Background: Rare cases of Kawasaki disease (KD) and multisystem inflammatory syndrome in children (MIS-C) have been reported following the coronavirus disease 2019 (COVID-19) vaccination; however, the association between COVID-19 vaccination and the risk of developing KD/MIS-C has not yet been established. Methods: We conducted a self-controlled case series analysis using a large-linked database that connects the COVID-19 immunization registry with nationwide claims data. We identified individuals aged < 18 years who received their initial COVID-19 vaccination and had a KD/MIS-C diagnosis with a prescription for intravenous immunoglobulin or corticosteroids between October 18, 2021, and April 15, 2023. The observation period was set as 240 days from the date of the COVID-19 vaccination. The risk window was 60 days after vaccination, with the remaining observation period serving as the control window. The incidence rate ratios (IRRs) and 95% confidence intervals (CIs) in the risk versus control windows were estimated using the conditional Poisson regression model. We further analyzed the vaccine doses and types for secondary analysis. We also performed subgroup analyses stratified by sex, age, comorbidities, and other conditions and sensitivity analyses by varying the length of the risk window and outcome definition. Results: Among 2,369,490 individuals who received the COVID-19 vaccination, 12 cases of KD/MIS-C were identified, which included five and seven patients in the risk and control windows, respectively. There was no increased risk of KD/MIS-C within the 60-day period of vaccination (IRR, 0.53; 95% CI, 0.17–1.60). Secondary subgroup and sensitivity analyses showed no significant increase in the risk of KD/MIS-C after COVID-19 vaccination, which is consistent with the results of the main analysis. Conclusion The results of this nationwide study suggest that the risk of developing KD/MIS-C did not increase after COVID-19 vaccination. However, owing to the lack of a sufficient number of cases, future studies utilizing multinational long-term follow-up databases should be conducted. Considering the increasing incidence of KD/MIS-C and the limited understanding of its precise biological mechanisms, additional research on KD/MIS-C is warranted.

Background: Rare cases of Kawasaki disease (KD) and multisystem inflammatory syndrome in children (MIS-C) have been reported following the coronavirus disease 2019 (COVID-19) vaccination; however, the association between COVID-19 vaccination and the risk of developing KD/MIS-C has not yet been established. Methods: We conducted a self-controlled case series analysis using a large-linked database that connects the COVID-19 immunization registry with nationwide claims data. We identified individuals aged < 18 years who received their initial COVID-19 vaccination and had a KD/MIS-C diagnosis with a prescription for intravenous immunoglobulin or corticosteroids between October 18, 2021, and April 15, 2023. The observation period was set as 240 days from the date of the COVID-19 vaccination. The risk window was 60 days after vaccination, with the remaining observation period serving as the control window. The incidence rate ratios (IRRs) and 95% confidence intervals (CIs) in the risk versus control windows were estimated using the conditional Poisson regression model. We further analyzed the vaccine doses and types for secondary analysis. We also performed subgroup analyses stratified by sex, age, comorbidities, and other conditions and sensitivity analyses by varying the length of the risk window and outcome definition. Results: Among 2,369,490 individuals who received the COVID-19 vaccination, 12 cases of KD/MIS-C were identified, which included five and seven patients in the risk and control windows, respectively. There was no increased risk of KD/MIS-C within the 60-day period of vaccination (IRR, 0.53; 95% CI, 0.17–1.60). Secondary subgroup and sensitivity analyses showed no significant increase in the risk of KD/MIS-C after COVID-19 vaccination, which is consistent with the results of the main analysis. Conclusion The results of this nationwide study suggest that the risk of developing KD/MIS-C did not increase after COVID-19 vaccination. However, owing to the lack of a sufficient number of cases, future studies utilizing multinational long-term follow-up databases should be conducted. Considering the increasing incidence of KD/MIS-C and the limited understanding of its precise biological mechanisms, additional research on KD/MIS-C is warranted.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

BACKGROUND: Osteoporosis, characterized by decreased bone mineral density due to an imbalance between osteoblast and osteoclast activity, poses significant challenges in bone healing, particularly in postmenopausal women. Current treatments, such as bisphosphonates, are effective but associated with adverse effects like medication-related osteonecrosis of the jaw, necessitating safer alternatives. METHODS: This study investigated the use of L-serine-incorporated gelatin sponges for bone regeneration in calvarial defects in an ovariectomized rat model of osteoporosis. Thirty rats were divided into three groups: a control group, a group treated with a gelatin sponge containing an amino acid mixture, and a group treated with a gelatin sponge containing L-serine. Bone regeneration was assessed using micro-computed tomography (micro-CT) and histological analyses. RESULTS: The L-serine group showed a significant increase in bone volume (BV) and bone area compared to the control and amino acid groups. The bone volume to total volume (BV/TV) ratio was also significantly higher in the L-serine group.Immunohistochemical analysis demonstrated that L-serine treatment suppressed the expression of cathepsin K, a marker of osteoclast activity, while increasing serine racemase activity. CONCLUSION These findings suggest that L-serine-incorporated gelatin sponges not only enhance bone formation but also inhibit osteoclast-mediated bone resorption, providing a promising and safer alternative to current therapies for osteoporosis-related bone defects. Further research is needed to explore its clinical applications in human patients.

Suicidal cases involving sodium nitrite have been reported worldwide. However, postmortem features, such as brownish or grayish livor mortis, remain difficult to interpret, especially as decomposition advances. Here, we present three fatal cases (2020-2023) presumably caused by sodium nitrite ingestion. In these cases, characteristic nitrite-induced changes were inconsistent or obscured by decomposition, but ingestion traces (cup or bottle near the decedents) were observed at each scene. Additionally, containers labeled “sodium nitrite” were found in two cases; however, since sodium nitrite is designated a suicide-hazardous material in South Korea, future scenes may rarely reveal such clear labeling. Although autopsy, including methemoglobin testing, can confirm the cause of death, any delay in the investigative process risks the loss of critical evidence about the ingestion process and other factors. This underscores the importance of focusing on early scene evidence, particularly ingestion traces, and conducting thorough chemical and forensic examinations. Our findings illustrate that timely detection of ingestion-related evidence and subsequent forensic analysis, in conjunction with autopsy results, can elucidate a decedent’s cause and manner of death and clarify any criminal implications.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

BACKGROUND: Osteoporosis, characterized by decreased bone mineral density due to an imbalance between osteoblast and osteoclast activity, poses significant challenges in bone healing, particularly in postmenopausal women. Current treatments, such as bisphosphonates, are effective but associated with adverse effects like medication-related osteonecrosis of the jaw, necessitating safer alternatives. METHODS: This study investigated the use of L-serine-incorporated gelatin sponges for bone regeneration in calvarial defects in an ovariectomized rat model of osteoporosis. Thirty rats were divided into three groups: a control group, a group treated with a gelatin sponge containing an amino acid mixture, and a group treated with a gelatin sponge containing L-serine. Bone regeneration was assessed using micro-computed tomography (micro-CT) and histological analyses. RESULTS: The L-serine group showed a significant increase in bone volume (BV) and bone area compared to the control and amino acid groups. The bone volume to total volume (BV/TV) ratio was also significantly higher in the L-serine group.Immunohistochemical analysis demonstrated that L-serine treatment suppressed the expression of cathepsin K, a marker of osteoclast activity, while increasing serine racemase activity. CONCLUSION These findings suggest that L-serine-incorporated gelatin sponges not only enhance bone formation but also inhibit osteoclast-mediated bone resorption, providing a promising and safer alternative to current therapies for osteoporosis-related bone defects. Further research is needed to explore its clinical applications in human patients.

Purpose: Accurate prediction of blood glucose variability is crucial for effective diabetes management, as both hypoglycemia and hyperglycemia are associated with increased morbidity and mortality. However, conventional predictive models rely primarily on patient-specific biometric data, often neglecting the influence of patient–provider interactions, which can significantly impact outcomes. This study introduces Cyclic Dual Latent Discovery (CDLD), a deep learning framework that explicitly models patient–provider interactions to improve prediction of blood glucose levels. By leveraging a real-world intensive care unit (ICU) dataset, the model captures latent attributes of both patients and providers, thus improving forecasting accuracy. Methods: ICU patient records were obtained from the MIMIC-IV v3.0 critical care database, including approximately 5,014 instances of patient–provider interaction. The CDLD model uses a cyclic training mechanism that alternately updates patient and provider latent representations to optimize predictive performance. During preprocessing, all numeric features were normalized, and extreme glucose values were capped at 500 mg/dL to mitigate the effect of outliers. Results: CDLD outperformed conventional models, achieving a root mean square error of 0.0852 on the validation set and 0.0899 on the test set, which indicates improved generalization. The model effectively captured latent patient–provider interaction patterns, yielding more accurate glucose variability predictions than baseline approaches. Conclusion Integrating patient–provider interaction modeling into predictive frameworks can increase blood glucose prediction accuracy. The CDLD model offers a novel approach to diabetes management, potentially paving the way for artificial intelligence-driven personalized treatment strategies.

BACKGROUND: Osteoporosis, characterized by decreased bone mineral density due to an imbalance between osteoblast and osteoclast activity, poses significant challenges in bone healing, particularly in postmenopausal women. Current treatments, such as bisphosphonates, are effective but associated with adverse effects like medication-related osteonecrosis of the jaw, necessitating safer alternatives. METHODS: This study investigated the use of L-serine-incorporated gelatin sponges for bone regeneration in calvarial defects in an ovariectomized rat model of osteoporosis. Thirty rats were divided into three groups: a control group, a group treated with a gelatin sponge containing an amino acid mixture, and a group treated with a gelatin sponge containing L-serine. Bone regeneration was assessed using micro-computed tomography (micro-CT) and histological analyses. RESULTS: The L-serine group showed a significant increase in bone volume (BV) and bone area compared to the control and amino acid groups. The bone volume to total volume (BV/TV) ratio was also significantly higher in the L-serine group.Immunohistochemical analysis demonstrated that L-serine treatment suppressed the expression of cathepsin K, a marker of osteoclast activity, while increasing serine racemase activity. CONCLUSION These findings suggest that L-serine-incorporated gelatin sponges not only enhance bone formation but also inhibit osteoclast-mediated bone resorption, providing a promising and safer alternative to current therapies for osteoporosis-related bone defects. Further research is needed to explore its clinical applications in human patients.