in striatal tissue were measured using enzyme-linked immunosorbent assay. Dopamine transporter (DAT) expression levels were additionally quantified through quantitative polymerase chain reaction (qPCR). Gut microbial composition was analyzed through 16S ribosomal RNA gene sequencing, while metabolic profiling was conducted using liquid chromatography-mass spectrometry (LC-MS). RESULTS: Acupuncture administration significantly attenuated stereotypic behaviors, concurrently reducing striatal levels of DA, Glu and ASP concentrations while upregulating DAT expression compared with untreated TS controls (P<0.05 or P<0.01). Comparative analysis identified significant differences in Muribaculaceae (P=0.001), Oscillospiraceae (P=0.049), Desulfovibrionaceae (P=0.001), and Marinifilaceae (P=0.014) following acupuncture intervention. Metabolomic profiling revealed alterations in 7 metabolites and 18 metabolic pathways when compared to the TS mice, which involved various amino acid metabolisms associated with DA, Glu, and ASP. CONCLUSIONS Acupuncture demonstrates significant modulatory effects on both central neurotransmitter systems and gut microbial ecology, thereby highlighting its dual therapeutic potential for TS management through gut-brain axis regulation.
Animals ; Tourette Syndrome/metabolism* ; Gastrointestinal Microbiome ; Neurotransmitter Agents/metabolism* ; Acupuncture Therapy ; Male ; Mice, Inbred C57BL ; Mice