Purpose: This study investigated the factors influencing posttraumatic growth (PTG) among nurses who worked in COVID-19 isolation wards in tertiary hospitals in South Korea. Method: A cross-sectional descriptive correlation research design was used. In total, 190 nurses who had worked with COVID-19-infected patients participated in the study. Their demographic characteristics, self-disclosure, resilience, social support, and PTG were examined using a structured online questionnaire administered from March 16 to 25, 2022. Data were analyzed using SPSS/WIN 27.0. Results: The mean scores for self-disclosure, resilience, social support, and PTG were 42.79 (range: 12~60), 60.92 (range: 0~100), 46.98 (range: 12~60), and 45.66 (range: 0~80), respectively. Self-disclosure (β=.18, p=.003) and resilience (β=.65, p<.001) significantly influenced PTG, explaining 55% of the total variance in this variable. Conclusion This study highlights the need to systematically develop and implement effective intervention programs to strengthen nurses' self-disclosure and resilience.

Purpose: This study investigated the factors influencing posttraumatic growth (PTG) among nurses who worked in COVID-19 isolation wards in tertiary hospitals in South Korea. Method: A cross-sectional descriptive correlation research design was used. In total, 190 nurses who had worked with COVID-19-infected patients participated in the study. Their demographic characteristics, self-disclosure, resilience, social support, and PTG were examined using a structured online questionnaire administered from March 16 to 25, 2022. Data were analyzed using SPSS/WIN 27.0. Results: The mean scores for self-disclosure, resilience, social support, and PTG were 42.79 (range: 12~60), 60.92 (range: 0~100), 46.98 (range: 12~60), and 45.66 (range: 0~80), respectively. Self-disclosure (β=.18, p=.003) and resilience (β=.65, p<.001) significantly influenced PTG, explaining 55% of the total variance in this variable. Conclusion This study highlights the need to systematically develop and implement effective intervention programs to strengthen nurses' self-disclosure and resilience.

Purpose: This study investigated the factors influencing posttraumatic growth (PTG) among nurses who worked in COVID-19 isolation wards in tertiary hospitals in South Korea. Method: A cross-sectional descriptive correlation research design was used. In total, 190 nurses who had worked with COVID-19-infected patients participated in the study. Their demographic characteristics, self-disclosure, resilience, social support, and PTG were examined using a structured online questionnaire administered from March 16 to 25, 2022. Data were analyzed using SPSS/WIN 27.0. Results: The mean scores for self-disclosure, resilience, social support, and PTG were 42.79 (range: 12~60), 60.92 (range: 0~100), 46.98 (range: 12~60), and 45.66 (range: 0~80), respectively. Self-disclosure (β=.18, p=.003) and resilience (β=.65, p<.001) significantly influenced PTG, explaining 55% of the total variance in this variable. Conclusion This study highlights the need to systematically develop and implement effective intervention programs to strengthen nurses' self-disclosure and resilience.

Nanog is a key transcription factor that regulates the self-renewal and pluripotency of embryonic stem cells (ESCs).Although Kap1 has been demonstrated to regulate the stability of stemness factors, including Oct4 and Lin28A, its role in regulating Nanog protein stability in ESCs remains unexplored. In the present study, we examined the interaction between Kap1 and Nanog and its role in stabilizing the Nanog protein. Immunoprecipitation assays revealed that Nanog specifically interacted with the coiled-coil domain of Kap1. Kap1 overexpression increased the stability of the Nanog protein by inhibiting its ubiquitination and proteasomal degradation, whereas Kap1 silencing accelerated Nanog degradation. Furthermore, Kap1 overexpression inhibits Nanog degradation by interfering with the binding of Nanog to Fbxw8, an E3 ubiquitin ligase that promotes Nanog degradation via a proteasome-dependent process. These results indicate that Kap1 acts as a key regulator to preserve ESC properties by modulating the protein stability of stemness factors, including Oct4, Lin28A, and Nanog.

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: 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.

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: Recent diabetes management guidelines recommend that sodium-glucose cotransporter 2 inhibitors (SGLT2is) or glucagon-like peptide 1 receptor agonists (GLP-1RAs) with proven cardiovascular benefits should be prioritized for combination therapy in patients with type 2 diabetes mellitus (T2DM) and established cardiovascular disease (CVD). This study was aimed at evaluating SGLT2i or GLP-1RA usage rates and various related factors in patients with T2DM and established CVD. Methods: We enrolled adults with T2DM aged ≥30 years who were hospitalized due to established CVD from January 2019 to May 2020 at 13 secondary and tertiary hospitals in Korea in this retrospective observational study. Results: Overall, 2,050 patients were eligible for analysis among 2,107 enrolled patients. The mean patient age, diabetes duration, and glycosylated hemoglobin level were 70.0 years, 12.0 years, and 7.5%, respectively. During the mean follow-up duration of 9.7 months, 25.7% of the patients were prescribed SGLT2is after CVD events. However, only 1.8% were prescribed GLP-1RAs. Compared with SGLT2i non-users, SGLT2i users were more frequently male and obese. Furthermore, they had a shorter diabetes duration but showed worse glycemic control and better renal function at the time of the event. GLP-1RA users had a longer duration of diabetes and worse glycemic control at the time of the event than GLP-1RA non-users. Conclusion The SGLT2i or GLP-1RA prescription rates were suboptimal in patients with T2DM and established CVD. Sex, body mass index, diabetes duration, glycemic control, and renal function were associated with the use of these agents.