Objective: To assess the feasibility of ultrafast brain magnetic resonance imaging (MRI) in pediatric patients. Materials and Methods: We retrospectively reviewed 194 pediatric patients aged 0 to 19 years (median 10.2 years) who underwent both ultrafast and conventional brain MRI between May 2019 and August 2020. Ultrafast MRI sequences included T1 and T2-weighted images (T1WI and T2WI), fluid-attenuated inversion recovery (FLAIR), T2*-weighted image (T2*WI), and diffusion-weighted image (DWI). Qualitative image quality and lesion evaluations were conducted on 5-point Likert scales by two blinded radiologists, with quantitative assessment of lesion count and size on T1WI, T2WI, and FLAIR sequences for each protocol. Wilcoxon signed-rank tests and intraclass correlation coefficient (ICC) analyses were used for comparison. Results: The total scan times for equivalent image contrasts were 1 minute 44 seconds for ultrafast MRI and 15 minutes 30 seconds for conventional MRI. Overall, image quality was lower in ultrafast MRI than in conventional MRI, with mean quality scores ranging from 2.0 to 4.8 for ultrafast MRI and 4.8 to 5.0 for conventional MRI across sequences (P < 0.001 for T1WI, T2WI, FLAIR, and T2*WI for both readers; P = 0.018 [reader 1] and 0.031 [reader 2] for DWI). Lesion detection rates on ultrafast MRI relative to conventional MRI were as follows: T1WI, 97.1%; T2WI, 99.6%; FLAIR, 92.9%; T2*WI, 74.1%; and DWI, 100%. The ICC (95% confidence interval) for lesion size measurements between ultrafast and conventional MRI was as follows: T1WI, 0.998 (0.996–0.999); T2WI, 0.998 (0.997–0.999); and FLAIR, 0.99 (0.985–0.994). Conclusion Ultrafast MRI significantly reduces scan time and provides acceptable results, albeit with slightly lower image quality than conventional MRI, for evaluating intracranial abnormalities in pediatric patients.

Objective: To assess the feasibility of ultrafast brain magnetic resonance imaging (MRI) in pediatric patients. Materials and Methods: We retrospectively reviewed 194 pediatric patients aged 0 to 19 years (median 10.2 years) who underwent both ultrafast and conventional brain MRI between May 2019 and August 2020. Ultrafast MRI sequences included T1 and T2-weighted images (T1WI and T2WI), fluid-attenuated inversion recovery (FLAIR), T2*-weighted image (T2*WI), and diffusion-weighted image (DWI). Qualitative image quality and lesion evaluations were conducted on 5-point Likert scales by two blinded radiologists, with quantitative assessment of lesion count and size on T1WI, T2WI, and FLAIR sequences for each protocol. Wilcoxon signed-rank tests and intraclass correlation coefficient (ICC) analyses were used for comparison. Results: The total scan times for equivalent image contrasts were 1 minute 44 seconds for ultrafast MRI and 15 minutes 30 seconds for conventional MRI. Overall, image quality was lower in ultrafast MRI than in conventional MRI, with mean quality scores ranging from 2.0 to 4.8 for ultrafast MRI and 4.8 to 5.0 for conventional MRI across sequences (P < 0.001 for T1WI, T2WI, FLAIR, and T2*WI for both readers; P = 0.018 [reader 1] and 0.031 [reader 2] for DWI). Lesion detection rates on ultrafast MRI relative to conventional MRI were as follows: T1WI, 97.1%; T2WI, 99.6%; FLAIR, 92.9%; T2*WI, 74.1%; and DWI, 100%. The ICC (95% confidence interval) for lesion size measurements between ultrafast and conventional MRI was as follows: T1WI, 0.998 (0.996–0.999); T2WI, 0.998 (0.997–0.999); and FLAIR, 0.99 (0.985–0.994). Conclusion Ultrafast MRI significantly reduces scan time and provides acceptable results, albeit with slightly lower image quality than conventional MRI, for evaluating intracranial abnormalities in pediatric patients.

Objective: To assess the feasibility of ultrafast brain magnetic resonance imaging (MRI) in pediatric patients. Materials and Methods: We retrospectively reviewed 194 pediatric patients aged 0 to 19 years (median 10.2 years) who underwent both ultrafast and conventional brain MRI between May 2019 and August 2020. Ultrafast MRI sequences included T1 and T2-weighted images (T1WI and T2WI), fluid-attenuated inversion recovery (FLAIR), T2*-weighted image (T2*WI), and diffusion-weighted image (DWI). Qualitative image quality and lesion evaluations were conducted on 5-point Likert scales by two blinded radiologists, with quantitative assessment of lesion count and size on T1WI, T2WI, and FLAIR sequences for each protocol. Wilcoxon signed-rank tests and intraclass correlation coefficient (ICC) analyses were used for comparison. Results: The total scan times for equivalent image contrasts were 1 minute 44 seconds for ultrafast MRI and 15 minutes 30 seconds for conventional MRI. Overall, image quality was lower in ultrafast MRI than in conventional MRI, with mean quality scores ranging from 2.0 to 4.8 for ultrafast MRI and 4.8 to 5.0 for conventional MRI across sequences (P < 0.001 for T1WI, T2WI, FLAIR, and T2*WI for both readers; P = 0.018 [reader 1] and 0.031 [reader 2] for DWI). Lesion detection rates on ultrafast MRI relative to conventional MRI were as follows: T1WI, 97.1%; T2WI, 99.6%; FLAIR, 92.9%; T2*WI, 74.1%; and DWI, 100%. The ICC (95% confidence interval) for lesion size measurements between ultrafast and conventional MRI was as follows: T1WI, 0.998 (0.996–0.999); T2WI, 0.998 (0.997–0.999); and FLAIR, 0.99 (0.985–0.994). Conclusion Ultrafast MRI significantly reduces scan time and provides acceptable results, albeit with slightly lower image quality than conventional MRI, for evaluating intracranial abnormalities in pediatric patients.

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: We evaluated severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2)-specific humoral and cellular responses for up to 6 months after the 3rd dose of ancestral coronavirus disease 2019 (COVID-19) vaccination in people living with HIV (PLWH) and healthy controls (HCs) who were not infected with COVID-19. Methods: Anti-spike receptor-binding domain IgG (anti-RBD IgG) concentrations using chemiluminescence immunoassay and neutralizing antibodies using focus reduction neutralization test (FRNT) were assessed at 1 week after each dose of vaccination, and 3 and 6 months after the 3rd dose in 62 PLWH and 25 HCs. T-cell responses using intracellular cytokine stain were evaluated at 1 week before, and 1 week and 6 months after the 3rd dose. Results: At 1 week after the 3rd dose, adequate anti-RBD IgG (> 300 binding antibody unit /mL) was elicited in all PLWH except for one patient with 36 CD4 T-cell count/mm3 . The geometric mean titers of 50% FRNT against wild type (WT) and omicron BA.5 strains of SARS-CoV-2 in PLWH with CD4 T-cell count ≥ 500 cells/mm3(high CD4 recovery, HCDR) were comparable to HC, but they were significantly decreased in PLWH with CD4 T-cell count < 500/mm3 (low CD4 recovery, LCDR). After adjusting for age, gender, viral suppression, and number of preexisting comorbidities, CD4 T-cell counts < 500/mm3 significantly predicted a poor magnitude of neutralizing antibodies against WT, omicron BA.5, and XBB 1.5 strains among PLWH. Multivariable linear regression adjusting for age and gender revealed that LCDR was associated with reduced neutralizing activity (P = 0.017) and interferon-γ-producing T-cell responses (P = 0.049 for CD T-cell; P = 0.014 for CD8 T-cell) against WT, and strongly associated with more decreased cross-neutralization against omicron BA.5 strains (P < 0.001). Conclusion HCDR demonstrated robust humoral and cell-mediated immune responses after a booster dose of ancestral SARS-CoV-2 vaccine, whereas LCDR showed diminished immune responses against WT virus and more impaired cross-neutralization against omicron BA.5 strain.

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: We evaluated severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2)-specific humoral and cellular responses for up to 6 months after the 3rd dose of ancestral coronavirus disease 2019 (COVID-19) vaccination in people living with HIV (PLWH) and healthy controls (HCs) who were not infected with COVID-19. Methods: Anti-spike receptor-binding domain IgG (anti-RBD IgG) concentrations using chemiluminescence immunoassay and neutralizing antibodies using focus reduction neutralization test (FRNT) were assessed at 1 week after each dose of vaccination, and 3 and 6 months after the 3rd dose in 62 PLWH and 25 HCs. T-cell responses using intracellular cytokine stain were evaluated at 1 week before, and 1 week and 6 months after the 3rd dose. Results: At 1 week after the 3rd dose, adequate anti-RBD IgG (> 300 binding antibody unit /mL) was elicited in all PLWH except for one patient with 36 CD4 T-cell count/mm3 . The geometric mean titers of 50% FRNT against wild type (WT) and omicron BA.5 strains of SARS-CoV-2 in PLWH with CD4 T-cell count ≥ 500 cells/mm3(high CD4 recovery, HCDR) were comparable to HC, but they were significantly decreased in PLWH with CD4 T-cell count < 500/mm3 (low CD4 recovery, LCDR). After adjusting for age, gender, viral suppression, and number of preexisting comorbidities, CD4 T-cell counts < 500/mm3 significantly predicted a poor magnitude of neutralizing antibodies against WT, omicron BA.5, and XBB 1.5 strains among PLWH. Multivariable linear regression adjusting for age and gender revealed that LCDR was associated with reduced neutralizing activity (P = 0.017) and interferon-γ-producing T-cell responses (P = 0.049 for CD T-cell; P = 0.014 for CD8 T-cell) against WT, and strongly associated with more decreased cross-neutralization against omicron BA.5 strains (P < 0.001). Conclusion HCDR demonstrated robust humoral and cell-mediated immune responses after a booster dose of ancestral SARS-CoV-2 vaccine, whereas LCDR showed diminished immune responses against WT virus and more impaired cross-neutralization against omicron BA.5 strain.

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.