ObjectiveThis study aimed to identify factors associated with adenoma recurrence within three years after endoscopic mucosal resection (EMR) and to develop an individualized predictive model.MethodsPatients undergoing their first EMR for colorectal polyps at the Affiliated Hospital of Xuzhou Medical University (September 2018–May 2025) were retrospectively included and randomly divided into training and testing cohorts (7:3). Patients from the Third Affiliated Hospital of Xuzhou Medical University served as the external validation cohort. Least absolute shrinkage and selection operator (LASSO) regression was used to select predictors. Logistic regression (LR), random forest (RF), support vector machine (SVM), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost) models were constructed. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). SHapley additive explanations (SHAP) values were applied to interpret variable contributions, and an online prediction tool was developed.ResultsA total of 1,454 patients were enrolled, of whom 731 developed adenoma recurrence within three years. LASSO identified ten predictors: infection of Helicobacter pylori (H. pylori), the number of adenomas, age, triglyceride level, body mass index (BMI), diarrhea, smoking history, family history, adenoma size, and fecal occult blood positivity. GBM achieved the highest mean AUC in repeated cross-validation (0.813) and maintained robust discrimination in the testing (AUC = 0.818) and external validation cohorts (AUC = 0.775), with good calibration and clinical utility. SHAP analysis identified H. pylori infection, adenoma number, and age as the leading contributors to model predictions.ConclusionGBM demonstrated favorable discrimination, calibration, and clinical utility across validation cohorts, supporting its use for individualized risk stratification. The resulting online calculator may further facilitate risk assessment and inform follow-up strategies in clinical practice.

Objective:To construct and validate a risk prediction model for refeeding syndrome (RFS) in patients with severe acute pancreatitis (SAP), identify high-risk individuals before overt electrolyte abnormalities occur, and provide decision support for the timing of enteral nutrition initiation and early personalized intervention. Methods:A retrospective cohort study was conducted on SAP patients admitted to Xuzhou Medical University Affiliated Hospital (XYFY) between September 2018 and September 2025. Patients were divided into RFS and Non-RFS groups based on the development of RFS after initiating enteral nutrition. Clinical data differences between groups were compared, least absolute shrinkage and selection operator (LASSO) regression was used for feature selection, and six machine learning (ML) algorithms were applied to build prediction models. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curves. SHapley Additive exPlanations (SHAP) analysis was performed to interpret the contribution of key features. Results:Seven predictive features were identified for model construction. The gradient boosting machine (GBM) model exhibited good generalization ability, with area under the curve (AUC) values of 0.851 (95% CI: 0.809-0.894) in the training set and 0.762 (95% CI: 0.672-0.852) in the testing set. Calibration curves confirmed consistency between predicted probabilities and actual outcomes, while decision curves demonstrated favorable net benefits across different clinical decision thresholds. SHAP analysis ranked feature importance as follows: serum potassium (K), serum sodium (Na), serum calcium (Ca), gastrointestinal decompression, blood urea nitrogen (BUN), diabetes mellitus (DM) history, and diuretic use. Conclusion:The GBM model effectively predicts RFS risk in SAP patients after initiating enteral nutrition.

Objective:To explore the mediating effect of nighttime sleep duration between digestive diseases and depressive symptoms among middle-aged and elderly adults, so as to guide clinical intervention and treatment of depression related to digestive diseases. Methods:Based on the data of the China Health and Retirement Longitudinal Study (CHARLS) from 2015 to 2020, difference analysis, Spearman correlation analysis, and multivariate logistic regression were conducted to analyze the relationships among digestive diseases, nighttime sleep duration, and depressive symptoms. The mediating effect model was established and tested by the Bootstrap method. Results:The prevalence rate of digestive diseases was 23.05%, and the detection rate of depressive symptoms was 35.90%. Correlation and regression analysis indicated that digestive diseases (OR = 1.62, 95% CI: 1.45-1.81) and insufficient nighttime sleep (OR = 2.22, 95% CI: 2.01-2.45) significantly impacted depressive symptoms. The results of the mediating effect model showed that insufficient nighttime sleep had a partial mediating effect between digestive diseases and depressive symptoms among middle-aged and elderly adults. Conclusion:Digestive diseases can lead to insufficient nighttime sleep and promote the occurrence of depressive symptoms among middle-aged and elderly adults. Therefore, in clinical practice, when treating digestive patients with comorbid depression, the quality and duration of their nighttime sleep should not be ignored. Improving sleep conditions is expected to alleviate depressive symptoms.

Supplementary Figure 7. BFAR promotes exhaustion and inactivation of T cells dependent on YBX1

Sequences of shRNA/siRNA for specific gene knockdown

The mechanism underlying the role of trefoil factor family 3 (TFF3) in intestinal metaplasia remains unclear. This study reveals the molecular mechanism by which TFF3, in the process of gastric mucosal epithelial cell intestinal metaplasia (IM) induced by high salt, activates the JAK2/STAT3/CDX2 pathway, providing a potential target for the occurrence of IM. An in vitro model of high salt-induced intestinal metaplasia was established using bioinformatics to screen the GEO dataset for significantly differentially expressed genes related to intestinal metaplasia. The gastric epithelial cell line GES-1 was cultured in high-salt medium, and changes in cell function and the expression of TFF3, JAK2, STAT3, and CDX2 were examined following TFF3 knockdown or overexpression. Subsequent experiments disrupted the TFF3-JAK2/STAT3-CDX2 pathway to assess its effects on gene expression and cell function. The expression of TFF3 is upregulated during intestinal metaplasia, which promotes cell proliferation and migration. TFF3 regulates the expression of JAK2, STAT3, and CDX2 and activates the JAK2/STAT3 pathway to induce CDX2 expression in gastric epithelial cells, leading to intestinal metaplasia. Functional assays revealed that the TFF3-JAK2/STAT3-CDX2 pathway enhances both cell proliferation and migration. TFF3 induces intestinal metaplasia in gastric epithelial cells through the JAK2/STAT3-CDX2 pathway, providing new insights into the underlying mechanism and therapeutic strategies for intestinal metaplasia.

Primers used for plasmid construction

Supplementary Figure 6. BFAR is not ubiquitinated by PRP19

Supplementary Figure 2. BFAR drives gastrointestinal tumorigenesis

Supplementary Figure 3. Infiltration of macrophages and T cells is independent of BFAR

Supplementary Figure 8. BFAR is a inhibitory factor to anti-PD1 therapy

Backgrounds: As the population ages, the relationship among digestive diseases, nighttime sleep duration, and depressive symptoms in middle-aged and elderly adults, especially sleep's mediating role, remains unclear, thus prompting research. Aims To explore how nighttime sleep duration mediates between digestive diseases and depressive symptoms in this group, aiming to guide clinical treatment of related depression. Methods Using 2015–2020 China Health and Retirement Longitudinal Study (CHARLS) data, we applied difference, Spearman correlation, and multivariate logistic regression analyses. A mediating effect model was set up and tested via the Bootstrap method. Results Digestive diseases had a 23.05% prevalence rate, and depressive symptoms had a 35.90% detection rate. Both digestive diseases (OR = 1.55, 95% CI: 1.38–1.73) and insufficient nighttime sleep (OR = 2.17, 95% CI: 1.97–2.40) significantly affected depressive symptoms. The model showed that insufficient nighttime sleep had a partial mediating effect. Discussion Clinically, treating relevant patients requires a holistic approach with an emphasis on sleep. Future research should use objective measures and longitudinal studies to clarify mechanisms and causalities. Conclusion Digestive diseases can cause insufficient sleep, promoting depressive symptoms. Clinicians should not overlook sleep quality when treating digestive patients with comorbid depression, as improving sleep may relieve symptoms.

Supplementary Figure 4. Bfar knockdown restrains the neutrophil chemokines expressions

This study aims to assess the serum levels of pepsinogen (PG)I, PG II, and gastrin (G17) in patients with gastric intestinal metaplasia (GIM) and evaluate their correlation with demographic characteristics. A total of 247 normal controls (NC) and 240 patients diagnosed with GIM were enrolled in this study. All participants underwent a gastroscopy procedure followed by pathological examination for diagnosis confirmation. The expression level of PGI, PG II, and G 17 was detected by fluorescence immunochromatography and Hp infection was detected by 13-carbon breath test. The demographic characteristics of the subjects were obtained through questionnaires. Compared to the NC group, the GIM group showed a reduction in PG II expression level [10.71(6.40,16.89) VS 9.21(6.14,14.55), p = 0.010]. GIM patients had a higher prevalence of previous Hp eradication history (14.98

OBJECTIVE:This study aims to develop and validate a model for predicting the 1-year recurrence of adenomatous polyps following endoscopic mucosal resection (EMR), and explore associated risk factors. METHODS:Patients who underwent their first EMR for colorectal polyps at the Affiliated Hospital of Xuzhou Medical University from September 2018 to September 2023 were retrospectively enrolled. The dataset was randomly divided into training and testing sets at a ratio of 7:3. Additional patient data from October 2023 to April 2025 from the same center were utilized as the internal validation set, while an external validation set was obtained from Xuzhou Central Hospital. Feature variables were selected via least absolute shrinkage and selection operator (LASSO) regression. Five machine learning (ML) algorithms, including logistic regression (LR), random forest (RF), support vector machine (SVM), gradient boosting machine (GBM), and eXtreme gradient boosting (XGBoost), were used to build predictive models. Model performance was examined via receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Feature importance was interpreted via SHapley Additive exPlanations (SHAP). RESULTS:According to LASSO regression, 10 predictive variables were identified. The performance of the GBM model was the highest, with an area under the curve (AUC) of 0.824 (95 % CI: 0.764-0.884) in the testing set, 0.793 (95 % CI: 0.718-0.868) in the internal validation set, and 0.800 (95 % CI: 0.725-0.875) in the external validation set. Calibration curves indicated close agreement between predicted and observed results. Decision curve analysis demonstrated a satisfactory net benefit across a wide spectrum of threshold probabilities. CONCLUSION:The ML-based model developed in this study accurately predicts the short-term recurrence of adenomatous polyps after EMR and may assist clinicians in personalizing follow-up strategies.

The immunosuppressive tumor microenvironment remains a major barrier to effective immunotherapy in gastric cancer. In this study, we identified the E3 ubiquitin ligase BFAR as a critical regulator of neutrophil-mediated immune evasion through the S100A8/A9–BFAR–PRP19–YBX1 signaling axis. Multiomics analyses revealed that BFAR is overexpressed in gastric cancer and correlates with poor prognosis. Functional studies demonstrated that BFAR knockdown suppressed tumor growth by reducing neutrophil infiltration and immunosuppressive reprogramming to restore CD8+ T-cell function. Mechanistically, BFAR mediated K48-linked ubiquitination and degradation of PRP19, leading to stabilization of the oncoprotein YBX1, which transcriptionally upregulated neutrophil-recruiting chemokines CXCL1/CXCL3. Infiltrating neutrophils secreted S100A8/A9, which activated NF-κB to induce BFAR expression in tumor cells and created a feed-forward loop that sustains an immunosuppressive tumor microenvironment. Furthermore, BFAR promoted neutrophil PD-L1 expression via GM-CSF, reinforcing T-cell exhaustion. Clinically, BFAR expression correlated with neutrophil infiltration and poor response to anti–PD-1 therapy, whereas its inhibition synergizes with immune checkpoint blockade in preclinical models. Our work unveils BFAR as a central orchestrator of neutrophil-driven immunosuppression and proposes targeting this axis to enhance immunotherapy efficacy in gastric cancer.

BACKGROUND:Colorectal polyps are precancerous diseases of colorectal cancer. Early detection and resection of colorectal polyps can effectively reduce the mortality of colorectal cancer. Endoscopic mucosal resection (EMR) is a common polypectomy procedure in clinical practice, but it has a high postoperative recurrence rate. Currently, there is no predictive model for the recurrence of colorectal polyps after EMR. AIM:To construct and validate a machine learning (ML) model for predicting the risk of colorectal polyp recurrence one year after EMR. METHODS:This study retrospectively collected data from 1694 patients at three medical centers in Xuzhou. Additionally, a total of 166 patients were collected to form a prospective validation set. Feature variable screening was conducted using univariate and multivariate logistic regression analyses, and five ML algorithms were used to construct the predictive models. The optimal models were evaluated based on different performance metrics. Decision curve analysis (DCA) and SHapley Additive exPlanation (SHAP) analysis were performed to assess clinical applicability and predictor importance. RESULTS:Multivariate logistic regression analysis identified 8 independent risk factors for colorectal polyp recurrence one year after EMR (P < 0.05). Among the models, eXtreme Gradient Boosting (XGBoost) demonstrated the highest area under the curve (AUC) in the training set, internal validation set, and prospective validation set, with AUCs of 0.909 (95%CI: 0.89-0.92), 0.921 (95%CI: 0.90-0.94), and 0.963 (95%CI: 0.94-0.99), respectively. DCA indicated favorable clinical utility for the XGBoost model. SHAP analysis identified smoking history, family history, and age as the top three most important predictors in the model. CONCLUSION:The XGBoost model has the best predictive performance and can assist clinicians in providing individualized colonoscopy follow-up recommendations.

Background and Objectives: The pathogenesis of irritable bowel syndrome with diarrhea (IBS-D) is not fully clear. This study aims to explore the underlying disease mechanisms of IBS-D using bioinformatics approaches. Methods: Raw sequencing data related to IBS-D were downloaded from the GEO database (datasets GSE14841, GSE36701, GSE146853, and GSE166869), followed by differential gene expression analysis using R. Disease-related genes associated with IBS-D were identified from five databases: Genecards, Disgenet, TTD, Drugbank, and OMIM. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and protein-protein interaction (PPI) analysis were performed on the identified genes. Further disease correlation analysis of key genes was conducted using the Comparative Toxicogenomics Database (CTD). Results: A total of 481 differentially expressed genes (305 upregulated, 176 downregulated) were identified from the GEO datasets. Enrichment analysis suggested that the pathogenesis of IBS-D may involve multiple molecular mechanisms, including immune response, chromatin remodeling, and viral infections. Across the disease databases, 228 IBS-D-related genes were found (Genecards: 222, Drugbank: 6). GEO dataset and the disease database had a total of 4 intersecting genes (CCL2, MUC1, CASR, PRKCA). Related disease analysis of the key 4 genes by CTD database revealed that IBS-D was associated with Chemical and Drug Induced Liver Injury, Fatty Liver, Hepatomegaly, Liver Cirrhosis, Liver Diseases, Liver Neoplasms existed in correlation. Conclusion: CCL2, MUC1, CASR and PRKCA may be involved in the pathological process of IBS-D through IL-17 signaling pathway and bile secretion regulation, and their co-occurrence with liver diseases in the CTD database suggests that the mechanism of intestinal-hepatic interactions is worth exploring in depth.

Primers used for real-time quantitative PCR

Supplementary Figure 5. BFAR induces exhaustion and inactivation of T cells dependent on its RING domain