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.

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.

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.

BACKGROUND:Intertrochanteric fracture of femur has various fracture types and fixation methods,and the mechanical stability of each fixation system is quite different.It is of scientific clinical significance to use finite element analysis method to carry out biomechanical research on various fixation systems. OBJECTIVE:To compare and analyze the mechanical stability of various internal fixations applied to femoral intertrochanteric fracture A031-A2.1 by finite element method. METHODS:Based on the validated finite element model of femur(Intact),the model was cut and made into A031-A2.1 intertrochanteric fracture of femur.Different internal fixation systems were implanted by simulating clinical operation methods,and fixation models of proximal femoral nail antirotation,dynamic hip screw,percutaneous compression plate and proximal femoral locking plate were established respectively.All nodes under the distal femur of the four groups of models were constrained,and compression loads of 700,1 400 and 2 100 N were applied to the femoral head.Von Mises stress distribution and compression stiffness of each group of models were observed through calculation and analysis,and mechanical stability of each group was compared. RESULTS AND CONCLUSION:(1)Through calculation and analysis,after calculating the compression stiffness by comparing the deformation of each model,the compression stiffness of each model under various loads showed the trend:physiological group>proximal femoral nail antirotation group>proximal femoral locking plate group>percutaneous compression plate group>dynamic hip screw group.The compressive stiffness of the complete physiological group model was significantly higher than that of all surgical group models.(2)The stress index was observed.Due to the stress shielding effect,the stress peak value of each fixed group was higher than that of physiological group,and the maximum peak value was concentrated on each internal fixation.Proximal femoral nail antirotation group had the smallest stress peak,while dynamic hip screw group had the highest stress.The stress distribution trend showed physiological group

Objective To explore the effect of visual balance training combined with digital treadmill intervention on walking func-tion in patients with ischemic stroke.Methods From July,2023 to December,2024,90 patients with ischemic stroke in Gansu Provincial Hospital were ran-domly divided into control group(n=30),treadmill group(n=30)and combined group(n=30).All groups re-ceived routine rehabilitation treatment,while the treadmill group added digital treadmill training,and the com-bined group added visual feedback balance training and digital treadmill training,for four weeks.All groups were assessed with Berg Balance Scale(BBS),Pro-Kin visual feedback balance training system,Tinetti Performance-Oriented Mobility Assessment(POMA),digital treadmill system,Timed Up and Go Test(TUGT),Fugl-Meyer Assessment-Lower Extremities(FMA-LE),Functional Ambulation Category(FAC)and modified Barthel Index(MBI)before and after intervention.Results The effects of intra-group(F>147.291,P<0.001),inter-group(F>4.919,P<0.05)and interaction(F>18.386,P<0.001)were all significant for the indicators including BBS score,length trajectory and elliptical area of eyes open or closed,POMA score,step length,hip and knee range of motion on the healthy and affected side,TUGT time,FMA-LE score,and MBI score.Post-hoc tests showed that after treatment,all the above indicators improved in each group(P<0.01),and they were the best in the combined group,followed by the treadmill group(P<0.05).After treatment,the FAC grades improved in all the groups(|Z|>1.971,P<0.05),and it was better in the combined group than in the control group(P<0.01).Conclusion Visual feedback balance training combined with digital treadmill intervention can improve balance function,walking ability and activities of daily living in patients with ischemic stroke,which is more effective than tread-mill training alone.

Hepatocellular carcinoma(HCC),which is essentially primary liver cancer,is closely related to CD8+T cell immune infiltration and immune suppression.We constructed a CD8+T cells related risk score model to pre-dict the prognosis of HCC patients and provided therapeutic guidance based on the risk score.Using integrated bulk RNA sequencing(RNA-seq)and single-cell RNA sequencing(scRNA-seq)datasets,we identified stable CD8+T cell signatures.Based on these signatures,a 3-gene risk score model,comprised of KLRB1,RGS2,and TN-FRSF1B was constructed.The risk score model was well validated through an independent external validation co-hort.We divided patients into high-risk and low-risk groups according to the risk score and compared the differ-ences in immune microenvironment between these two groups.Compared with low-risk patients,high-risk patients have higher M2-type macrophage content(P＜0.0001)and lower CD8+T cells infiltration(P＜0.0001).High-risk patients predict worse response to immunotherapy treatment than low-risk patients(P＜0.01).Drug sensitivity a-nalysis shows that PI3K-β inhibitor AZD6482 and TGFβRII inhibitor SB505124 may be suitable therapies for high-risk patients,while the IGF-1R inhibitor BMS-754807 or the novel pyrimidine-based anti-tumor metabolic drug Gemcitabine could be potential therapeutic choices for low-risk patients.Moreover,expression of these 3-gene mod-el was verified by immunohistochemistry.In summary,the establishment and validation of a CD8+T cell-derived risk model can more accurately predict the prognosis of HCC patients and guide the construction of personalized treatment plans.