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.

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.

Background: The purpose of this study was to investigate the knowledge and attitude regarding the care of the elderly with hearing loss among nurses. Methods: This study used a cross-sectional descriptive design. Participants were 228 nurses who have experienced in caring the elderly with hearing loss. Data were collected from February 21 to March 21, 2022 using self-report questionnaires including 30 items of knowledge regarding the care of the elderly with hearing loss and 20 items of attitude regarding the care of the elderly with hearing loss. The data were analyzed by using the SPSS version 23.0 program (IBM Corp, Armonk, NY, USA). Results: The subjects’ average age was 33.35±7.79, and the average clinical career was 10.29±7.83. Nurses’ knowledge regarding the care of the elderly with hearing loss was the average score 62.9 out of 100.0. Nurses’ attitude regarding the care of the elderly with hearing loss was the average score of 85.4 out of 100.0. The relationship between nurses´ knowledge and attitude regarding the care of the elderly with hearing loss was statistically significant (r=0.17; P=0.008). Conclusions The findings of this study show that the nurses have a positive nursing attitude regarding the care of the elderly with hearing loss. It is suggested to develop an educational/training program that can help the nurses accumulate experience and improve their knowledge on the elderly with hearing loss.

Background: Point-of-care testing (POCT) coagulometers are increasingly used for monitoring warfarin therapy. However, in high international normalized ratio (INR) ranges, significant discrepancy in the INR between POCT and conventional laboratory tests occurs. We compared the INR of POCT (CoaguChek XS Plus; Roche Diagnostics, Mannheim, Germany) with that of a conventional laboratory test (ACL TOP 750; Instrumentation Laboratory SpA, Milan, Italy) and explored possible reasons for discrepancy. Methods: Paired POCT and conventional laboratory test INRs were analyzed in 400 samples from 126 patients undergoing warfarin therapy after cardiac surgery. Coagulation factor and thrombin generation tests were compared using the Mann–Whitney U test. Correlations between coagulation factors and INRs were determined using Pearson correlation coefficients. Results: The mean difference in the INR between the tests increased at high INR ranges. Endogenous thrombin potential levels were decreased at INR <2.0 for CoaguChek XS Plus and 2.0< INR <3.0 for ACL TOP 750 compared with those at INR <2.0 for both tests, indicating a better performance of ACL TOP 750 in assessing thrombin changes. The correlation coefficients of coagulation factors were stronger for ACL TOP 750 INR than for CoaguChek XS Plus INR. Vitamin K-dependent coagulation factors were found to contribute to the INR discrepancy. Conclusions Decreases in vitamin K-dependent coagulation and anticoagulation factors can explain the significant discrepancy between the two tests in high INR ranges. Since conventional laboratory test INR values are more reliable than POCT INR values, a confirmatory conventional laboratory test is required for high INR ranges.

Background: The International Agency for Research on Cancer classifies 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) as a known carcinogen. This study aimed to investigate the association between exposure to secondhand smoke (SHS) and NNAL concentrations in non-smokers. Methods: This was a cross-sectional study based on data from the 2016 to 2018 Korea National Health and Nutrition Examination Survey. Urine NNAL concentrations were categorized into tertiles of 3,615 study participants who were non-smokers. All sampling and weight variables were stratified, and analyses to account for the complex sampling design were conducted. Results: The overall, male, and female percentages of SHS exposure among non-smokers were 22.4%, 29.2%, and 20.4%, respectively. The geometric means of urine NNAL concentrations were 1.896±0.098 pg/mL and 1.094±0.028 pg/mL in the SHS exposure and non-exposure groups, respectively. After adjusting for confounding variables, in the total group, the geometric mean of urine NNAL concentrations was significantly higher in the SHS exposure group than in the SHS non-exposure group (adjusted P-value <0.001). Compared with the non-exposure group, the adjusted odds ratios (95% confidence intervals) for the highest NNAL tertile group of overall SHS exposure in the total, men, and women groups were 2.44 (1.95–3.05), 1.65 (1.08–2.53), and 2.73 (2.11–3.52), respectively, after full adjustment. Conclusion The urine NNAL concentration in the SHS exposure group was significantly higher than that in the non-exposure group. Exposure to SHS was associated with a higher risk of elevated urine NNAL concentrations in non-smokers.