Purpose: This study aimed to investigate the effects of postnatal systemic corticosteroids on neurodevelopment in very low birth weight (VLBW) preterm infants. Methods: This was a population-based study of the Korean Neonatal Network of VLBW infant born at 23+0 and 31+6 weeks of gestation between 2013 and 2020. VLBW preterm infants assessed using the Bayley Scales of Infant and Toddler Development, third edition (BSID-III) at 18–24 months of corrected age and 3 years of age were enrolled. The primary outcomes were BSID-III scores and neurodevelopmental delays, with scores of <85. Socioeconomic status and clinical variables were adjusted for using multivariate regression analyses. Results: In total, 517 infants were enrolled in this study. Among the 216 (41.8%) infants who received postnatal systemic corticosteroids, the rate of cognitive delay was significantly higher at 18–24 months of corrected age than at 3 years of age. The rates of language and motor delays were significantly higher both at 18–24 months of corrected age and at 3 years of age. When multivariate logistic regression was performed, postnatal systemic corticosteroid use was significantly associated with cognitive delay at 18–24 months of corrected age, but not at 3 years of age. There was no significant association between postnatal systemic corticosteroid use and language or motor delay at 18-24 months of corrected age or at 3 years of age after multivariate logistic regression. Conclusion Postnatal systemic corticosteroid use in VLBW preterm infants increased the risk of cognitive delay at 18–24 months of corrected age, but not at 3 years.

Background: Skipping breakfast is associated with an increased risk of chronic inflammatory diseases. This study aimed to examine the association between breakfast-eating habits and inflammation, using high-sensitivity C-reactive protein (hs-CRP) as a marker. Methods: A total of 4,000 Korean adult males with no history of myocardial infarction, angina, stroke, diabetes, rheumatoid arthritis, cancer, or current smoking were included. Data from the 2016–2018 Korea National Health and Nutrition Examination Survey were used for analysis. The frequency of breakfast consumption was assessed through a questionnaire item in the dietary survey section asking participants about their weekly breakfast consumption routines over the past year. Participants were categorized into two groups, namely “0–2 breakfasts per week” and “3–7 breakfasts per week”; hs-CRP concentrations were measured through blood tests. Results: Comparing between the “infrequent breakfast consumption (0–2 breakfasts per week)” and “frequent breakfast consumption (3–7 breakfasts per week)” groups, the mean hs-CRP was found to be significantly higher in the “infrequent breakfast consumption” group, even after adjusting for age, body mass index, physical activity, alcohol consumption, systolic blood pressure, blood pressure medication, fasting blood glucose, and triglycerides (mean hs-CRP: frequent breakfast consumption, 1.36±0.09 mg/L; infrequent breakfast consumption, 1.17±0.05 mg/L; P-value=0.036). Conclusion Less frequent breakfast consumption was associated with elevated hs-CRP levels. Further large-scale studies incorporating adjusted measures of daily eating patterns as well as food quality and quantity are required for a deeper understanding of the role of breakfast in the primary prevention of chronic inflammatory diseases.

Fine particulate matter (FPM) is a major component of air pollution and has emerged as a significant global health concern owing to its adverse health effects. Previous studies have investigated the correlation between bone health and FPM through cohort or review studies. However, the effects of FPM exposure on bone health are poorly understood. This study aimed to investigate the effects of FPM on bone health and elucidate these effects in vitro and in vivo using mice. Micro-CT analysis in vivo revealed FPM exposure decreased bone mineral density, trabecular bone volume/total volume ratio, and trabecular number in the femurs of mice, while increasing trabecular separation. Histological analysis showed that the FPM-treated group had a reduced trabecular area and an increased number of osteoclasts in the bone tissue. Moreover, in vitro studies revealed that low concentrations of FPM significantly enhanced osteoclast differentiation. These findings further support the notion that short-term FPM exposure negatively impacts bone health, providing a foundation for further research on this topic.

Purpose: Although some medications trigger the worsening of myasthenia gravis (MG), their clinical influence on patients with MG has not been significantly evaluated. We aimed to investigate whether the risk of clinical worsening of MG increases after administering cautionary drugs in patients with MG. Materials and Methods: This retrospective case-control study was based on the medical records of patients diagnosed with MG between 2007 and 2020. We analyzed the risk of MG worsening in patients exposed to cautionary drugs during the risk period, defined as 6 months from the first exposure to cautionary drugs. The risk of MG worsening in the exposed patients was compared to that in the non-exposed patients, who were individually matched in a 1:1 ratio with exposed cases for sex, age, thymoma, and autoantibodies. Results: Of the 2002 patients diagnosed with MG, 552 (27.6%) were exposed to cautionary drugs. Neuromuscular blocking agents (320 patients) and beta blockers (66123 person-days) were the most frequently prescribed medications. After exact matching, 220 exposed and 220 non-exposed patients were enrolled. The incidence rate of clinical worsening during the risk period was significantly higher in the exposed patients than in the non-exposed patients (odds ratio=4.09; 95% confidence interval, 1.88–8.90;p<0.001). Clinical worsening was observed in 31 (14.1%) of the exposed patients and in 8 (3.6%) of the non-exposed patients. Conclusion The administration of cautionary drugs increased the risk of clinical worsening in patients with MG. Clinicians should be aware of this risk when cautionary drugs need to be administered.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Fine particulate matter (FPM) is a major component of air pollution and has emerged as a significant global health concern owing to its adverse health effects. Previous studies have investigated the correlation between bone health and FPM through cohort or review studies. However, the effects of FPM exposure on bone health are poorly understood. This study aimed to investigate the effects of FPM on bone health and elucidate these effects in vitro and in vivo using mice. Micro-CT analysis in vivo revealed FPM exposure decreased bone mineral density, trabecular bone volume/total volume ratio, and trabecular number in the femurs of mice, while increasing trabecular separation. Histological analysis showed that the FPM-treated group had a reduced trabecular area and an increased number of osteoclasts in the bone tissue. Moreover, in vitro studies revealed that low concentrations of FPM significantly enhanced osteoclast differentiation. These findings further support the notion that short-term FPM exposure negatively impacts bone health, providing a foundation for further research on this topic.

Purpose: Although some medications trigger the worsening of myasthenia gravis (MG), their clinical influence on patients with MG has not been significantly evaluated. We aimed to investigate whether the risk of clinical worsening of MG increases after administering cautionary drugs in patients with MG. Materials and Methods: This retrospective case-control study was based on the medical records of patients diagnosed with MG between 2007 and 2020. We analyzed the risk of MG worsening in patients exposed to cautionary drugs during the risk period, defined as 6 months from the first exposure to cautionary drugs. The risk of MG worsening in the exposed patients was compared to that in the non-exposed patients, who were individually matched in a 1:1 ratio with exposed cases for sex, age, thymoma, and autoantibodies. Results: Of the 2002 patients diagnosed with MG, 552 (27.6%) were exposed to cautionary drugs. Neuromuscular blocking agents (320 patients) and beta blockers (66123 person-days) were the most frequently prescribed medications. After exact matching, 220 exposed and 220 non-exposed patients were enrolled. The incidence rate of clinical worsening during the risk period was significantly higher in the exposed patients than in the non-exposed patients (odds ratio=4.09; 95% confidence interval, 1.88–8.90;p<0.001). Clinical worsening was observed in 31 (14.1%) of the exposed patients and in 8 (3.6%) of the non-exposed patients. Conclusion The administration of cautionary drugs increased the risk of clinical worsening in patients with MG. Clinicians should be aware of this risk when cautionary drugs need to be administered.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using highresolution biosignals collected within 4 h of arrival. Materials and Methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices. Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables. Conclusion Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Background: Skipping breakfast is associated with an increased risk of chronic inflammatory diseases. This study aimed to examine the association between breakfast-eating habits and inflammation, using high-sensitivity C-reactive protein (hs-CRP) as a marker. Methods: A total of 4,000 Korean adult males with no history of myocardial infarction, angina, stroke, diabetes, rheumatoid arthritis, cancer, or current smoking were included. Data from the 2016–2018 Korea National Health and Nutrition Examination Survey were used for analysis. The frequency of breakfast consumption was assessed through a questionnaire item in the dietary survey section asking participants about their weekly breakfast consumption routines over the past year. Participants were categorized into two groups, namely “0–2 breakfasts per week” and “3–7 breakfasts per week”; hs-CRP concentrations were measured through blood tests. Results: Comparing between the “infrequent breakfast consumption (0–2 breakfasts per week)” and “frequent breakfast consumption (3–7 breakfasts per week)” groups, the mean hs-CRP was found to be significantly higher in the “infrequent breakfast consumption” group, even after adjusting for age, body mass index, physical activity, alcohol consumption, systolic blood pressure, blood pressure medication, fasting blood glucose, and triglycerides (mean hs-CRP: frequent breakfast consumption, 1.36±0.09 mg/L; infrequent breakfast consumption, 1.17±0.05 mg/L; P-value=0.036). Conclusion Less frequent breakfast consumption was associated with elevated hs-CRP levels. Further large-scale studies incorporating adjusted measures of daily eating patterns as well as food quality and quantity are required for a deeper understanding of the role of breakfast in the primary prevention of chronic inflammatory diseases.