Objectives: This study aimed to develop a NOVA-based scoring approach for evaluating ultra- processed food (UPF) intake among Korean adults and to examine its performance. Previous studies have reported that young adults have the highest levels of UPF consumption. Accordingly, this study focused on adults aged 19–40 years and developed scoring components reflecting dietary patterns specific to Korean eating habits. Methods: Using 24-hour dietary recall data from adults aged 19–40 years in the Korea National Health and Nutrition Examination Survey (2021–2023), foods were classified according to the Korean-adapted NOVA system. The top 10 food groups accounting for ≥ 80% of cumulative UPF-derived energy were selected to construct the scoring components. The tool was operationalized using food frequency questionnaire (FFQ) data from the Gangwon cohort study (2022–2024). Intake frequencies were converted into scores ranging from 0 (“rarely or never”) to 5 (“≥ 1 time/day”), and summed to generate the NOVA–UPF score (range: 0–50). Results: Among 237 young adults (aged 20–49 years), the mean NOVA–UPF score was 22.9 ± 8.3. A positive association was observed between the NOVA–UPF score and FFQbased UPF energy intake (Spearman’s ρ = 0.629, P < 0.001). Cross-classification showed that 51.9% were classified into the same tertile and 94.9% into the same or adjacent tertiles, with a weighted kappa coefficient of 0.279. Conclusion This NOVA-based scoring approach may serve as a preliminary tool for assessing UPF intake in Korean young adults. Further refinement and rigorous validation using quantitative dietary assessment methods and more diverse populations are required before broader application.

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: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Background: Posterior spinal fusion is performed for severe adolescent idiopathic scoliosis (AIS), with bone graft playing a critical role in achieving solid fusion. Iliac crest bone graft (ICBG) is the gold standard but has donor site morbidity, leading to increased use of non-ICBG. This study compared the effectiveness and safety of ICBG and non-ICBG bone grafts in AIS. Methods: This meta-analysis included 23 studies with 3,350 patients, categorized into ICBG and non-ICBG groups. The primary outcome was fusion rate; secondary outcomes were implant-related complication rate, infection rate, donor site pain, blood loss, and operation time. A proportion meta-analysis was conducted to estimate binary outcomes, and a weighted mean model was used to estimate continuous variables. Results: The fusion rate was 97% (95% confidence interval [CI], 93-99) in ICBG group and 97% (95% CI, 95-98) in non-ICBG group. Implant-related complications occurred in 5% (95% CI, 3-7) and 4% (95% CI, 2-6) of each group, while surgical site infections occurred in 3% (95% CI, 2-7) and 2% (95% CI, 1-3). Donor site pain was observed exclusively in ICBG group, with a prevalence of 49% (95% CI, 3–97). Estimated pooled blood loss was 1,461.10 mL (95% CI, 545.30-2,376.90) in ICBG group and 1,047.45 mL (95% CI, 666.92-1,427.99) in non-ICBG group. The estimated pooled operation time was 246.77 (95% CI, 221.74-271.80) and 232.37 (95% CI, 194.88-269.86) for each group. Conclusions Considering both ICBG and non-ICBG demonstrated high fusion rates and low complication rates, non-ICBG grafts may serve as an effective and safe alternative to ICBG, minimizing morbidity, donor site pain, and blood loss.

This comprehensive review explores the broad landscape of brain–computer interface (BCI) technology and its potential use in intensive care units (ICUs), particularly for patients with motor impairments such as quadriplegia or severe brain injury. By employing brain signals from various sensing techniques, BCIs offer enhanced communication and motor rehabilitation strategies for patients. This review underscores the concept and efficacy of noninvasive, electroencephalogram-based BCIs in facilitating both communicative interactions and motor function recovery. Additionally, it highlights the current research gap in intuitive “stop” mechanisms within motor rehabilitation protocols, emphasizing the need for advancements that prioritize patient safety and individualized responsiveness. Furthermore, it advocates for more focused research that considers the unique requirements of ICU environments to address the challenges arising from patient variability, fatigue, and limited applicability of current BCI systems outside of experimental settings.

This comprehensive review explores the broad landscape of brain–computer interface (BCI) technology and its potential use in intensive care units (ICUs), particularly for patients with motor impairments such as quadriplegia or severe brain injury. By employing brain signals from various sensing techniques, BCIs offer enhanced communication and motor rehabilitation strategies for patients. This review underscores the concept and efficacy of noninvasive, electroencephalogram-based BCIs in facilitating both communicative interactions and motor function recovery. Additionally, it highlights the current research gap in intuitive “stop” mechanisms within motor rehabilitation protocols, emphasizing the need for advancements that prioritize patient safety and individualized responsiveness. Furthermore, it advocates for more focused research that considers the unique requirements of ICU environments to address the challenges arising from patient variability, fatigue, and limited applicability of current BCI systems outside of experimental settings.