BACKGROUND: Recently, the rate of infections with non-tuberculous mycobacteria (NTM) has been increasing in Korea. Precise identification of NTM is critical to determination of the pathogen and to target treatment of NTM patients. METHODS: Sixty-eight unclassified mycobacteria isolates by rpoB PCR-RFLP assay (PRA) collected in 2008 were analyzed by National Center for Biotechnology Information (NCBI) Basic Local Alignment Search Tool (BLAST) search after sequencing of 16S rRNA, hsp65, rpoB genes. RESULTS: Nineteen strains of 68 isolates were specified as species after sequencing analysis of 3 gene types. We found 3 M. lentifulavum, 5 M. arupense, 4 M. triviale, 4 M. parascrofulaceum, and one M. obuense. One M. tuberculosis and another M. peregrinum were mutated at the Msp I recognition site needed for rpoB PRA. The remaining 49 isolates did not coincide with identical species at the 3 kinds genes. CONCLUSION: Sequencing analysis of 16S rRNA, hsp65, rpoB was useful for identification of NTM unclassified by rpoB PRA.
Biotechnology ; Korea ; Nontuberculous Mycobacteria ; RNA, Ribosomal, 16S ; Tuberculosis

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: 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: 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: 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: Catheter-associated urinary tract infections (CAUTIs) account for a large proportion of healthcare-associated infections and have a significant impact on morbidity, length of hospital stay, and mortality. Adherence to the recommended infection prevention practices can effectively reduce the incidence of CAUTIs. This study aimed to assess the characteristics of CAUTIs and the efficacy of prevention programs across hospitals of various sizes. Methods: Intervention programs, including training, surveillance, and monitoring, were implemented. Data on the microorganisms responsible for CAUTIs, urinary catheter utilization ratio, rate of CAUTIs per 1,000 device days, and factors associated with the use of indwelling catheters were collected from 2017 to 2019. The incidence of CAUTIs and associated data were compared between university hospitals and small- and medium-sized hospitals. Results: Thirty-two hospitals participated in the study, including 21 university hospitals and 11 small- and medium-sized hospitals. The microorganisms responsible for CAUTIs and their resistance rates did not differ between the two groups. In the first quarter of 2018, the incidence rate was 2.05 infections/1,000 device-days in university hospitals and 1.44 infections/1,000 device-days in small- and medium-sized hospitals. After implementing interventions, the rate gradually decreased in the first quarter of 2019, with 1.18 infections/1,000 device-days in university hospitals and 0.79 infections/1,000 device-days in small- and medium-sized hospitals. However, by the end of the study, the infection rate increased to 1.74 infections/1,000 device-days in university hospitals and 1.80 infections/1,000 device-days in small- and medium-sized hospitals. Conclusion We implemented interventions to prevent CAUTIs and evaluated their outcomes. The incidence of these infections decreased in the initial phases of the intervention when adequate support and personnel were present. The rate of these infections may be reduced by implementing active interventions such as consistent monitoring and adherence to guidelines for preventing infections.