Background and Objectives: Although the clinical consequences of advanced heart failure (HF) may be similar across different etiologies of cardiomyopathies, their proteomic expression may show substantial differences in relation to underlying pathophysiology. We aimed to identify myocardial tissue–based proteomic characteristics and the underlying molecular pathophysiology in non-ischemic cardiomyopathy with different etiologies. Methods: Comparative extensive proteomic analysis of the myocardium was performed in nine patients with biopsy-proven non-ischemic cardiomyopathies (3 dilated cardiomyopathy [DCM], 2 hypertrophic cardiomyopathy [HCM], and 4 myocarditis) as well as five controls using tandem mass tags combined with liquid chromatography–mass spectrometry.Differential protein expression analysis, Gene Ontology (GO) analysis, and Ingenuity Pathway Analysis (IPA) were performed to identify proteomic differences and molecular mechanisms in each cardiomyopathy type compared to the control. Proteomic characteristics were further evaluated in accordance with clinical and pathological findings. Results: The principal component analysis score plot showed that the controls, DCM, and HCM clustered well. However, myocarditis samples exhibited scattered distribution. IPA revealed the downregulation of oxidative phosphorylation and upregulation of the sirtuin signaling pathway in both DCM and HCM. Various inflammatory pathways were upregulated in myocarditis with the downregulation of Rho GDP dissociation inhibitors. The molecular pathophysiology identified by extensive proteomic analysis represented the clinical and pathological properties of each cardiomyopathy with abundant proteomes. Conclusions Different etiologies of non-ischemic cardiomyopathies in advanced HF exhibit distinct proteomic expression despite shared pathologic findings. The benefit of tailored management strategies considering the different proteomic expressions in non-ischemic advanced HF requires further investigation.

Background and Objectives: Although the clinical consequences of advanced heart failure (HF) may be similar across different etiologies of cardiomyopathies, their proteomic expression may show substantial differences in relation to underlying pathophysiology. We aimed to identify myocardial tissue–based proteomic characteristics and the underlying molecular pathophysiology in non-ischemic cardiomyopathy with different etiologies. Methods: Comparative extensive proteomic analysis of the myocardium was performed in nine patients with biopsy-proven non-ischemic cardiomyopathies (3 dilated cardiomyopathy [DCM], 2 hypertrophic cardiomyopathy [HCM], and 4 myocarditis) as well as five controls using tandem mass tags combined with liquid chromatography–mass spectrometry.Differential protein expression analysis, Gene Ontology (GO) analysis, and Ingenuity Pathway Analysis (IPA) were performed to identify proteomic differences and molecular mechanisms in each cardiomyopathy type compared to the control. Proteomic characteristics were further evaluated in accordance with clinical and pathological findings. Results: The principal component analysis score plot showed that the controls, DCM, and HCM clustered well. However, myocarditis samples exhibited scattered distribution. IPA revealed the downregulation of oxidative phosphorylation and upregulation of the sirtuin signaling pathway in both DCM and HCM. Various inflammatory pathways were upregulated in myocarditis with the downregulation of Rho GDP dissociation inhibitors. The molecular pathophysiology identified by extensive proteomic analysis represented the clinical and pathological properties of each cardiomyopathy with abundant proteomes. Conclusions Different etiologies of non-ischemic cardiomyopathies in advanced HF exhibit distinct proteomic expression despite shared pathologic findings. The benefit of tailored management strategies considering the different proteomic expressions in non-ischemic advanced HF requires further investigation.

Purpose: This study aims to find ways to reduce the reality shock of newly graduated nurses by exploring the relationship between nursing practice readiness, social support from clinical nurse educators, and reality shock. Methods: From August 24 to September 13, 2023, data were collected from 134 newly graduated nurses with less than 12 months of clinical experience, who were trained by clinical nurse educators at one general hospital and two tertiary hospitals in I City and G Province. The data were analyzed using descriptive statistics, independent t-test, one-way analysis of variance, Pearson’s correlation coefficient, and hierarchical multiple regression with the SPSS/WIN 28.0 program. Results: Reality shock was negatively correlated with nursing practice readiness (r=-.50, p<.001) and social support (r=-.19, p=.03), while nursing practice readiness was positively correlated with social support (r=.37, p<.001). Nursing practice readiness was identified as a significant predictor of reality shock in newly graduated nurses (β=-.46, p<.001), accounting for 31% of the variance in reality shock (F=18.82, p<.001). Conclusion To alleviate the reality shock of newly graduated nurses, it is important to improve their practice readiness and ensure systematic, continuous education for clinical nurse educators.

Purpose: This study aims to find ways to reduce the reality shock of newly graduated nurses by exploring the relationship between nursing practice readiness, social support from clinical nurse educators, and reality shock. Methods: From August 24 to September 13, 2023, data were collected from 134 newly graduated nurses with less than 12 months of clinical experience, who were trained by clinical nurse educators at one general hospital and two tertiary hospitals in I City and G Province. The data were analyzed using descriptive statistics, independent t-test, one-way analysis of variance, Pearson’s correlation coefficient, and hierarchical multiple regression with the SPSS/WIN 28.0 program. Results: Reality shock was negatively correlated with nursing practice readiness (r=-.50, p<.001) and social support (r=-.19, p=.03), while nursing practice readiness was positively correlated with social support (r=.37, p<.001). Nursing practice readiness was identified as a significant predictor of reality shock in newly graduated nurses (β=-.46, p<.001), accounting for 31% of the variance in reality shock (F=18.82, p<.001). Conclusion To alleviate the reality shock of newly graduated nurses, it is important to improve their practice readiness and ensure systematic, continuous education for clinical nurse educators.

Purpose: This study aims to find ways to reduce the reality shock of newly graduated nurses by exploring the relationship between nursing practice readiness, social support from clinical nurse educators, and reality shock. Methods: From August 24 to September 13, 2023, data were collected from 134 newly graduated nurses with less than 12 months of clinical experience, who were trained by clinical nurse educators at one general hospital and two tertiary hospitals in I City and G Province. The data were analyzed using descriptive statistics, independent t-test, one-way analysis of variance, Pearson’s correlation coefficient, and hierarchical multiple regression with the SPSS/WIN 28.0 program. Results: Reality shock was negatively correlated with nursing practice readiness (r=-.50, p<.001) and social support (r=-.19, p=.03), while nursing practice readiness was positively correlated with social support (r=.37, p<.001). Nursing practice readiness was identified as a significant predictor of reality shock in newly graduated nurses (β=-.46, p<.001), accounting for 31% of the variance in reality shock (F=18.82, p<.001). Conclusion To alleviate the reality shock of newly graduated nurses, it is important to improve their practice readiness and ensure systematic, continuous education for clinical nurse educators.

Background and Objectives: Although the clinical consequences of advanced heart failure (HF) may be similar across different etiologies of cardiomyopathies, their proteomic expression may show substantial differences in relation to underlying pathophysiology. We aimed to identify myocardial tissue–based proteomic characteristics and the underlying molecular pathophysiology in non-ischemic cardiomyopathy with different etiologies. Methods: Comparative extensive proteomic analysis of the myocardium was performed in nine patients with biopsy-proven non-ischemic cardiomyopathies (3 dilated cardiomyopathy [DCM], 2 hypertrophic cardiomyopathy [HCM], and 4 myocarditis) as well as five controls using tandem mass tags combined with liquid chromatography–mass spectrometry.Differential protein expression analysis, Gene Ontology (GO) analysis, and Ingenuity Pathway Analysis (IPA) were performed to identify proteomic differences and molecular mechanisms in each cardiomyopathy type compared to the control. Proteomic characteristics were further evaluated in accordance with clinical and pathological findings. Results: The principal component analysis score plot showed that the controls, DCM, and HCM clustered well. However, myocarditis samples exhibited scattered distribution. IPA revealed the downregulation of oxidative phosphorylation and upregulation of the sirtuin signaling pathway in both DCM and HCM. Various inflammatory pathways were upregulated in myocarditis with the downregulation of Rho GDP dissociation inhibitors. The molecular pathophysiology identified by extensive proteomic analysis represented the clinical and pathological properties of each cardiomyopathy with abundant proteomes. Conclusions Different etiologies of non-ischemic cardiomyopathies in advanced HF exhibit distinct proteomic expression despite shared pathologic findings. The benefit of tailored management strategies considering the different proteomic expressions in non-ischemic advanced HF requires further investigation.

Purpose: This study aims to find ways to reduce the reality shock of newly graduated nurses by exploring the relationship between nursing practice readiness, social support from clinical nurse educators, and reality shock. Methods: From August 24 to September 13, 2023, data were collected from 134 newly graduated nurses with less than 12 months of clinical experience, who were trained by clinical nurse educators at one general hospital and two tertiary hospitals in I City and G Province. The data were analyzed using descriptive statistics, independent t-test, one-way analysis of variance, Pearson’s correlation coefficient, and hierarchical multiple regression with the SPSS/WIN 28.0 program. Results: Reality shock was negatively correlated with nursing practice readiness (r=-.50, p<.001) and social support (r=-.19, p=.03), while nursing practice readiness was positively correlated with social support (r=.37, p<.001). Nursing practice readiness was identified as a significant predictor of reality shock in newly graduated nurses (β=-.46, p<.001), accounting for 31% of the variance in reality shock (F=18.82, p<.001). Conclusion To alleviate the reality shock of newly graduated nurses, it is important to improve their practice readiness and ensure systematic, continuous education for clinical nurse educators.

Background and Objectives: Although the clinical consequences of advanced heart failure (HF) may be similar across different etiologies of cardiomyopathies, their proteomic expression may show substantial differences in relation to underlying pathophysiology. We aimed to identify myocardial tissue–based proteomic characteristics and the underlying molecular pathophysiology in non-ischemic cardiomyopathy with different etiologies. Methods: Comparative extensive proteomic analysis of the myocardium was performed in nine patients with biopsy-proven non-ischemic cardiomyopathies (3 dilated cardiomyopathy [DCM], 2 hypertrophic cardiomyopathy [HCM], and 4 myocarditis) as well as five controls using tandem mass tags combined with liquid chromatography–mass spectrometry.Differential protein expression analysis, Gene Ontology (GO) analysis, and Ingenuity Pathway Analysis (IPA) were performed to identify proteomic differences and molecular mechanisms in each cardiomyopathy type compared to the control. Proteomic characteristics were further evaluated in accordance with clinical and pathological findings. Results: The principal component analysis score plot showed that the controls, DCM, and HCM clustered well. However, myocarditis samples exhibited scattered distribution. IPA revealed the downregulation of oxidative phosphorylation and upregulation of the sirtuin signaling pathway in both DCM and HCM. Various inflammatory pathways were upregulated in myocarditis with the downregulation of Rho GDP dissociation inhibitors. The molecular pathophysiology identified by extensive proteomic analysis represented the clinical and pathological properties of each cardiomyopathy with abundant proteomes. Conclusions Different etiologies of non-ischemic cardiomyopathies in advanced HF exhibit distinct proteomic expression despite shared pathologic findings. The benefit of tailored management strategies considering the different proteomic expressions in non-ischemic advanced HF requires further investigation.

Background: The Korean Red Cross has conducted serologic tests for C, c, E, e antigens and found 18 D-- donors.In this study, we performed RHCE genotyping to identify the molecular characteristics of the serologic D-- blood type in Korean blood donors. Methods: We performed RHCE-specific PCR-based electrophoresis to check the amplification pattern of each exon.Sanger sequencing was conducted to find the variants in the nucleotide sequence. We determined the RHCE genotype based on the electrophoresis and Sanger sequencing results. Results: Total eight out of 18 D-- donors were participated in this research. In the PCR-based electrophoresis tests, RHCE exons 3, 4, and 6 were not amplified in samples #4, #6, and #8. Also, sample #2 showed an abnormal band pattern of RHCE exon 9. The Sanger sequencing results showed that the nucleotide sequences of the RHCE exons 5, 7, and 8 in samples #4, #6 and #8 corresponded to the nucleotide sequences of RHD exons 5, 7, and 8, respectively, suggesting the possibility of a RHCE-RHD(3-8)-RHCE hybrid allele. The nucleotide sequences of RHCE exons 7 and 8 in sample #2 were the same as the nucleotide sequences of RHD exons 7 and 8, respectively.In samples #1, #3, #5, and #7, no specific variants known to cause D-- phenotype were found. Conclusion RHCE genes partially replaced by the RHD genes were found in four out of eight participants and three of them were identified as ?RHCE*02N.07, which is known as the RHCE null allele. A further study with complete RHCE sequencing could be helpful for an understanding of the molecular mechanisms of samples in which no significant variants were identified.

Concerns about the effectiveness of current vaccines against the rapidly spreading severe acute respiratory syndrome-coronavirus-2 omicron (B.1.1.529) variant are increasing. This study aimed to assess neutralizing antibody activity against the wild-type (BetaCoV/Korea/ KCDC03/2020), delta, and omicron variants after full primary and booster vaccinations with BNT162b2. A plaque reduction neutralization test was employed to determine 50% neutralizing dilution (ND 50 ) titers in serum samples. ND 50 titers against the omicron variant (median [interquartile range], 5.3 [< 5.0–12.7]) after full primary vaccination were lower than those against the wild-type (144.8 [44.7–294.0]) and delta (24.3 [14.3–81.1]) variants.Furthermore, 19/30 participants (63.3%) displayed lower ND 50 titers than the detection threshold (< 10.0) against omicron after full primary vaccination. However, the booster vaccine significantly increased ND 50 titers against BetaCoV/Korea/KCDC03/2020, delta, and omicron, although titers against omicron remained lower than those against the other variants (P < 0.001). Our study suggests that booster vaccination with BNT162b2 significantly increases humoral immunity against the omicron variant.