Antibiotic resistance is the most important factor leading to failed Helicobacter pylori eradication therapy, and personalized treatment based on antibiotic susceptibility is becoming increasingly important. To strengthen the understanding of antibiotic genotypic resistance of H. pylori and identify new antibiotic resistance loci, in this study, we identified phenotypic resistance information for 60 clinical isolates and compared the concordance of phenotypic and genotypic resistance using whole-genome sequencing (WGS).

Background: Liver hepatocellular carcinoma (LIHC) is a complicated disease with poor survival and lack of viable treatment options. The roles of ferroptosis and immunotherapy in LIHC are increasingly prominent, but the interplay of ferroptosis with the tumor microenvironment (TME) in LIHC is currently under-investigated.Methods: In this study, we analyzed normal liver tissues and tumor tissues from the TCGA and GTEx databases to obtain differentially expressed ferroptosis-related genes (FRGs). We then clustered LIHC based on the expression levels of selected FRGs and acquired distinct subtypes with significant heterogeneity regarding survival prognoses, PD-L1 expression, and immune cell infiltration. The correlation of those FRGs with TME in LIHC and pan-cancer analysis was also investigated. GO functional annotations and KEGG pathway analyses were performed to investigate the potential reactions of the obtained differentially expressed genes (DEGs). Further external validation was performed using microarrays on the GEO database and the key ferroptosis regulator SLC7A11 expression between LIHC and normal cells was detected by Western blotting.Results: A large proportion of genes were upregulated in the LIHC group. Among three clusters, cluster 3 had the worst prognosis combined with the highest PD-L1 expression and was positively correlated with various immune cells. Subsequently, survival analysis and Cox regression analysis screened out SLC7A11 as an independent prognostic factor in LIHC featured strong PD-L1 expression and unfavorable survival time. We filter out SLC7A11 as an independent prognostic signature in LIHC patients with strongly associated PD-L1 expression and unfavorable survival probability. In the pan-cancer analysis, high expression of SLC7A11 showed poor overall survival in seven cancers, while the correlation between immune checkpoints (ICs) and SLC7A11 varied by cancer type, indicating the potential therapeutic effects of SLC7A11 in cancers other than LIHC. Western blot was further employed to verify the expression of SLC7A11 in LIHC in vitro.Conclusion: Ferroptosis and TME synergistically play key roles in oncogenesis and progression of LIHC, and SLC7A11 can be used as a predictive biomarker for customized immunotherapy.

Chronic atrophic gastritis (CAG) is a precursor to gastric cancer, a leading cause of cancer-related deaths worldwide. Despite current therapeutic strategies, preventing the transition from gastritis to cancer remains a challenge. Traditional Chinese Medicine (TCM), particularly the Yiqi-Huayu-Jiedu (YQHYJD) formula, have exhibited promising results in CAG management. However, the pharmacological underpinnings of this formula remain elusive. The study aimed to elucidate the pharmacological mechanisms of the YQHYJD formula in treating CAG and its role in inhibiting the progression to gastric cancer through the modulation of the "inflammation-cancer" sequence. Mass spectrometric analysis of YQHYJD formula-containing serum was conducted to determine the active compounds involved in CAG treatment. A CAG rat model was induced using a combination of deoxycholic acid and ammonia, while a gastric precancerous lesion cell model was generated by exposing GES-1 cells to deoxycholic acid. Both models were treated with varying concentrations of the YQHYJD formula to assess its effects of the JAK2/STAT3 signaling-mediated epithelial-mesenchymal transition (EMT) pathway. Mass spectrometry analysis identified 80 active compounds in the YQHYJD formula, including quercetin. Network pharmacology analysis revealed that these active compounds may exert their therapeutic effects on CAG through various mechanisms, including the JAK/STAT signaling. Using rat and cellular models of CAG, we found that the JAK/STAT pathway is activated alongside partial epithelial-mesenchymal transition (pEMT). YQHYJD treatment effectively mitigated the activation of the JAK2/STAT3 activation and pEMT. Furthermore, the therapeutic effect of the YQHYJD formula was maintained even in the presence of Colivelin or overexpressed STAT3. The YQHYJD formula treats CAG by inhibiting the JAK2/STAT3 -mediated pEMT, thereby suppressing the gastric "inflammation-cancer" transformation. This study provides mechanistic insights into the efficacy of YQHYJD in CAG treatment and suggests new therapeutic strategies for preventing gastric cancer development.

Background: To develop and validate a panel of serum IgG N-glycan biomarkers for both the diagnosis of rheumatoid arthritis (RA) and the differentiation of Traditional Chinese Medicine (TCM) syndromes in RA patients. Methods: We conducted a case–control study involving 105 patients meeting the 2010 American College of Rheumatology/European Alliance against Rheumatism RA classification criteria and 79 healthy controls. RA patients were classified according to TCM principles into cold and heat patterns. Serum IgG was enriched using titanium dioxide-porous graphitic carbon (TiO2-PGC) wafers and analyzed by high-performance liquid chromatography. IgG N-glycans were quantified using multiple reaction monitoring. Potential N-glycan biomarkers for RA diagnosis and TCM syndrome differentiation were identified and validated using multivariate data analysis. Results: Orthogonal partial least squares discriminant analysis (OPLS-DA) identified 57 N-glycans (variable importance in projection > 1) that differentiated between RA cold pattern, heat pattern, and healthy controls. Through random forest machine learning and Kruskal–Wallis testing, we identified three acidic N-glycans (5_4_0_1-a, 5_4_0_2-a, and 5_4_0_2-b) as potential diagnostic biomarkers. In the training set, receiver operating characteristic analysis demonstrated that this three-N-glycan panel effectively distinguished RA patients from healthy controls (AUC 0.90), with particularly strong discrimination between RA heat pattern and healthy controls (AUC 0.99) and between RA cold pattern and healthy controls (AUC 0.84). The robust predictive performance was further validated in an independent test set. Additionally, we developed a logistic regression model for future clinical application in predicting both RA diagnosis and its heat/cold syndrome patterns. Conclusion: This glycomics-based approach identified and validated novel N-glycan biomarkers associated with both RA diagnosis and TCM syndrome differentiation. The combination of these N-glycan biomarkers and our diagnostic model offers a promising strategy for integrating modern diagnostic techniques with TCM classification in RA management.

Recently, large models have shown significant potential for smart healthcare. However, the deployment of Large Vision-Language Models (LVLMs) for clinical services is currently hindered by three critical challenges: a tendency to hallucinate answers not grounded in visual evidence, the inefficiency of fixed-depth reasoning, and the difficulty of multi-institutional collaboration. To address these challenges, in this paper, we develop MedAlign, a novel framework to ensure visually accurate LVLM responses for Medical Visual Question Answering (Med-VQA). Specifically, we first propose a multimodal Direct Preference Optimization (mDPO) objective to explicitly align preference learning with visual context. We then design a Retrieval-Aware Mixture-of-Experts (RA-MoE) architecture that utilizes image and text similarity to route queries to a specialized and context-augmented LVLM (i.e., an expert), thereby mitigating hallucinations in LVLMs. To achieve adaptive reasoning and facilitate multi-institutional collaboration, we propose a federated governance mechanism, where the selected expert, fine-tuned on clinical datasets based on mDPO, locally performs iterative Chain-of-Thought (CoT) reasoning via the local meta-cognitive uncertainty estimator. Extensive experiments on three representative Med-VQA datasets demonstrate that MedAlign achieves state-of-the-art performance, outperforming strong retrieval-augmented baselines by up to $11.85\%$ in F1-score, and simultaneously reducing the average reasoning length by $51.60\%$ compared with fixed-depth CoT approaches.