Background/Aims: Spinal cord injury (SCI) frequently impairs defecation, severely affecting the quality of life. This study examines compensatory neural remodeling after SCI, focusing on basal colonic contractility, neural responses to electrical field stimulation, and alterations in excitatory cholinergic and inhibitory nitrergic pathways. Methods: Female Sprague–Dawley rats underwent either sham surgery or T10 spinal cord transection and were categorized into 3 groups: sham, 1-week post-SCI (acute), and 4-week post-SCI (chronic). Colonic contractility was assessed in an organ bath using electrical field stimulation in the presence of a nitric oxide synthase inhibitor. Neural protein expression was analyzed by immunofluorescence and Western blotting. Results: SCI produced region- and time-dependent impairments in colonic contractility, with distinct alterations in the proximal circular and longitudinal muscles across acute and chronic phases. Neural excitability shifted dynamically, showing enhanced excitatory activity in the proximal longitudinal muscle at 1-week and the distal circular muscle at 4-week post-SCI. Protein analysis revealed increased neuronal nitric oxide synthase in the proximal colon, decreasedsoluble guanylyl cyclase in the distal colon, upregulated muscarinic M3 receptor in the proximal colon, and reduced vaso-active intestinal peptide receptor 1 in both proximal and distal regions. Conclusion SCI induces spatiotemporal remodeling of excitatory and inhibitory neural pathways, contributing to colonic dysmotility and revealing potential targets for therapeutic intervention.

Objective: Sleep disturbances are common and debilitating nonmotor symptoms (NMS) in Parkinson’s disease (PD) patients and profoundly affect quality of life. Despite emerging evidence suggesting that monoamine oxidase B (MAO-B) and catechol-O-methyltransferase (COMT) inhibitors may alleviate NMS, their specific effects on sleep remain unclear. The aim of this network meta-analysis (NMA) was to compare the efficacy of these inhibitors related to sleep problems in PD patients. Methods: Following a systematic search of PubMed, Cochrane, and Embase, studies comparing MAO-B or COMT inhibitors and assessing sleep outcomes in PD patients were identified. An NMA was conducted using data from the seven studies that met our inclusion criteria. The outcomes included subjective sleep quality, daytime sleepiness, and objective polysomnography (PSG) parameters. Results: No statistically significant differences were found between the effects of the MAO-B and COMT inhibitors on improving subjective sleep quality or daytime sleepiness. However, analyses of objective PSG data revealed that, compared with rasagiline and placebo, safinamide significantly increased rapid eye movement sleep duration (mean difference, 5.70 min [95% CI, 2.26 to 9.14]) and decreased wake time after sleep onset (mean difference, -10.20 min [-19.38 to -1.02]). Conclusion These findings suggest that safinamide may offer additional value for managing sleep disruptions beyond its known motor benefits in patients with PD. Given the limited number and small scale of available trials, the overall evidence should be interpreted cautiously. Nonetheless, this analysis highlights the need for future high-quality trials focused on sleep outcomes to guide the personalized use of MAO-B and COMT inhibitors for sleep disturbances in PD 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: 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.