As one of the common chronic bacterial infections, Helicobacter pylori (Hp) infection is a key pathogenic factor of chronic gastritis, peptic ulcer and gastric cancer. In the formation of gastric ulcer, Hp virulence factors directly damage the gastric mucosal barrier, induce the local inflammation and immune response, interfere with the regulation of gastric acid secretion, and ultimately destroy the mucosal stability. In the occurrence of gastric cancer, Hp-driven chronic inflammation is the basis of inflammation-cancer transformation, and it induces the genomic instability and epigenetic changes by interfering with key signaling pathways in host cells, thereby promoting malignant transformation. This paper systematically reviews the related mechanisms of Hp-induced gastric ulcer and gastric cancer in recent years, holding great significance for the development of new intervention strategies and the precise prevention and treatment of gastric cancer.

Objective To observe the value of pituitary radiomics and MRI features combined model for predicting growth hormone(GH)status in pediatric short stature.Methods Totally 300 children with short stature were enrolled as training set,while other 73 cases were taken as external validation set.Based on growth hormone stimulation test,the children were divided into GH deficiency(GHD)group(n=228)and non-GHD group(n=145).The training set included 196 cases in GHD subgroup and 104 cases in non-GHD subgroup,while the validation set included 32 cases in GHD subgroup and 41 cases in non-GHD subgroup.Radiomics features of pituitary were extracted from T1WI.The key features were selected using least absolute shrinkage and selection operator(LASSO)regression,and machine learning models were subsequently constructed using support vector machine(SVM),logistic regression(LR),naive Bayes(NB)and K-nearest neighbor(KNN),respectively.Then combined models were constructed combining with MRI features,and the efficacy of each model was evaluated.Results The area under the curve(AUC)of SVM,LR,NB,and KNN radiomics model for predicting GH status in pediatric short stature was 0.860,0.831,0.838 and 0.901 in training set,0.788,0.829,0.823 and 0.770 in validation set,while of the relative combined SVM,LR,NB and KNN model was 0.924,0.903,0.859 and 0.920 in training set,and 0.827,0.881,0.836 and 0.718 in validation set.LRcombined model had the best overall performance,with sensitivity of 84.94％,specificity of 80.56％and accuracy of 83.61％in training set,and 80.95％,72.22％and 80.00％in validation set,respectively.Conclusion Pituitary radiomics and MRI features combined model could effectively predict GH status in pediatric short stature.

Objective:To investigate the metabolic pathways and potential molecular targets associated with dapagliflozin in the treatment of type 2 diabetes mellitus.Methods:Plasma samples from patients with type 2 diabetes mellitus were collected before and after 12 months of dapagliflozin treatment and analyzed using UPLC-VION IMS Q-Tof-based metabolomics and timsTOF Pro2 diaPASEF-based proteomics. Multivariate statistical analyses were performed to identify significant differences pre- and post-treatment. Correlation analysis was then conducted to assess relationships between differentially expressed metabolites and proteins closely associated with type 2 diabetes mellitus. Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analyses were used to construct metabolic pathway maps and predict therapeutic targets.Results:After 12 months of dapagliflozin treatment, 162 differential metabolites were identified, with 59 upregulated and 103 downregulated. A total of 440 differentially expressed proteins were detected, of which 272 were upregulated and 168 were downregulated. The main classes of differential metabolites included sphingolipids, glycerophospholipids, and glycosphingolipids. Key differentially expressed proteins included importin subunit alpha-11, synemin, Janus kinase 1, and far upstream element-binding protein 2. Correlation analysis revealed 98 shared enriched pathways between differential metabolites and proteins, involving neurotrophin signaling, chemokine signaling, and B cell receptor signaling pathways. Metabolic pathway analysis suggested that dapagliflozin might regulate insulin secretion by modulating glucose-dependent insulinotropic polypeptide, calmodulin-dependent protein kinase, and diacylglycerol levels.Conclusion:Dapagliflozin may exert therapeutic effects in type 2 diabetes mellitus through multiple mechanisms, including the modulation of metabolic and proteomic profiles, participation in key cellular signaling pathways, and regulation of insulin secretion.

OBJECTIVES: To investigate the association of serum albumin level after human albumin infusion with 28-day mortality in critically ill patients with acute kidney injury (AKI) and its impact on 90-day outcomes of the patients. METHODS: We conducted a retrospective cohort study based on the MIMIC IV database (2008-2019), including 5918 AKI patients treated with albumin in the ICU. Based on serum albumin levels within 72 h after albumin infusion, the patients were divided into low (<30 g/L), medium (30-35 g/L), and high albumin (>35 g/L) groups. Restricted cubic spline regression and multivariate logistic regression were used to analyze the association of albumin levels with patient mortality, and the results were verified in a external validation cohort consisting of 110 sepsis-induced AKI patients treated in Nanfang Hospital between 2017 and 2022 using survival analysis and multivariate adjustment. RESULTS: In the MIMIC training cohort, multivariate logistic regression showed no significant differences in 28-day mortality of the patients with different albumin levels (P>0.05). However, restricted cubic spline analysis indicated a non-linear dose-response relationship between albumin levels and 28-day mortality (threshold effect: risk increased when albumin levels >3.6 g/dL). Secondary endpoint analysis revealed that the patients with high albumin levels had a shorter duration of mechanical ventilation (P<0.001) but a longer ICU stay (P<0.001). In the validation cohort, albumin levels ≥30 g/L were significantly associated with a reduced 28-day mortality rate (P<0.05). CONCLUSIONS The association between increased serum albumin levels following albumin infusion and 28-day mortality of critically ill patients with AKI exhibits a cohort dependency and can be influenced by multiple factors including disease type and severity, infusion strategies, and statistical methods.
Humans ; Acute Kidney Injury/therapy* ; Critical Illness/mortality* ; Retrospective Studies ; Serum Albumin/analysis* ; Male ; Female ; Intensive Care Units ; Middle Aged ; Logistic Models ; Aged

This study aimed to comprehensively examine the association of gallstones, cholecystectomy, and cancer risk. Multivariable logistic regressions were performed to estimate the observational associations of gallstones and cholecystectomy with cancer risk, using data from a nationwide cohort involving 239 799 participants. General and gender-specific two-sample Mendelian randomization (MR) analysis was further conducted to assess the causalities of the observed associations. Observationally, a history of gallstones without cholecystectomy was associated with a high risk of stomach cancer (adjusted odds ratio (aOR)=2.54, 95% confidence interval (CI) 1.50-4.28), liver and bile duct cancer (aOR=2.46, 95% CI 1.17-5.16), kidney cancer (aOR=2.04, 95% CI 1.05-3.94), and bladder cancer (aOR=2.23, 95% CI 1.01-5.13) in the general population, as well as cervical cancer (aOR=1.69, 95% CI 1.12-2.56) in women. Moreover, cholecystectomy was associated with high odds of stomach cancer (aOR=2.41, 95% CI 1.29-4.49), colorectal cancer (aOR=1.83, 95% CI 1.18-2.85), and cancer of liver and bile duct (aOR=2.58, 95% CI 1.11-6.02). MR analysis only supported the causal effect of gallstones on stomach, liver and bile duct, kidney, and bladder cancer. This study added evidence to the causal effect of gallstones on stomach, liver and bile duct, kidney, and bladder cancer, highlighting the importance of cancer screening in individuals with gallstones.
Humans ; Mendelian Randomization Analysis ; Gallstones/complications* ; Female ; Male ; Cholecystectomy/statistics & numerical data* ; Middle Aged ; Risk Factors ; Aged ; Adult ; Neoplasms/etiology* ; Stomach Neoplasms/epidemiology*

Objective To observe the value of pituitary radiomics and MRI features combined model for predicting growth hormone(GH)status in pediatric short stature.Methods Totally 300 children with short stature were enrolled as training set,while other 73 cases were taken as external validation set.Based on growth hormone stimulation test,the children were divided into GH deficiency(GHD)group(n=228)and non-GHD group(n=145).The training set included 196 cases in GHD subgroup and 104 cases in non-GHD subgroup,while the validation set included 32 cases in GHD subgroup and 41 cases in non-GHD subgroup.Radiomics features of pituitary were extracted from T1WI.The key features were selected using least absolute shrinkage and selection operator(LASSO)regression,and machine learning models were subsequently constructed using support vector machine(SVM),logistic regression(LR),naive Bayes(NB)and K-nearest neighbor(KNN),respectively.Then combined models were constructed combining with MRI features,and the efficacy of each model was evaluated.Results The area under the curve(AUC)of SVM,LR,NB,and KNN radiomics model for predicting GH status in pediatric short stature was 0.860,0.831,0.838 and 0.901 in training set,0.788,0.829,0.823 and 0.770 in validation set,while of the relative combined SVM,LR,NB and KNN model was 0.924,0.903,0.859 and 0.920 in training set,and 0.827,0.881,0.836 and 0.718 in validation set.LRcombined model had the best overall performance,with sensitivity of 84.94％,specificity of 80.56％and accuracy of 83.61％in training set,and 80.95％,72.22％and 80.00％in validation set,respectively.Conclusion Pituitary radiomics and MRI features combined model could effectively predict GH status in pediatric short stature.

Objective:To investigate the metabolic pathways and potential molecular targets associated with dapagliflozin in the treatment of type 2 diabetes mellitus.Methods:Plasma samples from patients with type 2 diabetes mellitus were collected before and after 12 months of dapagliflozin treatment and analyzed using UPLC-VION IMS Q-Tof-based metabolomics and timsTOF Pro2 diaPASEF-based proteomics. Multivariate statistical analyses were performed to identify significant differences pre- and post-treatment. Correlation analysis was then conducted to assess relationships between differentially expressed metabolites and proteins closely associated with type 2 diabetes mellitus. Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analyses were used to construct metabolic pathway maps and predict therapeutic targets.Results:After 12 months of dapagliflozin treatment, 162 differential metabolites were identified, with 59 upregulated and 103 downregulated. A total of 440 differentially expressed proteins were detected, of which 272 were upregulated and 168 were downregulated. The main classes of differential metabolites included sphingolipids, glycerophospholipids, and glycosphingolipids. Key differentially expressed proteins included importin subunit alpha-11, synemin, Janus kinase 1, and far upstream element-binding protein 2. Correlation analysis revealed 98 shared enriched pathways between differential metabolites and proteins, involving neurotrophin signaling, chemokine signaling, and B cell receptor signaling pathways. Metabolic pathway analysis suggested that dapagliflozin might regulate insulin secretion by modulating glucose-dependent insulinotropic polypeptide, calmodulin-dependent protein kinase, and diacylglycerol levels.Conclusion:Dapagliflozin may exert therapeutic effects in type 2 diabetes mellitus through multiple mechanisms, including the modulation of metabolic and proteomic profiles, participation in key cellular signaling pathways, and regulation of insulin secretion.

Background/Aims: Low educational attainment is a well-established risk factor for nonalcoholic fatty liver disease (NAFLD) in developed areas. However, the association between educational attainment and the risk of NAFLD is less clear in China. Methods: A cross-sectional study including over 200,000 Chinese adults across mainland China was conducted. Information on education level and lifestyle factors were obtained through standard questionnaires, while NAFLD and advanced fibrosis were diagnosed using validated formulas. Outcomes included the risk of NAFLD in the general population and high probability of fibrosis among patients with NAFLD. Logistic regression analysis was employed to estimate the risk of NAFLD and fibrosis across education levels. A causal mediation model was used to explore the potential mediators. Results: Comparing with those receiving primary school education, the multi-adjusted odds ratios (95% confidence intervals) for NAFLD were 1.28 (1.16 to 1.41) for men and 0.94 (0.89 to 0.99) for women with college education after accounting for body mass index. When considering waist circumference, the odds ratios (95% CIs) were 0.94 (0.86 to 1.04) for men and 0.88 (0.80 to 0.97) for women, respectively. The proportions mediated by general and central obesity were 51.00% and 68.04% for men, while for women the proportions were 48.58% and 32.58%, respectively. Furthermore, NAFLD patients with lower educational attainment showed an incremental increased risk of advanced fibrosis in both genders. Conclusions In China, a low education level was associated with a higher risk of prevalent NAFLD in women, as well as high probability of fibrosis in both genders.