This study investigated the molecular mechanism of phenotypic switching of vascular smooth muscle cells (VSMCs), which play a crucial role in vascular remodeling using 9H-Carbazol-3-yl 4-aminobenzoate (CAB). CAB significantly attenuated platelet-derived growth factor (PDGF)-induced VSMC proliferation and migration. CAB suppressed PDGF-induced STAT3 activation by directly binding to the SH2 domain of STAT3. Downregulation of STAT3 phosphorylation by CAB attenuated CIAPIN1/JAK2/STAT3 axis through a decrease in CIAPIN1 transcription. Furthermore, abrogated CIAPIN1 decreased KLF4-mediated VSMC dedifferentiation and increased CDKN1B-induced cell cycle arrest and MMP9 suppression. CAB inhibited intimal hyperplasia in injury-induced neointima animal models by inhibition of the CIAPIN1/JAK2/STAT3 axis. However, CIAPIN1 overexpression attenuated CAB-mediated suppression of VSMC proliferation, migration, phenotypic switching, and intimal hyperplasia. Our study clarified the molecular mechanism underlying STAT3 inhibition of VSMC phenotypic switching and vascular remodeling and identified novel active CAB. These findings demonstrated that STAT3 can be a major regulator to control CIAPIN1/JAK2/STAT3 axis that may be a therapeutic target for treating vascular proliferative diseases.

Serologic ABO typing might be hampered in some patients with hematologic malignancies.We performed ABO genotyping using next-generation sequencing as part of a routine hematologic malignancy gene panel to determine the ABO blood type of patients with hematologic malignancies. Targeted sequencing of seven ABO gene exons was performed within a hematologic malignancy gene panel for 520 patients diagnosed with various hematologic malignancies. The distribution of predicted ABO blood phenotypes determined through genotyping was as follows: 33.3% A, 27.3% B, 26.7% O, and 12.7% AB. No significant associations were identified between ABO allele distributions and specific hematologic malignancy diagnoses. We compared the phenotypes predicted using ABO genotyping with serological ABO testing results in 502 samples where serological data were available. All genotyping-based phenotypes were accurate, with 99.8% (501/502) of initial serological results aligning with the true phenotypes. Unusual serological results were observed in 21 samples (4.2%). The percentages of recipient cells containing ABO allele variants indicated chimerism in relapsed patients who had undergone ABO-mismatched transplantation. Thus, incorporating ABO genotyping into the hematology gene panel provides valuable information offering a cost-effective approach to address challenges in blood typing and post-transplant care.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.