Objectives: This study aimed to investigate the impact of coronavirus disease 2019 (COVID-19) on admission rates and in-hospital mortality among patients with ischemic and hemorrhagic stroke. Methods: We constructed a dataset detailing the monthly hospitalizations and mortality rates of inpatients with stroke from January 2017 to December 2021. Employing an interrupted time series analysis, we explored the impact of COVID-19 on hospitalizations and 30-day in-hospital mortality among stroke patients. Results: The number of ischemic stroke admissions decreased by 18.5%, from 5335 to 4348, immediately following the COVID-19 outbreak (p<0.001). The in-hospital mortality rate for ischemic stroke increased slightly from 3.3% to 3.4% immediately after the outbreak, although it showed a decreasing trend over time. The number of hemorrhagic stroke admissions fell by 7.5%, from 2014 to 1864, immediately following the COVID-19 outbreak. The 30-day in-hospital mortality rate for hemorrhagic stroke initially decreased from 12.9% to 12.7%, but subsequently showed an increasing trend. Conclusions We confirmed that COVID-19 impacted both the admission and death rates of stroke patients. The admission rate for both ischemic and hemorrhagic strokes decreased, while in-hospital mortality increased. Specifically, in-hospital mortality from ischemic stroke rose initially after the outbreak before stabilizing. Additionally, our findings indicate variable effects based on sex, age, and socioeconomic status, suggesting that certain groups may be more susceptible. This underscores the need to identify and support vulnerable populations to mitigate adverse health outcomes.

Recent studies have shown an increased abundance of Sphingomonas paucimobilis, an aerobic, Gram-negative bacterium with a distinctive cell envelope rich in glycosphingolipids, within the gut microbiome of individuals with Alzheimer Disease (AD). However, the fact that S. paucimobilis is a well-known pathogen associated with nosocomial infections presents a significant challenge in investigating whether its presence in the gut microbiome is detrimental or beneficial, particularly in the context of AD. This study examines the impact of S. paucimobilis-derived extracellular vesicles (Spa-EV) on Aβ-induced pathology in cellular and animal models of AD. Microarray analysis reveals that Spa-EV treatment modulates Aβ42-induced alterations in gene expression in both HT22 neuronal cells and BV2 microglia cells. Among the genes significantly affected by SpaEV, notable examples include Bdnf, Nt3/4, and Trkb, which are key players of neurotrophic signaling; Pgc1α, an upstream regulator of mitochondrial biogenesis; Mecp2 and Sirt1, epigenetic factors that regulate numerous gene expressions; and Il1β, Tnfα, and Nfκb-p65, which are associated with neuroinflammation. Remarkably, Spa-EV effectively reverses Aβ42-induced alteration in the expression of these genes through the upregulation of Mecp2. Furthermore, administration of Spa-EV in Tg-APP/PS1 mice restores the reduced expression of neurotrophic factors, Pgc1α, MeCP2, and Sirt1, while suppressing the increased expression of proinflammatory genes in the brain. Our results indicate that Spa-EV has the potential to reverse Aβ-induced dysregulation of gene expression in neuronal and microglial cells. These alterations encompass those essential for neurotrophic signaling and neuronal plasticity, mitochondrial function, and the regulation of inflammatory processes.

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.

Recent studies have shown an increased abundance of Sphingomonas paucimobilis, an aerobic, Gram-negative bacterium with a distinctive cell envelope rich in glycosphingolipids, within the gut microbiome of individuals with Alzheimer Disease (AD). However, the fact that S. paucimobilis is a well-known pathogen associated with nosocomial infections presents a significant challenge in investigating whether its presence in the gut microbiome is detrimental or beneficial, particularly in the context of AD. This study examines the impact of S. paucimobilis-derived extracellular vesicles (Spa-EV) on Aβ-induced pathology in cellular and animal models of AD. Microarray analysis reveals that Spa-EV treatment modulates Aβ42-induced alterations in gene expression in both HT22 neuronal cells and BV2 microglia cells. Among the genes significantly affected by SpaEV, notable examples include Bdnf, Nt3/4, and Trkb, which are key players of neurotrophic signaling; Pgc1α, an upstream regulator of mitochondrial biogenesis; Mecp2 and Sirt1, epigenetic factors that regulate numerous gene expressions; and Il1β, Tnfα, and Nfκb-p65, which are associated with neuroinflammation. Remarkably, Spa-EV effectively reverses Aβ42-induced alteration in the expression of these genes through the upregulation of Mecp2. Furthermore, administration of Spa-EV in Tg-APP/PS1 mice restores the reduced expression of neurotrophic factors, Pgc1α, MeCP2, and Sirt1, while suppressing the increased expression of proinflammatory genes in the brain. Our results indicate that Spa-EV has the potential to reverse Aβ-induced dysregulation of gene expression in neuronal and microglial cells. These alterations encompass those essential for neurotrophic signaling and neuronal plasticity, mitochondrial function, and the regulation of inflammatory processes.

Recent studies have shown an increased abundance of Sphingomonas paucimobilis, an aerobic, Gram-negative bacterium with a distinctive cell envelope rich in glycosphingolipids, within the gut microbiome of individuals with Alzheimer Disease (AD). However, the fact that S. paucimobilis is a well-known pathogen associated with nosocomial infections presents a significant challenge in investigating whether its presence in the gut microbiome is detrimental or beneficial, particularly in the context of AD. This study examines the impact of S. paucimobilis-derived extracellular vesicles (Spa-EV) on Aβ-induced pathology in cellular and animal models of AD. Microarray analysis reveals that Spa-EV treatment modulates Aβ42-induced alterations in gene expression in both HT22 neuronal cells and BV2 microglia cells. Among the genes significantly affected by SpaEV, notable examples include Bdnf, Nt3/4, and Trkb, which are key players of neurotrophic signaling; Pgc1α, an upstream regulator of mitochondrial biogenesis; Mecp2 and Sirt1, epigenetic factors that regulate numerous gene expressions; and Il1β, Tnfα, and Nfκb-p65, which are associated with neuroinflammation. Remarkably, Spa-EV effectively reverses Aβ42-induced alteration in the expression of these genes through the upregulation of Mecp2. Furthermore, administration of Spa-EV in Tg-APP/PS1 mice restores the reduced expression of neurotrophic factors, Pgc1α, MeCP2, and Sirt1, while suppressing the increased expression of proinflammatory genes in the brain. Our results indicate that Spa-EV has the potential to reverse Aβ-induced dysregulation of gene expression in neuronal and microglial cells. These alterations encompass those essential for neurotrophic signaling and neuronal plasticity, mitochondrial function, and the regulation of inflammatory processes.

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.

Objectives: This study aimed to investigate the impact of coronavirus disease 2019 (COVID-19) on admission rates and in-hospital mortality among patients with ischemic and hemorrhagic stroke. Methods: We constructed a dataset detailing the monthly hospitalizations and mortality rates of inpatients with stroke from January 2017 to December 2021. Employing an interrupted time series analysis, we explored the impact of COVID-19 on hospitalizations and 30-day in-hospital mortality among stroke patients. Results: The number of ischemic stroke admissions decreased by 18.5%, from 5335 to 4348, immediately following the COVID-19 outbreak (p<0.001). The in-hospital mortality rate for ischemic stroke increased slightly from 3.3% to 3.4% immediately after the outbreak, although it showed a decreasing trend over time. The number of hemorrhagic stroke admissions fell by 7.5%, from 2014 to 1864, immediately following the COVID-19 outbreak. The 30-day in-hospital mortality rate for hemorrhagic stroke initially decreased from 12.9% to 12.7%, but subsequently showed an increasing trend. Conclusions We confirmed that COVID-19 impacted both the admission and death rates of stroke patients. The admission rate for both ischemic and hemorrhagic strokes decreased, while in-hospital mortality increased. Specifically, in-hospital mortality from ischemic stroke rose initially after the outbreak before stabilizing. Additionally, our findings indicate variable effects based on sex, age, and socioeconomic status, suggesting that certain groups may be more susceptible. This underscores the need to identify and support vulnerable populations to mitigate adverse health outcomes.

Recent studies have shown an increased abundance of Sphingomonas paucimobilis, an aerobic, Gram-negative bacterium with a distinctive cell envelope rich in glycosphingolipids, within the gut microbiome of individuals with Alzheimer Disease (AD). However, the fact that S. paucimobilis is a well-known pathogen associated with nosocomial infections presents a significant challenge in investigating whether its presence in the gut microbiome is detrimental or beneficial, particularly in the context of AD. This study examines the impact of S. paucimobilis-derived extracellular vesicles (Spa-EV) on Aβ-induced pathology in cellular and animal models of AD. Microarray analysis reveals that Spa-EV treatment modulates Aβ42-induced alterations in gene expression in both HT22 neuronal cells and BV2 microglia cells. Among the genes significantly affected by SpaEV, notable examples include Bdnf, Nt3/4, and Trkb, which are key players of neurotrophic signaling; Pgc1α, an upstream regulator of mitochondrial biogenesis; Mecp2 and Sirt1, epigenetic factors that regulate numerous gene expressions; and Il1β, Tnfα, and Nfκb-p65, which are associated with neuroinflammation. Remarkably, Spa-EV effectively reverses Aβ42-induced alteration in the expression of these genes through the upregulation of Mecp2. Furthermore, administration of Spa-EV in Tg-APP/PS1 mice restores the reduced expression of neurotrophic factors, Pgc1α, MeCP2, and Sirt1, while suppressing the increased expression of proinflammatory genes in the brain. Our results indicate that Spa-EV has the potential to reverse Aβ-induced dysregulation of gene expression in neuronal and microglial cells. These alterations encompass those essential for neurotrophic signaling and neuronal plasticity, mitochondrial function, and the regulation of inflammatory processes.

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.