Background: Pathogenic Escherichia coli (PEC) is a leading cause of acute diarrhea worldwide. With increasing antibiotic use, antimicrobial resistance in PEC has become a significant public health concern. In this study, we investigated the distribution and antimicrobial susceptibility patterns of PEC isolated from patients with acute diarrhea in the Busan and Gyeongnam regions of South Korea. Methods: In this retrospective cross-sectional laboratory-based study, 272 PCR-positive residual stool or rectal swab specimens collected between October 2023 and June 2024 were screened; after exclusion of 80 specimens without recoverable or confirmable isolates, 192 pathogenic E. coli isolates were included for pathotype identification and antimicrobial susceptibility testing. Pathotypes were identified using multiplex real-time polymerase chain reaction, and antimicrobial susceptibility was tested against 18 agents using the VITEK II system. Results: Enteroaggregative E. coli (EAEC) was the most prevalent pathotype (44.8%), followed by enteropathogenic E. coli (34.9%). High resistance rates were observed for ampicillin (77.1%), cefazolin (55.2%), and amoxicillin/clavulanic acid (44.3%). EAEC demonstrated the highest multidrug resistance (MDR) rate (34.9%). Pediatric patients (< 18 years) exhibited significantly higher resistance rates and MDR frequencies compared with adults, particularly against β-lactam antibiotics. Conclusion EAEC was identified as the predominant and most resistant pathotype in this region. The elevated resistance levels in children and the prevalence of MDR EAEC underscore the need for continuous local surveillance and appropriate antibiotic stewardship.

The Korea National Health and Nutrition Examination Survey (KNHANES) is a national health survey mandated by Article 16 of the National Health Promotion Act to assess the health and nutritional status of the Korean population. Over the past 2 decades, the survey has continuously introduced timely survey components while strengthening its survey methodology and operational systems to enhance both the policy relevance and scientific utility of its findings. The 10th KNHANES (2025–2027) preserves the statistical representativeness of its sampling design while expanding the use of web-based self-administered questionnaires to improve participant convenience. In response to Korea’s transition into a super-aged society, the 10th KNHANES incorporates enhanced older-adult health surveys, including osteoporosis assessment and older adults’ life functioning, and newly introduces items addressing social support as part of the social determinants of health. In addition, a longitudinal follow-up survey system has been established to monitor long-term changes in population health. Collectively, these changes are expected to improve understanding of aging-related health issues, support evidence-based national health policy development, and expand the applicability of KNHANES data for academic research.

Purpose: Periprosthetic joint infections (PJIs) resulting from total hip arthroplasty (THA) pose significant challenges. Partial two-stage exchange arthroplasty (TSEA) retains either the femoral stem or acetabular cup by preserving bone stock and soft tissue and offers a potential alternative to total TSEA. Our meta-analysis evaluated the pooled success rate of partial TSEA in treating late chronic PJIs in THA. Our study also focused on the outcomes associated with retained prosthetic implant (stem or cup) and the type of prior surgery (primary vs. revision arthroplasty) performed. Materials and Methods: We analyzed data from 355 hips of patients with PJI following THA across 11 studies using a random-effects model to estimate the success rate of partial TSEA and spacer dislocation that occurs during the interval between the first and second stages of partial TSEA. The heterogeneity between the subgroups was also calculated. Results: The overall pooled success rate of partial TSEA in the study cohort was 87% (95% confidence interval [CI] 83-91).Lacking statistical significance (P=0.904), the success rates were 88% for stem-retained and 90% for cup-retained implant cases. The partial TSEA success rates were 88% for primary arthroplasty and 80% for revision arthroplasty, although this was not statistically significant (P=0.193). Spacer dislocation occurred in only 5% of the cases (95% CI 1-9). Conclusion Partial TSEA achieves high success rates comparable to those of total TSEA. Although revision arthroplasty cases carry a high risk of recurrence, partial TSEA remains a viable, tailored treatment option with a low complication rate.

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.

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.