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.

Irritable bowel syndrome （IBS） is a functional bowel disorder characterized primarily by abdominal pain and altered defecation habits. In recent years， traditional Chinese medicine （TCM） has made progress in multiple aspects of IBS research and treatment， including syndrome distribution， development of TCM formulas， clinical efficacy evaluation， external therapies， and psychosocial regulation. However， it still faces challenges such as over-reliance on symptomatic manifestations rather than biomarkers for diagnostic criteria， and the lack of high-quality evidence-based data supporting the efficacy of TCM formulas in treating IBS. This paper proposed that TCM diagnosis and treatment of IBS should adhere to the strategy of integrating the holistic concept with syndrome differentiation and treatment， combining TCM external therapies such as acupuncture， moxibustion and acupoint application）， and emphasizing individualized diagnosis and treatment for psychosomatic abnormalities. Future research should integrate multi-omics technologies， artificial intelligence and other methods to deepen the understanding of the pathogenesis of IBS and the mechanisms of TCM formulas， so as to promote the standardization and internationalization of TCM in the diagnosis and treatment of IBS.

ObjectiveThis study aims to explore the potential of different orders of magnitude single-nucleotide polymorphism (SNP) locus combinations for predicting distant kinship relationships. A high-density SNP locus set was constructed, and a comprehensive assessment of its inference capability was conducted. MethodsFirstly, we selected three commercial chip panels, CGA (Chinese genotyping array, Illumina), GSA (Global screening array, Illumina), Affy (23MF_V2 high-density SNP array, Affymetrix) and merged them after quality control, forming a high-density SNP locus panel(1 180 k). Secondly, we selected 161 samples and collected their peripheral blood samples by using whole-genome sequencing technology. Within this sample population, the levels of kinship relationships fully covered the range from level 1 to level 9, and the number of kinship pairs at each level was consistently maintained at over 50 pairs. From 161 samples data of whole-genome sequencing, the 1 180 k locus set was extracted, which is referred to as the high-density SNP locus set in the following text. The kinship inference was conducted using the identity-by-descent (IBD) algorithm with the selected optimal parameters. To comprehensively evaluate the performance of the high-density SNP locus set in kinship inference, we compared it with the three commercial chip panels, the intersection of these three chip loci, and the control sets constructed by randomly reducing the number of the high-density SNP locus set. Based on the changes in the IBD lengths, as well as the dynamic trends in prediction accuracy, we conducted a scientific assessment of the kinship inference capability of the high-density SNP locus set. ResultsAfter screening, a set of 1 184 334 autosomal SNPs was obtained. During the process of screening the optimal IBD length threshold, the result revealed that 0 cM, 1 cM, and 2 cM all demonstrated good applicability. However, to avoid the issue of a large amount of redundant information caused by setting a too low IBD length threshold, this study ultimately selected 2 cM as the optimal threshold. Compared with the average results of three chip panels, the high-density SNP locus set increased the total IBD length and the average IBD length across levels 1-9; the accuracy of the confidence interval for level 8 was 70.97%, which represented a 3.50% improvement; the average confidence interval accuracy for levels 1-8 was 91.39%, representing a 1.00% increase; and the false negative rates at levels 8 and 9 were reduced by 2.42% and 6.76%, respectively. The system efficacy of the high-density SNP locus set for kinship inference of first to eighth degree relationships reached 98.91%. Through random reduction of the high-density SNP locus set results, it is found that increasing the number of SNPs with the panel, the detection efficiency of IBD length showed a significant upward trend. At the same time, the overall trend in the accuracy of kinship relationship prediction as well as the confidence interval accuracy also indicated that both metrics steadily increased with the addition of more loci. ConclusionThe results show that the high-density SNPs panel significantly enhances the efficacy of distant kinship inference, accurately covering kinship degrees, with the average confidence interval accuracy for first to eighth degree relationships stably above 90%. The study finds that increasing the number of SNPs panel can improve the ability to predict distant kinship.

Animal model research on spleen and stomach diseases in traditional Chinese medicine （TCM） is of great significance for elucidating the nature of diseases and syndromes and for revealing the mechanisms of action of Chinese herbal medicinals. At present， studies on classical TCM syndrome models of spleen and stomach diseases mainly focus on spleen deficiency syndrome， liver constraint syndrome， and damp-heat syndrome. Model construction is mostly based on the etiological and pathophysiological characteristics of syndrome， and model evaluation primarily involves macroscopic manifestations and physicochemical indicators. This paper summarizes the current research status of animal models integrating disease and syndrome for seven common spleen and stomach diseases， including chronic gastritis and gastric precancerous lesions， gastroesophageal reflux disease， functional dyspepsia， inflammatory bowel disease， irritable bowel syndrome， functional constipation， and functional diarrhea. The modeling methods and characteristics of disease-syndrome combined animal models for each disease are analyzed. It is proposed that future research on disease-syndrome integration in spleen and stomach diseases should move toward syste-matic， precise， and integrative development， and that interdisciplinary and cross-disciplinary research approaches should be adopted to enhance the predictive value and application efficiency of disease-syndrome combined animal models.

ObjectiveTo investigate the changes in the expression levels of Yes-associated protein 1 （YAP1） and human collagen type Ⅵ alpha 1 （COL6A1） in pancreatic cancer under hyperglycemic conditions and their association with prognosis. MethodsGEPIA database and R software were used to analyze the expression levels of YAP1 and COL6A1 and their correlation in pancreatic cancer， as well as their impact on the prognosis of patients with pancreatic cancer. The cBioPortal database was used to identify the co-expressed genes of YAP1 and COL6A1 in pancreatic cancer， and the gene ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analyses were performed. Quantitative real-time PCR and Western blot were used to measure the mRNA and protein expression levels of YAP1 and COL6A1 in human pancreatic cancer PANC-1 cells cultured under hyperglycemic or normoglycemic conditions. A nude mouse model of pancreatic cancer with type 2 diabetes was established， and immunohistochemistry was used to measure the expression of YAP1 and COL6A1 in subcutaneous tumor tissue. Clinical specimens and data of 96 patients with pancreatic cancer were collected from January 2016 to January 2020 in the First Affiliated Hospital of Guangxi Medical University， Guangxi Medical University Cancer Hospital and Wuming Hospital Affiliated to Guangxi Medical University. Among them， 48 patients with pancreatic cancer and diabetes were enrolled as study group， and 48 patients with pancreatic cancer alone were enrolled as control group； immunohistochemistry was used to measure the expression levels of YAP1 and COL6A1 in tissue samples from both groups， and their association with clinical indicators and survival outcomes was analyzed. The t-test was used for comparison of continuous data between groups， and the chi-square test or the Mann-Whitney U test was used for comparison of categorical data between groups； the Pearson correlation analysis was used for normally distributed continuous variables， while the Spearman correlation analysis was used for discontinuous variables or non-normally distributed variables. The Kaplan-Meier method was used to plot survival curves， and the Log-rank test was used for comparison of survival rates. The Cox proportional hazards model was used to perform univariate and multivariate analyses. ResultsBoth YAP1 and COL6A1 were highly expressed in pancreatic cancer， with a strong correlation between them （r=0.85， P<0.001）， and they were associated with the poor prognosis of patients （hazard ratio ［HR］=1.8 and 1.5， both P<0.05）. YAP1 and COL6A1 were significantly correlated with 219 and 122 genes， respectively （r=0.83 and 0.86， both P<0.05）， and the GO and KEGG enrichment analyses showed that these genes affected the development and progression of pancreatic cancer through extracellular matrix-receptor interaction， the Hippo signaling pathway， and the phosphatidylinositol 3-kinase/protein kinase B signaling pathway. Quantitative real-time PCR and Western blot showed that there were significant increases in the mRNA and protein expression levels of YAP1 and COL6A1 in human pancreatic cancer PANC-1 cells under hyperglycemic conditions （mRNA expression： t=4.726 and 6.197， both P<0.05； protein expression： t=6.826 and 5.254， both P<0.05）. Immunohistochemistry showed that there were significant increases in the expression levels of YAP1 and COL6A1 in subcutaneous tumor tissue of nude mice， with a positive correlation between them （r=0.985， P<0.001）. For the clinical specimens of pancreatic cancer tissue， immunohistochemistry showed that there were significant increases in the positive expression levels of YAP1 and COL6A1 proteins in the study group， with a positive correlation between them （r=0.882， P<0.001）. The multivariate analysis of clinical data showed that lymph node metastasis （HR=0.083， 95% confidence interval ［CI］： 0.030 — 0.231， P<0.05）， distant organ metastasis （HR=0.166， 95%CI： 0.065 — 0.420， P<0.05）， YAP1 expression level （HR=2.027， 95%CI： 1.065 — 3.857， P<0.05）， and COL6A1 expression level （HR=2.044， 95%CI： 1.019 — 4.099， P<0.05） were independent influencing factors for the prognosis of patients with pancreatic cancer. The survival analysis showed that the high YAP1 expression group had a significantly lower 2-year survival rate than the low YAP1 expression group （6.82% vs 25.00%， χ²=16.382， P<0.001）， and the high COL6A1 expression group had a significantly lower 2-year survival rate than the low COL6A1 expression group （14.58% vs 18.75%， χ²=4.579， P=0.032）. ConclusionThe hyperglycemic environment promotes the high expression of YAP1 and COL6A1 in pancreatic cancer， which is associated with poor prognosis in patients.

Objective To analyze the distribution characteristics of the single nucleotide polymorphism (SNP) site rs76206423 in the promoter region of the interleukin-2 receptor (IL-2R) β gene among Han males in a high radiation background area (HBRA) in Yangjiang City. Methods A total of 48 male participants from Tangkou Town, Yangxi County, Yangjiang City (HBRA group), and 51 male participants from Hengpo Town, Enping City (control group) were selected as the research subjects using the random number table method. Peripheral venous blood samples of participants from both groups were collected, and genomic DNA was extracted. The genotyping and allele frequency distribution of the rs76206423 (A/G) site in the IL-2R β promoter region was detected among the participants in both groups using the SNP detection method. The difference of allele frequencies between population in HBRA group and five area of East Asia, South Asia, Africa, Europe, and the Americas published in the Human Genome Project database from National Center for Biotechnology Information were analyzed. Results The allele frequencies of rs76206423 of population in both groups conformed to Hardy-Weinberg equilibrium (P>0.05). In the HBRA group, the AA genotype was predominant (64.6%), while the AG genotype was the most common in the control group (51.0%), with a significant difference (P<0.05). Population in both groups showed a predominance of the variant allele A (78.1% and 72.5%, respectively), with no significant difference (P>0.05). The frequency of the G allele of rs76206423 in the population in HBRA group was higher than those in South Asian, African, European, and American populations (all P<0.01), but showed no significant difference compared with East Asian populations (P>0.05). Conclusion In the Han male population from the HBRA in Yangjiang City, the rs76206423 site in the IL-2R β gene promoter region is predominantly composed of the wild-type A allele and AA genotype, indicating genetic stability and a relatively high degree of variation at this locus.

OBJECTIVE To investigate the clinical efficacy of integrating pharmacists into family health teams （FHTs） for long-term medication therapeutical management （MTM） in stroke patients， and empirically evaluate the service model. METHODS A pharmacist team， jointly established by clinical and community pharmacists from the Affiliated Suzhou Hospital of Nanjing Medical University （hereinafter referred to as “our hospital”）， developed a pharmacist-supported MTM model integrated into FHTs. Using a prospective randomized controlled design， 170 stroke patients discharged from our hospital （July 2022-December 2023） and enrolled in FHTs at Suzhou Runda Community Hospital were randomly divided into trial group （88 cases） and control group （82 cases） according to random number table. The control group received routine FHTs care （without pharmacist involvement in the team collaboration）， while the trial group xhz8405@126.com received 12-month MTM services supported by pharmacists via an information platform. These services specifically included innovative interventions such as personalized medication regimen optimization based on the MTM framework， dynamic medication adherence management， medication safety monitoring， a home medication assessment system， and distinctive service offerings. Outcomes of the 2 grousp were compared before and after intervention， involving medication adherence （adherence rate， adherence score）， compliance rates for stroke recurrence risk factors [blood pressure， low-density lipoprotein cholesterol （LDL-C）]， and incidence of adverse drug reactions （ADR）. RESULTS After 12 months， the trial group exhibited significantly higher medication adherence rates， improved adherence scores， higher compliance rates for blood pressure and LDL-C targets compared to the control group （P＜0.05）. The incidence of ADR in the trial group （4.55%） was significantly lower than that in the control group （8.11%）， though the difference was not statistically significant （P＞ 0.05）. CONCLUSIONS Pharmacist involvement in FHTs to deliver MTM services significantly enhances medication adherence and optimizes risk factor for stroke recurrence， offering practical evidence for advancing pharmaceutical care in chronic disease management under the family doctor system.

ObjectiveBased on ultra performance liquid chromatography-quadrupole-time-of-flight mass spectrometry（UPLC-Q-TOF-MS/MS）， the combination of serum pharmacochemistry， response profile of absorbed components in serum， network pharmacology and drug-likeness prediction was used to screen the potential active ingredients of Yanghetang against breast cancer. MethodsUPLC-Q-TOF-MS/MS was used to identify the main components in different solvent extracts of Yanghetang， and serum pharmacochemistry was applied to analyze the absorbed components from the serum of female SD rats after 0.5， 1， 2 h of administration. Combined with the response characteristic values of serum drug components obtained from UNIFI 1.8.2， the absorbed prototype components and metabolites were screened to get the absorbed components of Yanghetang with a significant patterns of elimination and growth. Network pharmacology was applied to construct a drug-component-pathway-target-disease network， and molecular docking was performed between absorbed components and key targets of breast cancer， and the drug similarity was analyzed by SwissADME. ResultsForty-two compounds were identified in Yanghetang samples extracted with different solvents， of which 16 compounds were common to the three different extraction solvents（methanol， 50% methanol and water）. The results of drug-containing serum analysis showed that there were 16 absorbed components in serum， including 5 prototypes and 11 metabolites. Network pharmacology results showed that Yanghetang against breast cancer involved 15 key targets such as proto-oncogene tyrosine-protein kinase Src（SRC）， epidermal growth factor receptor（EGFR） and phosphoinositide 3 kinase catalytic alpha polypeptide（PIK3CA）. Molecular docking results showed that 16 potential active ingredients were well combined with the predicted targets. Combined with drug likenesses， 12 compounds in the absorbed components of Yanghetang were considered to have potential for anti-breast cancer activity， mainly including α-pinene and γ-eudesmol and their metabolites， of which one was from Ephedrae Herba， one was from Rehmanniae Radix， and eight were from Cinnamomi Cortex. ConclusionThe chemical components of Yanghetang mainly include polysaccharides， monoterpene glycosides and coumarins， and its prototype components mainly undergo oxidation， hydrolysis and acetylation after entering the blood. Its anti-breast cancer mechanism may be related to the regulation of signaling pathways such as the mitogen-activated protein kinase（MAPK） and phosphatidylinositol 3-kinase/protein kinase B（PI3K/Akt）. The results of this study can lay a foundation for further exploration of Yanghetang in the treatment of breast cancer.