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.

Cognitive dysfunction, a significant complication of type 2 diabetes mellitus (T2DM), can potentially manifest even from the early stages of the disease. Despite evidence of global brain atrophy and related cognitive dysfunction in early-stage T2DM patients, specific regions vulnerable to these changes have not yet been identified. The study enrolled patients with T2DM of less than five years’ duration and without chronic complications (T2DM group, n=100) and demographically similar healthy controls (control group, n=50). High-resolution T1-weighted magnetic resonance imaging data were subjected to independent component analysis to identify structurally significant components indicative of morphometric networks. Within these networks, the groups’ gray matter volumes were compared, and distinctions in memory performance were assessed. In the T2DM group, the relationship between changes in gray matter volume within these networks and declines in memory performance was examined. Among the identified morphometric networks, the T2DM group exhibited reduced gray matter volumes in both the precuneus (Bonferronicorrected p=0.003) and insular-opercular (Bonferroni-corrected p=0.024) networks relative to the control group. Patients with T2DM demonstrated significantly lower memory performance than the control group (p=0.001). In the T2DM group, reductions in gray matter volume in both the precuneus (r=0.316, p=0.001) and insular-opercular (r=0.199, p=0.047) networks were correlated with diminished memory performance. Our findings indicate that structural alterations in the precuneus and insular-opercular networks, along with memory dysfunction, can manifest within the first 5 years following a diagnosis of T2DM.

Liver fibrosis is part of the wound healing process to help the liver recover from the injuries caused by various liver-damaging insults. However, liver fibrosis often progresses to life-threatening cirrhosis and hepatocellular carcinoma. To overcome the limitations of current in vivo liver fibrosis models for studying the pathophysiology of liver fibrosis and establishing effective treatment strategies, we developed a new mouse model of liver fibrosis using polyhexamethylene guanidine phosphate (PHMG-p), a humidifier sterilizer known to induce lung fibrosis in humans. Male C57/BL6 mice were intraperitoneally injected with PHMG-p (0.03% and 0.1%) twice a week for 5 weeks. Subsequently, liver tissues were examined histologically and RNA-sequencing was performed to evaluate the expression of key genes and pathways affected by PHMG-p. PHMG-p injection resulted in body weight loss of ~15% and worsening of physical condition. Necropsy revealed diffuse fibrotic lesions in the liver with no effect on the lungs. Histology, collagen staining, immunohistochemistry for smooth muscle actin and collagen, and polymerase chain reaction analysis of fibrotic genes revealed that PHMG-p induced liver fibrosis in the peri-central, peri-portal, and capsule regions. RNA-sequencing revealed that PHMG-p affected several pathways associated with human liver fibrosis, especially with upregulation of lumican and IRAK3, and downregulation of GSTp1 and GSTp2, which are closely involved in liver fibrosis pathogenesis. Collectively we demonstrated that the PHMG-p-induced liver fibrosis model can be employed to study human liver fibrosis.

Purpose: Osteoporosis is characterized by structural deterioration of the bone tissue because of the loss of osteoblastic activity or the increase in osteoclastic activity, resulting in bone fragility and an increased risk of fractures. Hederagenin (Hed) is a pentacyclic triterpenoid saponin isolated from Dipsaci Radix, the dried root of Dipsacus asper Wall. Dipsaci Radix has been used in Korean herbal medicine to treat bone fractures. In this study, we attempted to demonstrate the potential anti-osteoporotic effect of Hed by examining its effect on osteoblast differentiation in MC3T3-E1 cells. Methods: Osteoblastic MC3T3-E1 cells were cultured in 0, 1, and 10 μg/mL Hed for 3 and 7 days. The activity of alkaline phosphatase (ALP), bone nodule formation and level of expression of bone-related genes and proteins were measured in MC3T3-E1 cells exposed to Hed. The western blot test was used to detect the activation of the bone morphogenetic protein-2 (BMP2)/ Suppressor of Mothers against Decapentaplegic (SMAD)1 pathway. Results: Hed significantly increased the proliferation of MC3T3-E1 cells. Intracellular ALP activity was significantly increased in the 1 μg/mL Hed-treated group. Hed significantly increased the concentration of calcified nodules. Furthermore, Hed significantly upregulated the expression of genes and proteins associated with osteoblast proliferation and differentiation, such as Runt-related transcription factor 2 (Runx2), ALP, osteopontin (OPN), and type I procollagen (ProCOL1). Induction of osteoblast differentiation by Hed was associated with increased BMP2. In addition, Hed induced osteoblast differentiation by increasing the activity of SMAD1/5/8. These results suggest that Hed has the potential to prevent osteoporosis by promoting osteoblastogenesis in osteoblastic MC3T3-E1 cells via the modulation of the BMP2/SMAD1 pathway. Conclusion The results presented in this study indicate that Hed isolated from Dipsaci Radix has the potential to be developed as a healthcare food and functional material possessing anti-osteoporosis effects.

This article aims to introduce the inception and operation of the COVID-19 International Collaborative Research Project, the world’s first coronavirus disease 2019 (COVID-19) open data project for research, along with its dataset and research method, and to discuss relevant considerations for collaborative research using nationwide real-world data (RWD). COVID-19 has spread across the world since early 2020, becoming a serious global health threat to life, safety, and social and economic activities. However, insufficient RWD from patients was available to help clinicians efficiently diagnose and treat patients with COVID-19, or to provide necessary information to the government for policy-making. Countries that saw a rapid surge of infections had to focus on leveraging medical professionals to treat patients, and the circumstances made it even more difficult to promptly use COVID-19 RWD. Against this backdrop, the Health Insurance Review and Assessment Service (HIRA) of Korea decided to open its COVID-19 RWD collected through Korea’s universal health insurance program, under the title of the COVID-19 International Collaborative Research Project. The dataset, consisting of 476 508 claim statements from 234 427 patients (7590 confirmed cases) and 18 691 318 claim statements of the same patients for the previous 3 years, was established and hosted on HIRA’s in-house server. Researchers who applied to participate in the project uploaded analysis code on the platform prepared by HIRA, and HIRA conducted the analysis and provided outcome values. As of November 2020, analyses have been completed for 129 research projects, which have been published or are in the process of being published in prestigious journals.

Background: Diseases related to cerebrospinal fluid flow, such as hydrocephalus, syringomyelia, and Chiari malformation, are often found in small dogs. Although studies in human medicine have revealed a correlation with cerebrospinal fluid flow in these diseases by magnetic resonance imaging, there is little information and no standard data for normal dogs. Objectives: The purpose of this study was to obtain cerebrospinal fluid flow velocity data from the cerebral aqueduct and subarachnoid space at the foramen magnum in healthy beagle dogs. Methods: Six healthy beagle dogs were used in this experimental study. The dogs underwent phase-contrast and time-spatial labeling inversion pulse magnetic resonance imaging. Flow rate variations in the cerebrospinal fluid were observed using sagittal time-spatial labeling inversion pulse images. The pattern and velocity of cerebrospinal fluid flow were assessed using phase-contrast magnetic resonance imaging within the subarachnoid space at the foramen magnum level and the cerebral aqueduct. Results: In the ventral aspect of the subarachnoid space and cerebral aqueduct, the cerebrospinal fluid was characterized by a bidirectional flow throughout the cardiac cycle. The mean ± SD peak velocities through the ventral and dorsal aspects of the subarachnoid space and the cerebral aqueduct were 1.39 ± 0.13, 0.32 ± 0.12, and 0.76 ± 0.43 cm/s, respectively. Conclusions Noninvasive visualization of cerebrospinal fluid flow movement with magnetic resonance imaging was feasible, and a reference dataset of cerebrospinal fluid flow peak velocities was obtained through the cervical subarachnoid space and cerebral aqueduct in healthy dogs.

Background: Immunohistochemistry (IHC) has played an essential role in the diagnosis of hematolymphoid neoplasms. However, IHC interpretations can be challenging in daily practice, and exponentially expanding volumes of IHC data are making the task increasingly difficult. We therefore developed a machine-learning expert-supporting system for diagnosing lymphoid neoplasms. Methods: A probabilistic decision-tree algorithm based on the Bayesian theorem was used to develop mobile application software for iOS and Android platforms. We tested the software with real data from 602 training and 392 validation cases of lymphoid neoplasms and compared the precision hit rates between the training and validation datasets. Results: IHC expression data for 150 lymphoid neoplasms and 584 antibodies was gathered. The precision hit rates of 94.7% in the training data and 95.7% in the validation data for lymphomas were not statistically significant. Results in most B-cell lymphomas were excellent, and generally equivalent performance was seen in T-cell lymphomas. The primary reasons for lack of precision were atypical IHC profiles for certain cases (e.g., CD15-negative Hodgkin lymphoma), a lack of disease-specific markers, and overlapping IHC profiles of similar diseases. Conclusions Application of the machine-learning algorithm to diagnosis precision produced acceptable hit rates in training and validation datasets. Because of the lack of origin- or disease- specific markers in differential diagnosis, contextual information such as clinical and histological features should be taken into account to make proper use of this system in the pathologic decision-making process.