Purpose: This study aimed to perform a genome characterization of Streptococcus mitis KHUD 011, a strain isolated from the oral microbiome of a healthy Korean individual, and to compare its genomic features with other S. mitis strains. Materials and Methods: The strain was identified through 16S rRNA gene sequencing, and its genome was sequenced using the PacBio Sequel II platform. De novo assembly and annotation were performed, followed by comparative genomic analysis with three additional strains (S. mitis NCTC 12261, S022-V3-A4, and B6). Pan-genome and phylogenetic analyses were conducted to identify strain-specific genes and assess inter-strain genomic diversity. Results: The genome of S. mitis KHUD 011 consisted of 1,782 protein-coding genes, with a G+C content of 40.24%. Pan-genome analysis identified 1,263 core gene clusters (50.0%), 496 dispensable clusters (19.7%), and 763 strain-specific clusters (30.3%). KHUD 011 displayed 88 strain-specific genes, particularly associated with cell wall/membrane biogenesis, transcriptional regulation, and carbohydrate metabolism. Phylogenetic analysis placed KHUD 011 closely with NCTC 12261, forming a distinct cluster apart from other strains. Conclusion The genome characterization of S. mitis KHUD 011 underscores substantial inter-strain genomic diversity influenced by host interactions, ecological niches, and health status. The identified strain-specific genes, particularly those associated with cell wall/ membrane biogenesis, transcriptional regulation, and carbohydrate metabolism, suggest adaptations to the oral microbiome and its interaction with the host. These findings highlight the ecological versatility of S. mitis and the importance of exploring strains from diverse environments to better understand their role within the host and the broader microbiome.

Purpose: This study aimed to perform a genome characterization of Streptococcus mitis KHUD 011, a strain isolated from the oral microbiome of a healthy Korean individual, and to compare its genomic features with other S. mitis strains. Materials and Methods: The strain was identified through 16S rRNA gene sequencing, and its genome was sequenced using the PacBio Sequel II platform. De novo assembly and annotation were performed, followed by comparative genomic analysis with three additional strains (S. mitis NCTC 12261, S022-V3-A4, and B6). Pan-genome and phylogenetic analyses were conducted to identify strain-specific genes and assess inter-strain genomic diversity. Results: The genome of S. mitis KHUD 011 consisted of 1,782 protein-coding genes, with a G+C content of 40.24%. Pan-genome analysis identified 1,263 core gene clusters (50.0%), 496 dispensable clusters (19.7%), and 763 strain-specific clusters (30.3%). KHUD 011 displayed 88 strain-specific genes, particularly associated with cell wall/membrane biogenesis, transcriptional regulation, and carbohydrate metabolism. Phylogenetic analysis placed KHUD 011 closely with NCTC 12261, forming a distinct cluster apart from other strains. Conclusion The genome characterization of S. mitis KHUD 011 underscores substantial inter-strain genomic diversity influenced by host interactions, ecological niches, and health status. The identified strain-specific genes, particularly those associated with cell wall/ membrane biogenesis, transcriptional regulation, and carbohydrate metabolism, suggest adaptations to the oral microbiome and its interaction with the host. These findings highlight the ecological versatility of S. mitis and the importance of exploring strains from diverse environments to better understand their role within the host and the broader microbiome.

Purpose: This study aimed to perform a genome characterization of Streptococcus mitis KHUD 011, a strain isolated from the oral microbiome of a healthy Korean individual, and to compare its genomic features with other S. mitis strains. Materials and Methods: The strain was identified through 16S rRNA gene sequencing, and its genome was sequenced using the PacBio Sequel II platform. De novo assembly and annotation were performed, followed by comparative genomic analysis with three additional strains (S. mitis NCTC 12261, S022-V3-A4, and B6). Pan-genome and phylogenetic analyses were conducted to identify strain-specific genes and assess inter-strain genomic diversity. Results: The genome of S. mitis KHUD 011 consisted of 1,782 protein-coding genes, with a G+C content of 40.24%. Pan-genome analysis identified 1,263 core gene clusters (50.0%), 496 dispensable clusters (19.7%), and 763 strain-specific clusters (30.3%). KHUD 011 displayed 88 strain-specific genes, particularly associated with cell wall/membrane biogenesis, transcriptional regulation, and carbohydrate metabolism. Phylogenetic analysis placed KHUD 011 closely with NCTC 12261, forming a distinct cluster apart from other strains. Conclusion The genome characterization of S. mitis KHUD 011 underscores substantial inter-strain genomic diversity influenced by host interactions, ecological niches, and health status. The identified strain-specific genes, particularly those associated with cell wall/ membrane biogenesis, transcriptional regulation, and carbohydrate metabolism, suggest adaptations to the oral microbiome and its interaction with the host. These findings highlight the ecological versatility of S. mitis and the importance of exploring strains from diverse environments to better understand their role within the host and the broader microbiome.

Purpose: This study aimed to perform a genome characterization of Streptococcus mitis KHUD 011, a strain isolated from the oral microbiome of a healthy Korean individual, and to compare its genomic features with other S. mitis strains. Materials and Methods: The strain was identified through 16S rRNA gene sequencing, and its genome was sequenced using the PacBio Sequel II platform. De novo assembly and annotation were performed, followed by comparative genomic analysis with three additional strains (S. mitis NCTC 12261, S022-V3-A4, and B6). Pan-genome and phylogenetic analyses were conducted to identify strain-specific genes and assess inter-strain genomic diversity. Results: The genome of S. mitis KHUD 011 consisted of 1,782 protein-coding genes, with a G+C content of 40.24%. Pan-genome analysis identified 1,263 core gene clusters (50.0%), 496 dispensable clusters (19.7%), and 763 strain-specific clusters (30.3%). KHUD 011 displayed 88 strain-specific genes, particularly associated with cell wall/membrane biogenesis, transcriptional regulation, and carbohydrate metabolism. Phylogenetic analysis placed KHUD 011 closely with NCTC 12261, forming a distinct cluster apart from other strains. Conclusion The genome characterization of S. mitis KHUD 011 underscores substantial inter-strain genomic diversity influenced by host interactions, ecological niches, and health status. The identified strain-specific genes, particularly those associated with cell wall/ membrane biogenesis, transcriptional regulation, and carbohydrate metabolism, suggest adaptations to the oral microbiome and its interaction with the host. These findings highlight the ecological versatility of S. mitis and the importance of exploring strains from diverse environments to better understand their role within the host and the broader microbiome.

OBJECTIVES: This study aimed to establish the validity—specifically, the sensitivity and specificity—of the screening questionnaire and diagnostic criteria for oral frailty proposed by the Korean Academy of Geriatric Dentistry (KAGD) among community-dwelling older adults. METHODS: This study enrolled 100 participants. Among various definitions of oral frailty, this study used the criteria proposed by Tanaka as the reference test. The screening questionnaire consisted of 11 items for screening physical frailty, chewing ability, swallowing difficulties, oral dryness, and tongue and lip motor function. Each question had a different scoring weight, and if the total score was 1 or higher, an oral frailty diagnostic examination proposed by the KAGD would be recommended. The diagnostic test was the oral frailty diagnostic criteria proposed by the KAGD including 6 measures: chewing ability, occlusal force, tongue pressure, oral dryness, swallowing difficulty, and oral hygiene. If a participant exhibited 2 or more positive measures, this participant was classified as “oral frail.” The screening questionnaire was analyzed using a cut-off value of 1 or higher, while the diagnostic criteria utilized a cut-off of 2 or more positive measures. Sensitivity and specificity were calculated. RESULTS: The screening questionnaire showed significant power for screening oral frailty (area under the receiver operating characteristic curve, 0.783; sensitivity, 87.8%; specificity, 52.5%). The diagnostic accuracy of the newly proposed diagnostic criteria was acceptable (sensitivity, 95.1%; specificity, 42.4%). CONCLUSIONS The newly proposed screening questionnaire and diagnostic criteria in Korea appear to be a useful tool to identify oral frailty in community-dwelling older adults.

Background: The currently recommended pre-transfusion testing techniques for patients with autoantibodies are complex, time-consuming, and labor-intensive. Therefore, although the red blood cell (RBC) selection method using crossmatched RBC agglutination reaction grades (i.e., the “least incompatible” transfusion) is discouraged, many institutions still use it. We aimed to evaluate the effectiveness of this method combined with Rh subgroup phenotyping. Methods: We retrospectively investigated RBC transfusions from January 2019 to December 2021 in patients presenting as auto-control-positive via antibody identification (auto-control (+) group), where Rh subgroup phenotype-matched RBCs were selected based on the agglutination reaction grades of crossmatched units. For each study patient, an auto-control-negative patient was matched based on age, sex, department, and pre-transfusion Hb levels (auto-control (−) group). The mean Hb change per unit, transfusion-associated symptom/sign reports, and agglutination reaction grades upon crossmatching were analyzed. Results: In the auto-control (+) group, the Hb change per unit among different agglutination reaction grades of transfused RBCs and among different relative grades of transfused RBCs and crossmatching auto-controls was not significantly different (P=0.392 and P= 0.132, respectively). No significant difference was observed in Hb changes and transfusion-associated symptom/sign occurrence between the auto-control (+) and auto-control (−) groups (P=0.121 and P=0.822, respectively). In addition, no definite evidence of hemolysis in the auto-control (+) group was observed in the medical record review. Conclusions Together with Rh subgroup phenotyping, selecting the RBC unit with the lowest agglutination reaction grade upon crossmatching does not adversely affect transfusion efficiency.

Purpose: Renal tumors account for approximately 7% of all childhood cancers. These include Wilms tumor (WT), clear cell sarcoma of the kidney (CCSK), malignant rhabdoid tumor of the kidney (MRTK), renal cell carcinoma (RCC), congenital mesoblastic nephroma (CMN) and other rare tumors. We investigated the epidemiology of pediatric renal tumors in Korea. Materials and Methods: From January 2001 to December 2015, data of pediatric patients (0–18 years) newly-diagnosed with renal tumors at 26 hospitals were retrospectively analyzed. Results: Among 439 patients (male, 240), the most common tumor was WT (n=342, 77.9%), followed by RCC (n=36, 8.2%), CCSK (n=24, 5.5%), MRTK (n=16, 3.6%), CMN (n=12, 2.7%), and others (n=9, 2.1%). Median age at diagnosis was 27.1 months (range 0-225.5) and median follow-up duration was 88.5 months (range 0-211.6). Overall, 32 patients died, of whom 17, 11, 1, and 3 died of relapse, progressive disease, second malignant neoplasm, and treatment-related mortality. Five-year overall survival and event free survival were 97.2% and 84.8% in WT, 90.6% and 82.1% in RCC, 81.1% and 63.6% in CCSK, 60.3% and 56.2% in MRTK, and 100% and 91.7% in CMN, respectively (p < 0.001). Conclusion The pediatric renal tumor types in Korea are similar to those previously reported in other countries. WT accounted for a large proportion and survival was excellent. Non-Wilms renal tumors included a variety of tumors and showed inferior outcome, especially MRTK. Further efforts are necessary to optimize the treatment and analyze the genetic characteristics of pediatric renal tumors in Korea.

Purpose: This study aimed to identify levels of perception and performance of end-oflife care among nurses and to investigate correlations between perception and performance. Methods: This cross-sectional descriptive survey included 321 nurses from a tertiary hospital in Seoul, Korea. The participants had at least 6 months of work experience and had been involved in end-of-life care at least once, in either ward or intensive care unit settings.A structured questionnaire was utilized to assess their perception and performance of endof-life care. Results: The mean score for perception of end-of-life care was 3.23±0.34, while the score for performance of end-of-life care was 3.08±0.34. There was a significant positive correlation between nurses’ perception of end-of-life care and their performance in this area (r=0.78, P＜0.001). Conclusion It is necessary to change perceptions regarding end-of-life care and to develop systematic and standardized education programs including content such as assessing the hydration status of dying patients, evaluating mental aspects such as suicidal ideation, and providing spiritual care for nurses working in end-of-life departments.

The Dia and Mia antigens have been detected in Koreans with a frequency of 6.4∼14.5% and 0.9%, respectively. This study evaluated the effectiveness of different screening cells using the cells with Diaand Mia antigens for unexpected antibody screening. An unexpected antibody-screening test was performed separately using different screening cells, including the Dia antigen (Panel D) and Mia antigen (Panel M). A total of 2,077 specimens from 1,847 patients were collected, among which 49 (2.32%) and 43 (2.08%) were positive using Panel D and Panel M, respectively. Twenty-seven patients were positive with both panels, 2012 were negative with both panels, and thirty-eight patients showed a discordant result. The suspected anti-Diaand anti-Mia were detected in 4 (0.19%) and 5 (0.24%) patients, respectively. Therefore, the frequency of anti-Dia and anti-Mia antibodies in this study may be helpful for selecting unexpected antibody screening reagents.