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Abstract Background The association between uric acid and pulmonary embolism(PE) remains controversial, and there has been limited investigation into how uric acid influences pulmonary embolism across different age groups. Our study aimed to elucidate the relationship between uric acid levels and pulmonary embolism, considering variations across age groups. Methods A total of 368 patients who underwent computed tomography pulmonary angiography from July 2018 to May 2022 were included in the analysis. Subsequently, the cohort was stratified by age, with separate univariate and multivariate logistic regression analyses conducted for the elderly (aged ≥ 60 years) and non-elderly (aged

BackgroundPulmonary arterial hypertension (PAH) is a serious condition characterized by elevated pulmonary artery pressure, leading to right heart failure and increased mortality. This study investigates the link between PAH and genes associated with hypoxia and cuproptosis.MethodsWe utilized expression profiles and single-cell RNA-seq data of PAH from the GEO database and genecad. Genes related to cuproptosis and hypoxia were identified. After normalizing the data, differential gene expression was analyzed between PAH and control groups. We performed clustering analyses on cuproptosis-related genes and constructed a weighted gene co-expression network (WGCNA) to identify key genes linked to cuproptosis subtype scores. KEGG, GO, and DO enrichment analyses were conducted for hypoxia-related genes, and a protein–protein interaction (PPI) network was created using STRING. Immune cell composition differences were examined between groups. SingleR and Seurat were used for scRNA-seq data analysis, with PCA and t-SNE for dimensionality reduction. We analyzed hub gene expression across single-cell clusters and built a diagnostic model using LASSO and random forest, optimizing parameters with 10-fold cross-validation. A total of 113 combinations of 12 machine learning algorithms were employed to evaluate model accuracy. GSEA was utilized for pathway enrichment analysis of AHR and FAS, and a Nomogram was created to assess risk impact. We also analyzed the correlation between key genes and immune cell types using Spearman correlation.ResultsWe identified several diagnostic genes for PAH linked to hypoxia and cuproptosis. PPI networks illustrated relationships among these hub genes, with immune infiltration analysis highlighting associations with monocytes, macrophages, and CD8 T cells. The genes AHR, FAS, and FGF2 emerged as key markers, forming a robust diagnostic model (NaiveBayes) with an AUC of 0.9.ConclusionAHR, FAS, and FGF2 were identified as potential biomarkers for PAH, influencing cell proliferation and inflammatory responses, thereby offering new insights for PAH prevention and treatment.

ObjectiveThis study aims to apply different machine learning (ML) methods to construct risk prediction models for pulmonary embolism (PE) in hospitalized patients, and to evaluate and compare the predictive efficacy and clinical benefit of each model.MethodsWe conducted a retrospective study involving 332 participants (172 PE positive cases and 160 PE negative cases) recruited from Guangdong Medical University. Participants were randomly divided into a training group (70%) and a validation group (30%). Baseline data were analyzed using univariate analysis, and potential independent risk factors associated with PE were further identified through univariate and multivariate logistic regression analysis. Six ML models, namely Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), and AdaBoost were developed. The predictive efficacy of each model was compared using the receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC). Clinical benefit was assessed using decision curve analysis (DCA).ResultsLogistic regression analysis identified lower extremity deep venous thrombosis, elevated D-dimer, shortened activated partial prothrombin time, and increased red blood cell distribution width as potential independent risk factors for PE. Among the six ML models, the RF model achieved the highest AUC of 0.778. Additionally, DCA consistently indicated that the RF model offered the greatest clinical benefit.ConclusionThis study developed six ML models, with the RF model exhibiting the highest predictive efficacy and clinical benefit in the identification and prediction of PE occurrence in hospitalized patients.

ABSTRACT: Objectives: The incidence of Helicobacter pylori (HP) is 25–50% in developed countries and 80% in developing countries, including 56.2% in China. However, antibiotic resistance of HP is a threat to HP control. The purpose of this study was to comprehensively evaluate primary drug resistance of HP in China. Methods: The full text of reports of the primary antibiotic resistance prevalence of HP was obtained from multiple databases (PubMed, Web of Science, Evimed, Cochrane Library, and China National Knowledge Internet). Review Manager 5.2 was adopted for meta-analysis, sensitivity analysis, and bias analysis. The Newcastle-Ottawa Scale was used to assess the article quality. Results: In total, 38804 HP samples from 22 trials were extracted. The results suggested that the overall prevalence of amoxicillin, clarithromycin, metronidazole, and levofloxacin resistance among HP in adults was as follows: mean difference (MD) = 1.35%, 95% confidence interval (CI) [1.03%, 1.68%]; MD = 23.76%, 95% CI [20.23%, 27.3%]; MD = 69.32%, 95% CI [64.85%, 73.8%]; and MD = 29.45%, 95% CI [4.90, 176.96], respectively. From the results of sensitivity and publication bias, we find that these results are robust and had little publication bias. Conclusion: Our research showed that in China, the prevalence of HP resistance to primary antibiotics warrants attention, especially with regard to metronidazole, levofloxacin, and clarithromycin.

Abstract Background Gastric cancer (GC) is one of the most common tumours in East Asia countries and is associated with Helicobacter pylori infection. H. pylori utilizes virulence factors, CagA and VacA, to up‐regulate pro‐inflammatory cytokines and activate NF‐κB signaling. Meanwhile, the PIEZO1 upregulation and cancer‐associated fibroblast (CAF) enrichment were found in GC progression. However, the mechanisms of PIEZO1 upregulation and its involvement in GC progression have not been fully elucidated. Methods The CAF enrichment and clinical significance were investigated in animal models and primary samples. The expression of NF‐κB and PIEZO1 in GC was confirmed by immunohistochemistry staining, and expression correlation was analysed in multiple GC datasets. GSEA and Western blot analysis revealed the YAP1‐CTGF axis regulation by PIEZO1. The stimulatory effects of CTGF on CAFs were validated by the co‐culture system and animal studies. Patient‐derived organoid and peritoneal dissemination models were employed to confirm the role of the PIEZO1‐YAP1‐CTGF cascade in GC. Results Both CAF signature and PIEZO1 were positively correlated with H. pylori infection. PIEZO1, a mechanosensor, was confirmed as a direct downstream of NF‐κB to promote the transformation from intestinal metaplasia to GC. Mechanistic studies revealed that PIEZO1 transduced the oncogenic signal from NF‐κB into YAP1 signaling, a well‐documented oncogenic pathway in GC progression. PIEZO1 expression was positively correlated with the YAP1 signature (CTGF, CYR61, and c‐Myc, etc.) in primary samples. The secreted CTGF by cancer cells stimulated the CAF infiltration to form a stiffened collagen‐enrichment microenvironment, thus activating PIEZO1 to form a positive feedback loop. Both PIEZO1 depletion by shRNA and CTGF inhibition by Procyanidin C1 enhanced the efficacy of 5‐FU in suppressing the GC cell peritoneal metastasis. Conclusion This study elucidates a novel driving PIEZO1‐YAP1‐CTGF force, which opens a novel therapeutic avenue to block the transformation from precancerous lesions to GC. H. pylori‐NF‐κB activates the PIEZO1‐YAP1‐CTGF axis to remodel the GC microenvironment by promoting CAF infiltration. Targeting PIEZO1‐YAP1‐CTGF plus chemotherapy might serve as a potential therapeutic option to block GC progression and peritoneal metastasis.

Abstract Helicobacter pylori (H. pylori) causes infection in the stomach and is a major factor for gastric carcinogenesis. The application of antimicrobial peptides (AMPs) as an alternative treatment to traditional antibiotics is limited by their facile degradation in the stomach, their poor penetration of the gastric mucosa, and the cost of peptide production. Here, the design and characterization of a genetically encoded H. pylori‐responsive microbicidal protein crystal Cry3Aa‐MIIA‐AMP‐P17 is described. This designed crystal exhibits preferential binding to H. pylori, and when activated, promotes the targeted release of the AMP at the H. pylori infection site. Significantly, when the activated Cry3Aa‐MIIA‐AMP‐P17 crystals are orally delivered to infected mice, the Cry3Aa crystal framework protects its cargo AMP against degradation, resulting in enhanced in vivo efficacy against H. pylori infection. Notably, in contrast to antibiotics, treatment with the activated crystals results in minimal perturbation of the mouse gut microbiota. These results demonstrate that engineered Cry3Aa crystals can serve as an effective platform for the oral delivery of therapeutic peptides to treat gastrointestinal diseases.