Background/Aims: Fexuprazan, a novel potassium-competitive acid blocker, was developed for treating acid-related disorders. Pharmacokinetic and pharmacodynamic properties of fexuprazan, unlike those of proton pump inhibitors, are independent of food effect. This study aims to evaluate differences in efficacy and safety of fexuprazan in patients with erosive esophagitis (EE) according to the timing of dosing. Methods: In this multicenter, open-label noninferiority study, patients who had typical reflux symptoms with endoscopically confirmed EE were randomized 1:1 to receive fexuprazan 40 mg daily 30 minutes before or after meal. Treatment was completed after 2 weeks or 4 weeks when healing was endoscopically confirmed. The primary endpoint was the proportion of patients with healed EE confirmed by endoscopy up to week 4. Safety endpoints included treatment-emergent adverse events (TEAEs). Results: In the prior-to-meal group (n = 89) and after-meal group (n = 86), 4-week EE healing rates were 98.77% and 100.00% (difference, 0.01%; 95% CI, –0.01% to 0.04%) and 2-week EE healing rates were 95.77% and 97.14% (difference, 0.01%; 95% CI, –0.05% to 0.07%), respectively. TEAEs were 9.78% and 8.70% in the prior-to-meal group and the after-meal group, respectively. Conclusions Non-inferiority analysis revealed that taking fexuprazan after meal was non-inferior to taking fexuprazan before meals in patients with EE. The frequency of adverse events was similar between the 2 study groups. The drug is safe and effective for healing EE regardless of the timing of dosing.

Background/Aims: Fexuprazan, a novel potassium-competitive acid blocker, was developed for treating acid-related disorders. Pharmacokinetic and pharmacodynamic properties of fexuprazan, unlike those of proton pump inhibitors, are independent of food effect. This study aims to evaluate differences in efficacy and safety of fexuprazan in patients with erosive esophagitis (EE) according to the timing of dosing. Methods: In this multicenter, open-label noninferiority study, patients who had typical reflux symptoms with endoscopically confirmed EE were randomized 1:1 to receive fexuprazan 40 mg daily 30 minutes before or after meal. Treatment was completed after 2 weeks or 4 weeks when healing was endoscopically confirmed. The primary endpoint was the proportion of patients with healed EE confirmed by endoscopy up to week 4. Safety endpoints included treatment-emergent adverse events (TEAEs). Results: In the prior-to-meal group (n = 89) and after-meal group (n = 86), 4-week EE healing rates were 98.77% and 100.00% (difference, 0.01%; 95% CI, –0.01% to 0.04%) and 2-week EE healing rates were 95.77% and 97.14% (difference, 0.01%; 95% CI, –0.05% to 0.07%), respectively. TEAEs were 9.78% and 8.70% in the prior-to-meal group and the after-meal group, respectively. Conclusions Non-inferiority analysis revealed that taking fexuprazan after meal was non-inferior to taking fexuprazan before meals in patients with EE. The frequency of adverse events was similar between the 2 study groups. The drug is safe and effective for healing EE regardless of the timing of dosing.

Background/Aims: Fexuprazan, a novel potassium-competitive acid blocker, was developed for treating acid-related disorders. Pharmacokinetic and pharmacodynamic properties of fexuprazan, unlike those of proton pump inhibitors, are independent of food effect. This study aims to evaluate differences in efficacy and safety of fexuprazan in patients with erosive esophagitis (EE) according to the timing of dosing. Methods: In this multicenter, open-label noninferiority study, patients who had typical reflux symptoms with endoscopically confirmed EE were randomized 1:1 to receive fexuprazan 40 mg daily 30 minutes before or after meal. Treatment was completed after 2 weeks or 4 weeks when healing was endoscopically confirmed. The primary endpoint was the proportion of patients with healed EE confirmed by endoscopy up to week 4. Safety endpoints included treatment-emergent adverse events (TEAEs). Results: In the prior-to-meal group (n = 89) and after-meal group (n = 86), 4-week EE healing rates were 98.77% and 100.00% (difference, 0.01%; 95% CI, –0.01% to 0.04%) and 2-week EE healing rates were 95.77% and 97.14% (difference, 0.01%; 95% CI, –0.05% to 0.07%), respectively. TEAEs were 9.78% and 8.70% in the prior-to-meal group and the after-meal group, respectively. Conclusions Non-inferiority analysis revealed that taking fexuprazan after meal was non-inferior to taking fexuprazan before meals in patients with EE. The frequency of adverse events was similar between the 2 study groups. The drug is safe and effective for healing EE regardless of the timing of dosing.

Purpose: We aimed to comprehensively analyze the immune cell and stromal components of tumor microenvironment at the single-cell level and identify tumor heterogeneity among the major top-derived oncogene mutations in non-small cell lung cancer (NSCLC) using single-cell RNA sequencing (scRNA-seq) data. Materials and Methods: The scRNA-seq dataset utilized in this study comprised 64369 primary tumor tissue cells from 21 NSCLC patients, focusing on mutations in EGFR, ALK, BRAF, KRAS, TP53, and the wild-type. Results: Tumor immune microenvironment (TIM) analysis revealed differential immune responses across NSCLC mutation subtypes. TIM analysis revealed different immune responses across the mutation subtypes. Two mutation clusters emerged: KRAS, TP53, and EGFR+TP53 mutations (MC1); and EGFR, BRAF, and ALK mutations (MC2). MC1 showed higher tertiary lymphoid structures signature scores and enriched populations of C2-T-IL7R, C3-T/NK-CXCL4, C9-T/NK-NKG, and C1-B-MS4A1 clusters than cluster 2. Conversely, MC2 cells exhibited higher expression levels of TNF, IL1B, and chemokines linked to alternative immune pathways. Remarkably, co-occurring EGFR and TP53 mutations were grouped as MC1. EGFR+TP53 mutations showed upregulation of peptide synthesis and higher synthetic processes, as well as differences in myeloid and T/NK cells compared to EGFR mutations. In T/NK cells, EGFR+TP53 mutations showed a higher expression of features related to cell activity and differentiation, whereas EGFR mutations showed the opposite. Conclusion Our research indicates a close association between mutation types and tumor microenvironment in NSCLC, offering insights into personalized approaches for cancer diagnosis and treatment.

Purpose: We aimed to comprehensively analyze the immune cell and stromal components of tumor microenvironment at the single-cell level and identify tumor heterogeneity among the major top-derived oncogene mutations in non-small cell lung cancer (NSCLC) using single-cell RNA sequencing (scRNA-seq) data. Materials and Methods: The scRNA-seq dataset utilized in this study comprised 64369 primary tumor tissue cells from 21 NSCLC patients, focusing on mutations in EGFR, ALK, BRAF, KRAS, TP53, and the wild-type. Results: Tumor immune microenvironment (TIM) analysis revealed differential immune responses across NSCLC mutation subtypes. TIM analysis revealed different immune responses across the mutation subtypes. Two mutation clusters emerged: KRAS, TP53, and EGFR+TP53 mutations (MC1); and EGFR, BRAF, and ALK mutations (MC2). MC1 showed higher tertiary lymphoid structures signature scores and enriched populations of C2-T-IL7R, C3-T/NK-CXCL4, C9-T/NK-NKG, and C1-B-MS4A1 clusters than cluster 2. Conversely, MC2 cells exhibited higher expression levels of TNF, IL1B, and chemokines linked to alternative immune pathways. Remarkably, co-occurring EGFR and TP53 mutations were grouped as MC1. EGFR+TP53 mutations showed upregulation of peptide synthesis and higher synthetic processes, as well as differences in myeloid and T/NK cells compared to EGFR mutations. In T/NK cells, EGFR+TP53 mutations showed a higher expression of features related to cell activity and differentiation, whereas EGFR mutations showed the opposite. Conclusion Our research indicates a close association between mutation types and tumor microenvironment in NSCLC, offering insights into personalized approaches for cancer diagnosis and treatment.

Purpose: We aimed to comprehensively analyze the immune cell and stromal components of tumor microenvironment at the single-cell level and identify tumor heterogeneity among the major top-derived oncogene mutations in non-small cell lung cancer (NSCLC) using single-cell RNA sequencing (scRNA-seq) data. Materials and Methods: The scRNA-seq dataset utilized in this study comprised 64369 primary tumor tissue cells from 21 NSCLC patients, focusing on mutations in EGFR, ALK, BRAF, KRAS, TP53, and the wild-type. Results: Tumor immune microenvironment (TIM) analysis revealed differential immune responses across NSCLC mutation subtypes. TIM analysis revealed different immune responses across the mutation subtypes. Two mutation clusters emerged: KRAS, TP53, and EGFR+TP53 mutations (MC1); and EGFR, BRAF, and ALK mutations (MC2). MC1 showed higher tertiary lymphoid structures signature scores and enriched populations of C2-T-IL7R, C3-T/NK-CXCL4, C9-T/NK-NKG, and C1-B-MS4A1 clusters than cluster 2. Conversely, MC2 cells exhibited higher expression levels of TNF, IL1B, and chemokines linked to alternative immune pathways. Remarkably, co-occurring EGFR and TP53 mutations were grouped as MC1. EGFR+TP53 mutations showed upregulation of peptide synthesis and higher synthetic processes, as well as differences in myeloid and T/NK cells compared to EGFR mutations. In T/NK cells, EGFR+TP53 mutations showed a higher expression of features related to cell activity and differentiation, whereas EGFR mutations showed the opposite. Conclusion Our research indicates a close association between mutation types and tumor microenvironment in NSCLC, offering insights into personalized approaches for cancer diagnosis and treatment.

Purpose: We aimed to comprehensively analyze the immune cell and stromal components of tumor microenvironment at the single-cell level and identify tumor heterogeneity among the major top-derived oncogene mutations in non-small cell lung cancer (NSCLC) using single-cell RNA sequencing (scRNA-seq) data. Materials and Methods: The scRNA-seq dataset utilized in this study comprised 64369 primary tumor tissue cells from 21 NSCLC patients, focusing on mutations in EGFR, ALK, BRAF, KRAS, TP53, and the wild-type. Results: Tumor immune microenvironment (TIM) analysis revealed differential immune responses across NSCLC mutation subtypes. TIM analysis revealed different immune responses across the mutation subtypes. Two mutation clusters emerged: KRAS, TP53, and EGFR+TP53 mutations (MC1); and EGFR, BRAF, and ALK mutations (MC2). MC1 showed higher tertiary lymphoid structures signature scores and enriched populations of C2-T-IL7R, C3-T/NK-CXCL4, C9-T/NK-NKG, and C1-B-MS4A1 clusters than cluster 2. Conversely, MC2 cells exhibited higher expression levels of TNF, IL1B, and chemokines linked to alternative immune pathways. Remarkably, co-occurring EGFR and TP53 mutations were grouped as MC1. EGFR+TP53 mutations showed upregulation of peptide synthesis and higher synthetic processes, as well as differences in myeloid and T/NK cells compared to EGFR mutations. In T/NK cells, EGFR+TP53 mutations showed a higher expression of features related to cell activity and differentiation, whereas EGFR mutations showed the opposite. Conclusion Our research indicates a close association between mutation types and tumor microenvironment in NSCLC, offering insights into personalized approaches for cancer diagnosis and treatment.

Purpose: We aimed to comprehensively analyze the immune cell and stromal components of tumor microenvironment at the single-cell level and identify tumor heterogeneity among the major top-derived oncogene mutations in non-small cell lung cancer (NSCLC) using single-cell RNA sequencing (scRNA-seq) data. Materials and Methods: The scRNA-seq dataset utilized in this study comprised 64369 primary tumor tissue cells from 21 NSCLC patients, focusing on mutations in EGFR, ALK, BRAF, KRAS, TP53, and the wild-type. Results: Tumor immune microenvironment (TIM) analysis revealed differential immune responses across NSCLC mutation subtypes. TIM analysis revealed different immune responses across the mutation subtypes. Two mutation clusters emerged: KRAS, TP53, and EGFR+TP53 mutations (MC1); and EGFR, BRAF, and ALK mutations (MC2). MC1 showed higher tertiary lymphoid structures signature scores and enriched populations of C2-T-IL7R, C3-T/NK-CXCL4, C9-T/NK-NKG, and C1-B-MS4A1 clusters than cluster 2. Conversely, MC2 cells exhibited higher expression levels of TNF, IL1B, and chemokines linked to alternative immune pathways. Remarkably, co-occurring EGFR and TP53 mutations were grouped as MC1. EGFR+TP53 mutations showed upregulation of peptide synthesis and higher synthetic processes, as well as differences in myeloid and T/NK cells compared to EGFR mutations. In T/NK cells, EGFR+TP53 mutations showed a higher expression of features related to cell activity and differentiation, whereas EGFR mutations showed the opposite. Conclusion Our research indicates a close association between mutation types and tumor microenvironment in NSCLC, offering insights into personalized approaches for cancer diagnosis and treatment.

Background and Objectives: Early diastolic mitral annular tissue (e’) velocity is a commonly used marker of left ventricular (LV) diastolic function. This study aimed to investigate the prognostic implications of e’ velocity in patients with mitral regurgitation (MR). Methods: This retrospective cohort study included 1,536 consecutive patients aged <65 years with moderate or severe chronic primary MR diagnosed between 2009 and 2018. The primary and secondary outcomes were all-cause and cardiovascular mortality, respectively.According to the current guidelines, the cut-off value of e’ velocity was defined as 7 cm/s. Results: A total of 404 individuals were enrolled (median age, 51.0 years; 64.1% male; 47.8% severe MR). During a median 6.0-year follow-up, there were 40 all-cause mortality and 16 cardiovascular deaths. Multivariate analysis revealed a significant association between e’ velocity and all-cause death (adjusted hazard ratio [aHR], 0.770; 95% confidence interval [CI], 0.634–0.935; p=0.008) and cardiovascular death (aHR, 0.690; 95% CI, 0.477–0.998;p=0.049). Abnormal e’ velocity (≤7 cm/s) independently predicted all-cause death (aHR, 2.467; 95% CI, 1.170–5.200; p=0.018) and cardiovascular death (aHR, 5.021; 95% CI, 1.189–21.211; p=0.028), regardless of symptoms, LV dimension and ejection fraction. Subgroup analysis according to sex, MR severity, mitral valve replacement/repair, and symptoms, showed no significant interactions. Including e’ velocity in the 10-year risk score improved reclassification for mortality (net reclassification improvement [NRI], 0.154; 95% CI, 0.308– 0.910; p<0.001) and cardiovascular death (NRI, 1.018; 95% CI, 0.680–1.356; p<0.001). Conclusions In patients aged <65 years with primary MR, e’ velocity served as an independent predictor of all-cause and cardiovascular deaths.

Purpose: By utilizing both protein and mRNA expression patterns, we can identify more detailed and diverse immune cells, providing insights into understanding the complex immune landscape in cancer ecosystems. Materials and Methods: This study was performed by obtaining publicly available Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data of peripheral blood mononuclear cells (PBMCs) from the Gene Expression Omnibus database. A total of 94674 total cells were analyzed, of which 32412 were T cells. There were 228 protein features and 16262 mRNA features in the data.The Seurat package was used for quality control and preprocessing, principal component analysis was performed, and Uniform Manifold Approximation and Projection was used to visualize the clusters. Protein and mRNA levels in the CITE-seq were analyzed. Results: We observed that a subset of T cells in the clusters generated at the protein level divided better. By identifying mRNA markers that were highly correlated with the CD4 and CD8 proteins and cross-validating CD26 and CD99 markers using flow cytometry, we found that CD4 + and CD8+ T cells were better discriminated in PBMCs. Weighted Nearest Neighbor clustering results identified a previously unobserved T cell subset. Conclusion In this study, we used CITE-seq data to confirm that protein expression patterns could be used to identify cells more precisely. These findings will improve our understanding of the heterogeneity of immune cells in the future and provide valuable insights into the complexity of the immune response in health and disease.