Haixing Jiang

Gastroesophageal reflux disease (GERD) is a common gastrointestinal disorder that severely reduces quality of life. Emerging data suggest that neuroticism, a personality trait associated with increased stress reactivity and sensitivity to physiological sensations, may contribute to GERD susceptibility. However, the causality remains unknown. A bidirectional Mendelian Randomization (MR) study was conducted to evaluate the causal link between neuroticism and GERD. Genetic data for neuroticism were sourced from the UK Biobank’s genome-wide association study (GWAS) (n = 3,80,506), while GERD-associated genetic data were obtained from the FinnGen project (n = 4,17,314) for primary analysis and from the IEU Open GWAS database (n = 6,02,604) for replication and meta-analysis. Sensitivity analyses, including tests for heterogeneity and pleiotropy, were conducted to assess the robustness of causal inferences. Inverse-variance weighted (IVW) analyses revealed significant associations between certain neuroticism traits and an increased risk of GERD. The traits with the strongest associations were mood (odds ratio [OR] = 2.01, 95% confidence interval [CI] = 1.60, 2.53, P  = 1.5 × 10 ^–9 ), worry (OR = 1.59, 95% CI = 1.21, 2.07, P = .00078), and tense (OR = 1.70, 95% CI = 1.18, 2.44, P  = .004). Weighted median and MR-Egger regression sensitivity analyses supported these findings. The results were validated in replication and meta-analysis. Reverse MR suggested GERD might influence traits like “WORRY” and “FED-UP”; however, these associations were non-significant after Bonferroni correction. Neuroticism traits, including mood, worry, and tense, may be associated with an increased risk of GERD. These findings highlight the potential role of psychological characteristics in GERD development and may provide insights into future risk assessment and management strategies for GERD.

Purpose: Rapid progression in late‐stage is a characteristic of pancreatic cancer (PC), leading to mortality. The critical role of lncRNA A2M‐AS1 (long non‐coding RNA alpha‐2‐macroglobulin antisense RNA 1) is involved in cancer progression, but the upstream regulator of A2M‐AS1 in the PC progression phenotype remains elusive. Methods: We conducted an integrated analysis using bioinformatics, in vitro experiments, and in vivo studies. Human PC tissues were analyzed for A2M‐AS1 and p53 expressions. The PANC‐1 and BxPC‐3 cell lines were used for functional assays, including cell proliferation, apoptosis, migration, and invasion assays. The role of p53 in regulating A2M‐AS1 was investigated through overexpression and knockdown studies, along with using a MAPK pathway inhibitor. Results: We found that A2M‐AS1 is downregulated in PC tissues and that its high expression correlates with a better prognosis. p53 was identified as a negative regulator of A2M‐AS1, with its knockdown leading to increased A2M‐AS1 expression and decreased PC cell invasiveness. Mechanistically, p53 was shown to bind to the A2M‐AS1 promoter, modulating its transcriptional activity. The MAPK pathway was revealed as a downstream effector of the p53‐A2M‐AS1 axis, with its inhibition reversing the effects on PC cell behavior. Conclusion: High A2M‐AS1 expression is associated with a better PC prognosis. A2M‐AS1 overexpression subdues PC cell development. Our study unveils a new mechanism by which p53 decreases A2M‐AS1 expression.

Nonalcoholic fatty liver disease (NAFLD) is a chronic liver condition with a steadily increasing prevalence. Evidence indicates that both smoking and obesity are significant risk factors for NAFLD, yet the extent to which smoking influences NAFLD through weight gain remains unclear. This study aimed to dissect the intricate relationship between smoking, body mass index (BMI), and NAFLD using Mendelian randomization (MR) analysis. We leveraged data from 30 genome-wide association studies involving over 1.2 million individuals, from which 123 single nucleotide polymorphisms were selected as instrumental variables for smoking. BMI data were sourced from the Genetic Investigation of Anthropometric Traits (GIANT) consortium, encompassing more than 700,000 individuals, with 521 single nucleotide polymorphisms serving as instrumental variables. NAFLD data were obtained from multiple databases, including the eMERGE Network, UK Biobank, Estonian Biobank, and FinnGen, comprising 8434 cases and 770,180 controls. All participants in this study were of European ancestry. We first applied univariate MR analysis to assess the causal relationship between smoking, NAFLD, and BMI. Subsequently, multivariate MR was used to assess the effect of smoking on NAFLD after adjusting for BMI. The coefficient product method was used to calculate the mediating effect of BMI. Results found that both smoking and high BMI were able to increase the risk of NAFLD, with odds ratios of 1.83 (95% confidence interval [CI]: 1.31–2.55) and 1.58 (95% CI: 1.42–1.77), respectively. BMI mediated 73.3% (95% CI: 62.3%–80.5%) of the effect of smoking on NAFLD. The findings support weight control and the encouragement of smoking cessation, especially in obese populations, as strategies to reduce the risk of NAFLD.

Objectives To establish and validate a model based on CT imaging during follow-ups for predicting the disease progression in ileal stricturing Crohn’s disease (CD). Methods Between January 2014 and February 2024, a retrospective review was conducted on 71 patients (training, n = 49; test, n = 22) who were initially diagnosed with ileal stricturing CD. Disease progression referred to the development of penetrating diseases, the requirement for CD-related hospitalization or surgery during follow-up. Radiomics features were extracted from visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) on baseline and follow-up CT scans, respectively. Integrating clinical characteristics and body composition features, a novel CT-based delta-radiomics nomogram was established according to multivariate Cox stepwise regression analysis. Receiver operating characteristic (ROC) analysis was performed to assess diagnostic performance. Results The delta-VAT radiomics model (RM) exhibited satisfactory performance in training cohort (the area under the ROC curve [AUC] = 0.792, 95% confidence Interval [CI] 0.666–0.917) and in test cohort (AUC = 0.640, 95% CI 0.411–0.870). The AUCs of the delta-SAT RM were 0.777 (95% CI 0.648–0.907) in training cohort and 0.612 (95% CI 0.377–0.846) in test cohort. The combined nomogram model showed good discrimination for predicting disease progression, with a C-index of 0.808 and 0.702 in the training and test cohorts, respectively. Conclusions We first constructed a comprehensive model incorporating delta-adipose radiomics, baseline neutrophil-to-lymphocyte ratio (NLR) level and the application of biological therapy to predict progression in ileal stricturing CD, which aids in the timely adjustment of therapeutic strategies and enhances patients’ quality of life.

Resveratrol alleviates liver fibrosis in mice by upregulating IL‐10 to reprogram the macrophage phenotype; however, the mechanism remains to be elucidated. Building on our previous work, in this study, we aimed to determine the role of the TLR2/MyD88/ERK and NF‐κB/NLRP3 inflammasome pathways in mediating the effects of resveratrol on liver fibrosis and macrophage polarization. We investigated the expression of cytokines in these inflammasome pathways in a mouse model of liver fibrosis and resveratrol‐treated macrophages. The results showed that expression of TLR2, MyD88, ERK, and NF‐κB1 in liver tissues was increased in the fourth week after treatment with resveratrol but decreased in the fifth week. Similar results were obtained for the NF‐κB/NLRP3 inflammasome pathway. The results also showed that cytokines in both the TLR2/MyD88/ERK and NF‐κB/NLRP3 inflammasome pathways in macrophages were elevated after 24 h and reduced after 36 h of treatment with resveratrol. Immunofluorescence and nucleocytoplasmic separation assays showed that resveratrol inhibited the translocation of NF‐κB from the cytoplasm to the nucleus in macrophages, and increased NF‐κB1 was associated with inhibition of the TLR2/MyD88/ERK pathway. Silencing NF‐κB1 increased the expression of TLR2, MyD88, and ERK. In conclusion, the TLR2/MyD88/ERK and NF‐κB/NLRP3 inflammasome pathways are involved in the effect of resveratrol on macrophage polarization and the subsequent modulation of liver fibrosis. NF‐κB1 acts as the common cytokine that coordinates the crosstalk between the two pathways. Our findings highlight the potential of the members of these pathways as therapeutic targets toward the treatment of liver fibrosis.

Objectives: This study aimed to develop and validate intratumoral, peritumoral, and combined radiomic models based on endoscopic ultrasonography (EUS) for retrospectively differentiating pancreatic neuroendocrine tumors (PNETs) from pancreatic cancer.Methods: A total of 257 patients, including 151 with pancreatic cancer and 106 with PNETs, were retroactively enrolled after confirmation through pathological examination. These patients were randomized to either the training or test cohort in a ratio of 7:3. Radiomic features were extracted from the intratumoral and peritumoral regions from conventional EUS images. Following this, the radiomic features underwent dimensionality reduction through the utilization of the least absolute shrinkage and selection operator (LASSO) algorithm. Six machine learning algorithms were utilized to train prediction models employing features with nonzero coefficients. The optimum intratumoral radiomic model was identified and subsequently employed for further analysis. Furthermore, a combined radiomic model integrating both intratumoral and peritumoral radiomic features was established and assessed based on the same machine learning algorithm. Finally, a nomogram was constructed, integrating clinical signature and combined radiomics model.Results: 107 radiomic features were extracted from EUS and only those with nonzero coefficients were kept. Among the six radiomic models, the support vector machine (SVM) model had the highest performance with AUCs of 0.853 in the training cohort and 0.755 in the test cohort. A peritumoral radiomic model was developed and assessed, achieving an AUC of 0.841 in the training and 0.785 in the test cohorts. The amalgamated model, incorporating intratumoral and peritumoral radiomic features, exhibited superior predictive accuracy in both the training (AUC=0.861) and test (AUC=0.822) cohorts. These findings were validated using the Delong test. The calibration and decision curve analyses (DCA) of the combined radiomic model displayed exceptional accuracy and provided the greatest net benefit for clinical decision-making when compared to other models. Finally, the nomogram also achieved an excellent performance.Conclusions: An efficient and accurate EUS-based radiomic model incorporating intratumoral and peritumoral radiomic features was proposed and validated to accurately distinguish PNETs from pancreatic cancer. This research has the potential to offer novel perspectives on enhancing the clinical utility of EUS in the prediction of PNETs.

To retrospectively develop and validate an interpretable deep learning model and nomogram utilizing endoscopic ultrasound (EUS) images to predict pancreatic neuroendocrine tumors (PNETs). Following confirmation via pathological examination, a retrospective analysis was performed on a cohort of 266 patients, comprising 115 individuals diagnosed with PNETs and 151 with pancreatic cancer. These patients were randomly assigned to the training or test group in a 7:3 ratio. The least absolute shrinkage and selection operator algorithm was employed to reduce the dimensionality of deep learning (DL) features extracted from pre-standardized EUS images. The retained nonzero coefficient features were subsequently applied to develop predictive eight DL models based on distinct machine learning algorithms. The optimal DL model was identified and used to establish a clinical signature, which subsequently informed the construction and evaluation of a nomogram. Gradient-weighted Class Activation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP) were implemented to interpret and visualize the model outputs. A total of 2048 DL features were initially extracted, from which only 27 features with coefficients greater than zero were retained. The support vector machine (SVM) DL model demonstrated exceptional performance, achieving area under the curve (AUC) values of 0.948 and 0.795 in the training and test groups, respectively. Additionally, a nomogram was developed, incorporating both DL and clinical signatures, and was visually represented for practical application. Finally, the calibration curves, decision curve analysis (DCA) plots, and clinical impact curves (CIC) exhibited by the DL model and nomogram indicated high accuracy. The application of Grad-CAM and SHAP enhanced the interpretability of these models. These methodologies contributed substantial net benefits to clinical decision-making processes. A novel interpretable DL model and nomogram were developed and validated using EUS images, cooperating with machine learning algorithms. This approach demonstrates significant potential for enhancing the clinical applicability of EUS in predicting PNETs from pancreatic cancer, thereby offering valuable insights for future research and implementation.