This study examined the effect of 12-week intensive dietary and exercise intervention program on body composition and stress-related hormones in obese women and to examine the residual effect after the intervention. The participants of this study were 30 obese women who had a body mass index of over 25 kg/m² and over 30% in body fat. They were classified into 2 groups depending on the history of weight cycling (WC); the WC group (≥±5% of the original body weight) and the non-weight cycling (NWC) group. Both groups were subject to a nutritional intervention program every 2 weeks with a mandatory exercise intervention for 12 weeks. Thereafter, the nutrition/exercise interventions were ceased for 12 weeks, after which the participants' levels of the hormones relating to energy metabolism and stress, meal intakes, dietary habits, level of knowledge on sodium intake, frequency of sodium intake, and quality of life (QOL) were checked. The changes of body weight were 71.3 ± 5.5 kg (week 0) vs. 65.0 ± 6.6 kg (week 12) vs. 65.6 ± 7.1 kg (week 24) in WC group and 71.6 ± 8.6 kg (week 0) vs. 68.8 ± 9.7 kg (week 12) vs. 70.3 ± 9.4 kg (week 24) in the NWC group. The levels of hormones, meal intakes, and QOL scores were better in the WC group, as adherence to the nutritional intervention was higher. We suggest that that adherence to dietary habits heavily influences weight loss and maintenance in individuals who frequently attempt to lose weight and consequently go through a vicious cycle of weight recycling.
Adipose Tissue ; Body Composition ; Body Mass Index ; Body Weight ; Energy Metabolism ; Female ; Food Habits ; Humans ; Meals ; Obesity ; Quality of Life ; Recycling ; Sodium ; Weight Loss*

BACKGROUND: Glucometers are widely used for self-monitoring and point-of-care testing in diabetes management. We evaluated the performance of the recently developed Wisecheck Glucose Monitoring System (Wisemeditech, Korea) compared to that of 2 other well-known glucometer systems. METHODS: The Wisecheck glucometer was evaluated for precision, linearity, and carryover rate. One-hundred fifty samples samples were tested, and the results obtained from the Wisecheck glucometer, ACCU-CHEK Performa (Roche Diagnostics, Germany) and SD GlucoLink (SD Diagnostics, Korea) were compared to those obtained using the laboratory reference method from the Toshiba 200FR (Toshiba, Japan), according to the Clinical and Laboratory Standards Institute (CLSI) guidelines. RESULTS: The coefficient of variation (CV) values for within-run imprecision at low, middle, and high levels were 2.06%, 1.02%, and 2.02%, respectively, and the CV values for total-run imprecision at low, middle, and high levels were 2.98%, 2.41%, and 1.88%, respectively. In the linearity test, the coefficient of determination (R2) was 0.9985 in glucose concentration ranging from 48.6 mg/dL to 428 mg/dL (P<0.0001). The results obtained using the Wisecheck glucometer were well correlated with those obtained using the Toshiba 200FR (R2=0.980, P<0.0001). The carryover rate was 0.12%. CONCLUSIONS: The Wisecheck glucometer showed good precision, linearity, and correlation with the reference method. It provided rapid and reliable measurements of blood glucose levels and seemed appropriate for use in diabetes management.
Blood Glucose ; Diabetes Mellitus ; Glucose* ; Methods

The fifth edition of the WHO classification (2022 WHO) and the International Consensus Classification (2022 ICC) of myeloid neoplasms have been recently published. We reviewed the changes in the diagnosis distribution in patients with MDS with excess blasts (MDS-EB) or AML using both classifications. Forty-seven patients previously diagnosed as having AML or MDS-EB with available mutation analysis data, including targeted next-generation and RNA-sequencing data, were included. We reclassified 15 (31.9%) and 27 (57.4%) patients based on the 2022 WHO and 2022 ICC, respectively. One patient was reclassified as having a translocation categorized as a rare recurring translocation in both classifications. Reclassification was mostly due to the addition of mutation-based diagnostic criteria (i.e., AML, myelodysplasia-related) or a new entity associated with TP53 mutation. In both classifications, MDS diagnosis required the confirmation of multi-hit TP53 alterations. Among 14 patients with TP53 mutations, 11 harbored multi-hit TP53 alterations, including four with TP53 mutations and loss of heterozygosity. Adverse prognosis was associated with multi-hit TP53 alterations (P=0.009) in patients with MDS-EB, emphasizing the importance of detecting the mutations at diagnosis. The implementation of these classifications may lead to the identification of different subtypes from previously heterogeneous diagnostic categories based on genetic characteristics.

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.