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.

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.

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 explore the correlation between the total cholesterol/high-density lipoprotein cholesterol (TC/HDL-C) ratio during pregnancy and the onset of gestational metabolic syndrome (GMS), as well as its association with pregnancy outcomes.Methods:Clinical data of 153 pregnant women diagnosed with GMS in the third trimester admitted to Women′s Hospital of Nanjing Medical University from January 2020 to December 2022 (observation group) were collected, and data of 153 healthy pregnant women (control group) were randomly selected for a comparative study. Indicators of the two groups were observed and compared. Multiple linear regression and restricted cubic spline analysis were used to analyze the correlation between TC/HDL-C in the first and second trimesters and metabolic indicators in the third trimester and GMS risk, respectively. According to the pregnancy outcomes of GMS pregnant women with different TC/HDL-C ratios, the association between TC/HDL-C ratio and preterm birth subtypes was analyzed.Results:There were statistically significant differences between the two groups in age, gestational week, pre-pregnancy body mass index (BMI), weight gain during pregnancy, abdominal circumference in the third trimester, whether to deliver vaginally, history of preterm birth, history of cesarean section, history of abortion, neonatal birth weight, and neonatal length (all P<0.05). There were statistically significant differences in blood pressure and BMI between the two groups in the second and third trimesters (all P<0.01). In the first trimester (gestational week <14 weeks), there were statistically significant differences in fasting plasma glucose (FPG), HDL-C, and TC/HDL-C ratio between the two groups (all P<0.05). In the second trimester (gestational weeks 14-27) and third trimester (gestational week ≥28 weeks), the levels of FPG, fasting insulin (FINS), triglyceride (TG), TC, low-density lipoprotein cholesterol (LDL-C), and TC/HDL-C ratio in the observation group were higher than those in the control group, while HDL-C was lower (all P<0.05). There were statistically significant differences between the two groups in postpartum abnormal blood pressure, abnormal BMI, abnormal glucose metabolism, and abnormal lipid metabolism (all P<0.05). After adjusting for age, gestational week, and BMI, first-trimester TC/HDL-C ratio was correlated with third-trimester FPG, systolic blood pressure, diastolic blood pressure, TG, TC, and HDL-C (all P<0.05); second-trimester TC/HDL-C ratio was correlated with third-trimester FPG, FINS, systolic blood pressure, diastolic blood pressure, TG, TC, HDL-C, and LDL-C (all P<0.05), and both were linearly related to third-trimester GMS risk ( Pfor trend<0.01). The TC/HDL-C ratio was positively correlated with the preterm birth rate ( RR>1). Conclusions:An increased TC/HDL-C ratio is closely related to the occurrence of GMS, which is prone to cause preterm birth and affect fetal growth and development.

Gastric cancer is one of the most common malignant tumors in the digestive system, characterized by high incidence and mortality rates. In recent years, with the rapid develop-ment of molecular immunology, the application of immune checkpoint inhibitors (ICIs) in neoadju-vant therapy has significantly improved pathological response rates and survival outcomes for patients with resectable locally advanced gastric cancer. The authors systematically review current research progress on combination strategies involving immune checkpoint inhibitors in neoadjuvant therapy for locally advanced gastric cancer, aiming to provide an evidence for optimizing individua-lized therapeutic regimens.

Objective:To investigate the value of nomogram model based on CT features in differentiating ectopic pancreas (EP) from gastrointestinal stromal tumors (GIST) with a long diameter less than 3 cm.Methods:This study was a case-control study. The clinical and imaging data of 43 patients with EP and 90 patients with GIST confirmed by pathology in the Affiliated Hospital of Guizhou Medical University from August 2013 to March 2024 were retrospectively analyzed. Preoperative CT images were analyzed to obtain qualitative features (number of lesions, location, morphology, growth pattern, borders, cystic degeneration, calcification, ulceration, catheter sign, central umbilication) and quantitative features (lesion long diameter, short diameter, long/short diameter, lesion and normal pancreas arterial-phase and venous-phase CT values, and enhancement ratio). Statistical analyses, including independent sample t-tests, Mann-Whitney U tests, χ2 tests, and Fisher exact tests, were performed to compare CT characteristics between the two groups. Binary logistic regression analysis was used to obtain independent predictors to identify the two groups, to establish a joint model, and to draw a nomogram. The discriminative performance of the independent predictors and the combined model was assessed using receiver operating characteristic (ROC) curves, while calibration curves were used to evaluate model fit. Results:The differences in age, location, morphology, border, catheter sign, central umbilication, short diameter, long/short diameter, arteriovenous phase enhancement CT value and arteriovenous phase enhancement ratio were statistically significant between the EP group and the GIST group (all P<0.05). The logistic analysis showed that the differences in age ( OR=0.920, 95% CI 0.885-0.956, P<0.001), border ( OR=5.994, 95% CI 2.111-17.022, P=0.001), long/short diameter ( OR=7.820, 95% CI 1.841-33.224, P=0.005), and venous phase enhancement ratio ( OR=8.847, 95% CI 1.103-70.972, P=0.040) were the independent predictors for distinguishing EP from GIST, and the area under the ROC curve (AUC) were 0.782 (95% CI 0.698-0.866), 0.684 (95% CI 0.600-0.767), 0.705 (95% CI 0.607-0.803), and 0.693 (95% CI 0.605-0.781), respectively. Combined age, border, long diameter/short diameter and venous phase enhancement ratio were plotted in a nomogram with an AUC of 0.881 (95% CI 0.817-0.945), sensitivity and specificity of 74.4% and 93.3%, respectively. The calibration curve demonstrated a strong agreement between predicted and actual probabilities (Hosmer-Lemeschow test, P=0.267). Conclusions:CT imaging reveals significant differences between EP and small GISTs (<3 cm). EP is more likely when patients are younger and lesions exhibit indistinct borders, a higher long-to-short diameter ratio, and greater venous-phase enhancement. The nomogram derived from CT features provides a valuable tool for differentiating EP from GIST.

Objective:To investigate the efficacy and safety of sintilimab combined with the SOX regimen for adjuvant treatment of stage Ⅲgastric cancer after D2 radical resection and to provide a reference for individualized clinical treatment.Methods:The clinical data of 245 pa-tients with stage III gastric cancer who underwent D2 radical resection at the 940th Hospital of the Joint Support Force of the People's Liber-ation Army from June 2019 to May 2022 were retrospectively analyzed.The 180 patients who received only the SOX regimen were desig-nated the control group,and the 65 patients who received sintilimab combined with the SOX regimen were designated the experimental group.The 3-year disease-free survival(DFS)rate,overall survival(OS)rate,and adverse reactions among the two groups and different sub-groups(HER-2 positive,dMMR,CPS≥5)were compared.Results:The 3-year DFS(81.5%vs.59.4%)and OS(84.6%vs.70.6%)rates in the experimental group were significantly higher than those in the control group(both P<0.05).Group analysis showed that in patients with CPS≥5,the 3-year DFS(91.5%vs.67.0%)and OS(95.7%vs.71.6%)rates within the experimental group were significantly better than those in the control group(both P<0.05).Intra-group analysis within the experimental group showed that the 3-year DFS rate(91.5%vs.55.6%)and OS rate(95.7%vs.55.6%)of patients with CPS≥5 were significantly better than those of patients with CPS<5(both P<0.05).The overall and grade≥3 incidences of liver and kidney function damage,thyroid dysfunction,colitis,pneumonia,and rash in the experimental group were higher than those in the control group(all P<0.05),while the differences in other adverse reactions,including leukopenia were not statistic-ally significant(all P>0.05).Conclusions:Sintilimab combined with the SOX regimen can significantly improve 3-year DFS and OS rates in pa-tients with stage Ⅲ gastric cancer after surgery,especially in the CPS≥5 subgroup,with significant benefits and controllable safety.