Background: The efficacy and safety of potassium-competitive acid blockers (P-CABs) in the eradication of Helicobacter pylori (Hp) remains controversial when compared with proton pump inhibitors (PPIs). Objectives: The current study set out to compare the differences in the eradication rate and adverse reactions between eradication regimens based on P-CAB or PPI drugs and the differences between the vonoprazan-based and the tegoprazan-based regimens to explore the efficacy and safety of different Hp eradication regimens. Data sources and methods: Databases including PubMed, EMBASE, Cochrane Library, and WOS were searched from the inception of these databases up to July 2023, and eligible randomized controlled trials (RCTs) were included. The outcome measures were the eradication rate and the incidence of adverse reactions of different regimens in treating Hp. The results were estimated as relative risk (RR) and its 95% confidence interval (CI), and R 4.2.1 software was used to perform the network meta-analysis (NMA). Results: A total of 20 studies were included in the analysis, involving 5815 patients with Hp. In terms of eradication rate, the 2-week vonoprazan-based triple regimen (V-Tri-2w) was the best, which was superior to the 2-week PPI-based quadruple regimen [P-Qua-2w, RR = 0.9, 95% CI: (0.85–0.95)] and the 1-week tegoprazan-based triple regimen [T-Tri-1w, RR = 0.79, 95% CI: (0.64–0.97)]; the 2-week tegoprazan-based quadruple regimen (T-Qua-2w) was superior to the 1-week PPI-based triple regimen [P-Tri-1w, RR = 0.82, 95% CI: (0.67–0.99)], and there was no difference between the remaining tegoprazan-based regimens and the PPI-based or vonoprazan-based regimens. In terms of the incidence of adverse reactions, the 2-week vonoprazan-based binary regimen (V-Bi-2w) was lower than that of the 2-week PPI-based quadruple regimen [P-Qua-2w, RR = 1.98, 95% CI: (1.57–2.52)]; there was no significant difference between 1 and 2 weeks for each regimen, such as the vonoprazan-based triple regimen [RR = 1.11, 95% CI: (0.82–1.52)]. Conclusion: In the eradication treatment of Hp, the efficacy and safety of vonoprazan-based regimens are generally better than those of PPI-based regimens. Among them, the V-Tri-2w regimen has the highest eradication rate and may be the preferred choice for Hp eradication.

Hepatocellular carcinoma (HCC) is a common malignancy with poor survival and requires long-term follow-up. Hence, we collected information on patients with Primary Hepatocellular Carcinoma in the United States from the Surveillance, Epidemiology, and EndResults (SEER) database. We used this information to establish a deep learning with a multilayer neural network (the NMTLR model) for predicting the survival rate of patients with Primary Hepatocellular Carcinoma. HCC patients pathologically diagnosed between January 2011 and December 2015 in the SEER (Surveillance, Epidemiology, and End Results) database of the National Cancer Institute of the United States were selected as study subjects. We utilized two deep learning-based algorithms (DeepSurv and Neural Multi-Task Logistic Regression [NMTLR]) and a machine learning-based algorithm (Random Survival Forest [RSF]) for model training. A multivariable Cox Proportional Hazards (CoxPH) model was also constructed for comparison. The dataset was randomly divided into a training set and a test set in a 7:3 ratio. The training dataset underwent hyperparameter tuning through 1000 iterations of random search and fivefold cross-validation. Model performance was assessed using the concordance index (C-index), Brier score, and Integrated Brier Score (IBS). The accuracy of predicting 1-year, 3-year, and 5-year survival rates was evaluated using Receiver Operating Characteristic (ROC) curves, calibration plots, and Area Under the Curve (AUC). The primary outcomes were the 1-year, 3-year, and 5-year overall survival rates. Models were developed using DeepSurv, NMTLR, RSF, and Cox Proportional Hazards regression. Model differentiation was evaluated using the C-index, calibration with concordance plots, and risk stratification capability with the log-rank test. The study included 2197 HCC patients, randomly divided into a training cohort (70%, n = 1537) and a testing cohort (30%, n = 660). Clinical characteristics between the two cohorts showed no significant statistical difference (p > 0.05). The deep learning models outperformed both RSF and CoxPH models, with C-indices of 0.735 (NMTLR) and 0.731 (DeepSurv) in the test dataset. The NMTLR model demonstrated enhanced accuracy and well-calibrated survival estimates, achieving an Area Under the Curve (AUC) of 0.824 for 1-year survival predictions, 0.813 for 3-year, and 0.803 for 5-year survival rates. This model's superior calibration and discriminative ability enhance its utility for clinical prognostication in Primary Hepatocellular Carcinoma. We deployed the NMTLR model as a web application for clinical practice. The NMTLR model have potential advantages over traditional linear models in prognostic assessment and treatment recommendations. This novel analytical approach may provide reliable information on individual survival and treatment recommendations for patients with primary liver cancer.

Introduction: The diagnostic performance of liquid biopsy-based biomarkers for HCC was comprehensively compared in this network meta-analysis (NMA).Methods: A thorough literature search was conducted to identify all comparative studies from January 1, 2000, to January 11, 2024. The QUADAS-2 tool was utilized to appraise the quality of studies involving diagnostic performance. R (v4.3.3) and an ANOVA model-based NMA were used to assess the diagnostic accuracy of each biomarker.Results: This study included 82 studies comprising a total of 15,024 patients.CircRNA demonstrated significantly superior performance in distinguishing HCC from healthy populations (superiority index: 3.550 (95% CI [0.143-3])) compared to other diagnostic biomarkers for HCC. “mRNA exhibited significantly superior performance in distinguishing HCC from liver disease patients (superiority index:10.621 (95% CI [7-11])) compared to other diagnostic biomarkers for HCC. Further subgroup analysis of the top-ranking liquid biopsy-based diagnostic biomarkers revealed that hsa_circ_000224 (superiority index: 3.091 (95% CI[0.143-9]) ranked remarkably higher in distinguishing HCC from both healthy populations and liver disease patients. Subgroup analysis of mRNA demonstrated that KIAA0101 mRNA (superiority index: 2.434 (95% CI [0.2-5]) ranked remarkably higher in distinguishing HCC from healthy populations and liver disease patients, respectively.Discussion: The results of this meta-analysis show that circRNA and mRNA are the first choice for HCC diagnosis. Subsequent analysis of circRNA and mRNA highlighted hsa_circ_000224, hsa_circ_0003998, KIAA0101 mRNA and GPC-3mRNA as the optimal diagnostic biomarkers for distinguishing HCC from healthy populations and liver disease patients, respectively. Well-structured prospective studies are crucial to comprehensively validate these findings.Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/,identifier CRD42024521299.

Helicobacter pylori is one of the main causes of gastritis and gastric ulcer. Early detection of H. pylori is of great significance for the prevention of gastric cancer. Herein, a sensitive electrochemical aptasensor using AgNPs-GO as redox probes was established for the specific detection of H. pylori in blood serum and stool samples. Firstly, AgNPs-GO nanocomposites were obtained by in-situ reduction of AgNPs on graphene oxide (GO) surface with glucose as reducing agent, in which AgNPs showed good biocompatibility and chemical stability, as well as redox property, and GO provided a large surface area to assemble a large number of AgNPs. Subsequently, the electrodeposition of AuNPs further improved the conductivity of the aptasensor. Additionally, the streptavidin was introduced into the aptasensor to effectively bind the biotin-modified aptamers. In this way, aptamers could be tethered to the surface through SA-biotin linkage, and H. pylori was selectivly binded by the aptamers subsequently. Under optimal conditions, the aptasensor could detect H. pylori in a wide concentration range (10¹ CFU mL⁻¹–10⁸ CFU mL⁻¹) with a low detection limit of 3 CFU mL⁻¹. What’s more, the developed method showed excellent performance in practical application, which provided a promising possibility for the detection of other pathogens in clinical diagnosis.

Background. Biejiajian pill (BJJP), a classical traditional Chinese formula, has been reported that it has an effective treatment for diabetic atherosclerosis in recent years, but its underlying mechanisms remain elusive. The study aimed to explore the potential mechanisms of BJJP on diabetic atherosclerosis by integrating network pharmacology, molecular docking, and molecular dynamics simulation. Methods. The active components of BJJP were collected by TCMSP and TCMID, and then the potential targets were obtained from the SwissTargetPrediction database. The targets related to diabetic atherosclerosis were identified from the GeneCards and OMIM databases. The intersection of the potential targets regulated by active components of BJJP and the targets of diabetic atherosclerosis were common targets, which were visualized by the Venn diagram. The common targets were imported into the STRING database to construct a protein-protein interaction (PPI) network. The network of Medicine-Compound-Target was constructed with Cytoscape 3.7.1 software. GO functional enrichment analysis and KEGG pathway enrichment analysis were performed using the DAVID database and visualized through bioinformatics. The intersecting targets were input into Cytoscape 3.7.1 software, and the Network Analyzer tool was employed to screen out the key targets. Then molecular docking was used to verify the binding affinity between the active compounds and the key targets, and molecular dynamics simulation was used to investigate the stability of the binding models. Results. A total of 81 active components, 186 targets of BJJP, and 4041 targets of diabetic atherosclerosis were obtained. Furthermore, 121 overlapping targets were identified. GO functional enrichment analysis revealed that these targets were correlated with the oxidation-reduction process, negative regulation of apoptotic process, inflammatory response, and other biological processes. The results of the KEGG pathway enrichment analysis showed that the common targets mainly participated in proteoglycans in cancer, PPAR signaling pathway, adherens junction, insulin resistance, HIF-1 signaling pathway, PI3K-Akt signaling pathway, etc. The results of molecular docking confirmed that the core active components in BJJP could bind well to the key targets. Results from molecular dynamics simulation showed that the binding energies of AKT1-Luteolin, MMP9-quercetin, and MMP9-luteolin complexes were 28.93kJmol⁻¹, 37.12kJmol⁻¹, and 62.91kJmol⁻¹, respectively. Conclusion. The study revealed that BJJP is characterized as multicomponent, multitarget, and multipathway to treat diabetic atherosclerosis, which is helpful to provide ideas and a basis for pharmacological research and clinical application in the future.