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.

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.

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.

Objective: The objectives of this study were to compare the time-dependent changes in occlusal contact area (OCA) and bite force (BF) of the deviated and non-deviated sides in mandibular prognathic patients with mandibular asymmetry before and after orthognathic surgery and investigate the factors associated with the changes in OCA and BF on each side. Methods: The sample consisted of 67 patients (33 men and 34 women; age range 15-36 years) with facial asymmetry who underwent 2-jaw orthognathic surgery. OCA and BF were taken before presurgical orthodontic treatment, within 1 month before surgery, and 1 month, 3 months, 6 months, 1 year, and 2 years after surgery. OCA and BF were measured using the Dental Prescale System. Results: The OCA and BF decreased gradually before surgery and increased after surgery on both sides. The OCA and BF were significantly greater on the deviated side than on the non-deviated side before surgery, and there was no difference after surgery. According to the linear mixed-effect model, only the changes in the mandibular plane angle had a significant effect on BF (p < 0.05). Conclusions There was a difference in the amount of the OCA and BF between the deviated and non-deviated sides before surgery. The change in mandibular plane angle affects the change, especially on the non-deviated side, during the observation period.

Background: The maximum surgical blood order schedule (MSBOS) is a list of surgical procedures with the corresponding recommended number of blood units. Nevertheless, with the advances in surgery and transfusion medicine, a need for updates in the MSBOS has been suggested. This study evaluated the need for regular revision of the MSBOS. Methods: The surgical procedures performed between August 2016 and July 2021 were investigated retrospectively. The transfused blood units for each type of surgery were analyzed in elective, single surgeries performed more than ten times per year. The Transfusion index (TI) and the Transfusion probability (TP) for each type of operation were calculated in five one-year intervals. Furthermore, the surgeries performed more than 10 times in all one-year intervals and presented a TI≥0.5 at least once during the study period were subjected to further analysis. Results: A total of 96,040 elective surgical procedures were performed during the five-year study period, including 77,639 single surgeries performed ≥10 times in one year. The average transfused blood units and the average TP per year decreased over time. In addition, the percentage of the number and type of operations presenting TI≥0.5 changed. Among the 27 surgeries that were further studied, six showed constant TI≥0.5; six changed from TI≥0.5 to ＜0.5, and 15 displayed fluctuations in TI. Conclusion Changes in surgical blood utilization was observed among one-year periods, which implies the need for regular revision of MSBOS.

Adverse transfusion reactions (ATRs) are unexpected reactions to transfusion. This study examined the frequency, types of ATRs, and related blood products retrospectively in pediatric patients with less information than in adult patients. Four hundred and forty transfusions were performed for two months at a children’s hospital: 247 units of red blood cell (RBC) products, 142 units of platelet products, and 41 units of fresh frozen plasma (FFP) were used.Five adverse reactions occurred in five patients, three cases were allergic reactions, and two were febrile nonhemolytic reactions. The frequency was 1.13%, and apheresis platelets and leukocyte-reduced RBC were transfused. Two patients’ ATRs were found in the previous transfusions, and ATRs were repeated in subsequent transfusions in one patient. One of the ATRs was not reported to the blood bank and was then discovered during the study. Because pediatric patients may have limitations in recognizing or expressing their symptoms compared to adults, medical staff rely solely on vital signs and laboratory results rather than symptoms, causing difficulty in noticing ATRs. Information on ATRs and education on appropriate blood products will improve awareness of ATRs and blood management among medical staff at transfusion sites and blood banks.