Objectives: Korea is one of the fastest-aging societies, and a large proportion of its older population lives alone. This study examined the impact of family interaction frequency on the association between living alone and depressive symptoms among older adults aged ≥80 years using nationally representative survey data. Methods: Among the 229 099 participants of the 2019 Korea Community Health Survey, 15 672 participants aged ≥80 years who either lived with close family or lived alone were included in the analysis. Participants living alone were classified according to the frequency of family interaction, ranging from less than once a month to more than once a week. The outcome variable was moderate to severe depressive symptoms, defined as a Patient Health Questionnaire-9 score of ≥10. Results: The prevalence of depressive symptoms was higher among older adults living alone (9.4–14.1%, depending on the frequency of family interaction) than among those living with close family (6.5%). Older adults living alone who interacted with family less than once a month were more likely to report depressive symptoms compared with those living with close family (adjusted odds ratio [aOR], 1.71; 95% confidence interval [CI], 1.36 to 2.15). Weekly family interaction mitigated the impact of living alone on the prevalence of depressive symptoms (aOR 1.10; 95% CI, 0.84 to 1.42). The influence of family interaction on the association between living alone and depressive symptoms remained consistent across subgroups of men, women, and those with difficulty in daily activities. Conclusions Encouraging regular interaction among family members could serve as an effective strategy to protect the mental health of older adults.

Objective: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality. Materials and Methods: This retrospective study included 46 pediatric patients who underwent conventional and accelerated, pre- and post-contrast, 3D T1-weighted brain MRI using a 3T scanner (SIGNA Premier; GE HealthCare) at a single tertiary referral center between March 1, 2023, and April 30, 2023. Conventional scans were reconstructed using intensity Filter A (Conv), whereas accelerated scans were reconstructed using intensity Filter A (Fast_A) and a DL-based algorithm (Fast_DL).Image quality was assessed quantitatively based on the coefficient of variation, relative contrast, apparent signal-to-noise ratio (aSNR), and apparent contrast-to-noise ratio (aCNR) and qualitatively according to radiologists’ ratings of overall image quality, artifacts, noisiness, gray-white matter differentiation, and lesion conspicuity. Results: The acquisition times for the pre- and post-contrast scans were 191 and 135 seconds, respectively, for the conventional scan. With the accelerated protocol, these were reduced to 135 and 80 seconds, achieving time reductions of 29.3% and 40.7%, respectively. DL-based reconstruction significantly reduced the coefficient of variation, improved the aSNR, aCNR, and overall image quality, and reduced the number of artifacts compared with the conventional acquisition method (all P < 0.05). However, the lesion conspicuity remained similar between the two protocols. Conclusion Utilizing a DL-based reconstruction algorithm in accelerated 3D T1-weighted pediatric brain MRI can significantly shorten the acquisition time, enhance image quality, and reduce artifacts, making it a viable option for pediatric imaging.

Purpose: The global increase in forcibly displaced people, combined with insufficient aid, leaves many—in particular, people with epilepsy—in a dire medical state. Our study aimed to understand the demographics and clinical features of epilepsy in the Nakivale refugee settlement and to highlight our intervention through the ‘CARE FOR ALL’ project, which will run for 5 years. Methods: Between August 2022 and May 2023, we conducted four outreach visits across three locations in Uganda, consulting 161 patients. After excluding incomplete data, we analyzed the medical records of 81 epilepsy cases. Results: Of the 81 patients, most were male (65.4%), under 18 years old (77.8%), had low education levels (93.8%), and were predominantly Congolese (58.0%). The majority experienced focal onset seizures (51.8%), and epilepsy began before the age of one in 28.4% of patients. All patients had comorbidities, with intellectual impairment (70.4%) and cerebral palsy (27.2%) being the most common. Identified risk factors included antenatal complications, central nervous system infections, and war-related injuries. Before our intervention, the treatment gap was 76.5%; this was reduced to 0% after the project, which also significantly decreased seizure frequency (seizure freedom 30.9%, P<0.05). Carbamazepine was the most common antiseizure medication used (59.2%). Conclusion Refugees with epilepsy face major barriers to care that negatively impact their quality of life. A coordinated effort by governments and health agencies is crucial to overcome these challenges and improve outcomes for displaced individuals with epilepsy.

Purpose: The global increase in forcibly displaced people, combined with insufficient aid, leaves many—in particular, people with epilepsy—in a dire medical state. Our study aimed to understand the demographics and clinical features of epilepsy in the Nakivale refugee settlement and to highlight our intervention through the ‘CARE FOR ALL’ project, which will run for 5 years. Methods: Between August 2022 and May 2023, we conducted four outreach visits across three locations in Uganda, consulting 161 patients. After excluding incomplete data, we analyzed the medical records of 81 epilepsy cases. Results: Of the 81 patients, most were male (65.4%), under 18 years old (77.8%), had low education levels (93.8%), and were predominantly Congolese (58.0%). The majority experienced focal onset seizures (51.8%), and epilepsy began before the age of one in 28.4% of patients. All patients had comorbidities, with intellectual impairment (70.4%) and cerebral palsy (27.2%) being the most common. Identified risk factors included antenatal complications, central nervous system infections, and war-related injuries. Before our intervention, the treatment gap was 76.5%; this was reduced to 0% after the project, which also significantly decreased seizure frequency (seizure freedom 30.9%, P<0.05). Carbamazepine was the most common antiseizure medication used (59.2%). Conclusion Refugees with epilepsy face major barriers to care that negatively impact their quality of life. A coordinated effort by governments and health agencies is crucial to overcome these challenges and improve outcomes for displaced individuals with epilepsy.

Objectives: Adhering to management guidelines, including routine follow-up and education, is crucial for the effective management of chronic diseases such as diabetes. Uncontrolled diabetes is linked to poor sleep quality. We used nationally representative data from community-dwelling Koreans to investigate potential associations between non-adherence to diabetes management and poor sleep quality. Methods: Of the 228 340 participants in the 2018 Korea Community Health Survey, we analyzed data from 68 246 participants aged 65 years and older. Based on their diabetes status and adherence to diabetes-related tests (glycated hemoglobin, fundus examination, and urine microalbuminuria) and management education, study participants were divided into 3 groups: no diabetes, adherence, and non-adherence. Outcome variables included poor overall sleep quality and sleep disturbances, as measured by the Pittsburgh Sleep Quality Index. Results: The study included 934 participants in the adherence group (2.0%) and 13 420 in the non-adherence group (20.2%). Participants displaying non-adherence were significantly more likely to report poor overall sleep quality (adjusted odds ratio [aOR], 1.09; 95% confidence interval [CI], 1.04 to 1.13) and to experience sleep fragmentation, nocturia, pain during sleep, and difficulty falling asleep compared to participants without diabetes. Even in the earlier stage of diabetes, non-adherent participants were more likely to report poor sleep quality (aOR, 1.06; 95% CI, 1.00 to 1.18). Conclusions Patients with diabetes who do not follow management guidelines are more likely to experience poor sleep quality than those without diabetes. Emphasizing diabetes management and increasing awareness of management strategies may improve sleep quality among patients with this disease.

Objective: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality. Materials and Methods: This retrospective study included 46 pediatric patients who underwent conventional and accelerated, pre- and post-contrast, 3D T1-weighted brain MRI using a 3T scanner (SIGNA Premier; GE HealthCare) at a single tertiary referral center between March 1, 2023, and April 30, 2023. Conventional scans were reconstructed using intensity Filter A (Conv), whereas accelerated scans were reconstructed using intensity Filter A (Fast_A) and a DL-based algorithm (Fast_DL).Image quality was assessed quantitatively based on the coefficient of variation, relative contrast, apparent signal-to-noise ratio (aSNR), and apparent contrast-to-noise ratio (aCNR) and qualitatively according to radiologists’ ratings of overall image quality, artifacts, noisiness, gray-white matter differentiation, and lesion conspicuity. Results: The acquisition times for the pre- and post-contrast scans were 191 and 135 seconds, respectively, for the conventional scan. With the accelerated protocol, these were reduced to 135 and 80 seconds, achieving time reductions of 29.3% and 40.7%, respectively. DL-based reconstruction significantly reduced the coefficient of variation, improved the aSNR, aCNR, and overall image quality, and reduced the number of artifacts compared with the conventional acquisition method (all P < 0.05). However, the lesion conspicuity remained similar between the two protocols. Conclusion Utilizing a DL-based reconstruction algorithm in accelerated 3D T1-weighted pediatric brain MRI can significantly shorten the acquisition time, enhance image quality, and reduce artifacts, making it a viable option for pediatric imaging.

Objective: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality. Materials and Methods: This retrospective study included 46 pediatric patients who underwent conventional and accelerated, pre- and post-contrast, 3D T1-weighted brain MRI using a 3T scanner (SIGNA Premier; GE HealthCare) at a single tertiary referral center between March 1, 2023, and April 30, 2023. Conventional scans were reconstructed using intensity Filter A (Conv), whereas accelerated scans were reconstructed using intensity Filter A (Fast_A) and a DL-based algorithm (Fast_DL).Image quality was assessed quantitatively based on the coefficient of variation, relative contrast, apparent signal-to-noise ratio (aSNR), and apparent contrast-to-noise ratio (aCNR) and qualitatively according to radiologists’ ratings of overall image quality, artifacts, noisiness, gray-white matter differentiation, and lesion conspicuity. Results: The acquisition times for the pre- and post-contrast scans were 191 and 135 seconds, respectively, for the conventional scan. With the accelerated protocol, these were reduced to 135 and 80 seconds, achieving time reductions of 29.3% and 40.7%, respectively. DL-based reconstruction significantly reduced the coefficient of variation, improved the aSNR, aCNR, and overall image quality, and reduced the number of artifacts compared with the conventional acquisition method (all P < 0.05). However, the lesion conspicuity remained similar between the two protocols. Conclusion Utilizing a DL-based reconstruction algorithm in accelerated 3D T1-weighted pediatric brain MRI can significantly shorten the acquisition time, enhance image quality, and reduce artifacts, making it a viable option for pediatric imaging.

Objective: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality. Materials and Methods: This retrospective study included 46 pediatric patients who underwent conventional and accelerated, pre- and post-contrast, 3D T1-weighted brain MRI using a 3T scanner (SIGNA Premier; GE HealthCare) at a single tertiary referral center between March 1, 2023, and April 30, 2023. Conventional scans were reconstructed using intensity Filter A (Conv), whereas accelerated scans were reconstructed using intensity Filter A (Fast_A) and a DL-based algorithm (Fast_DL).Image quality was assessed quantitatively based on the coefficient of variation, relative contrast, apparent signal-to-noise ratio (aSNR), and apparent contrast-to-noise ratio (aCNR) and qualitatively according to radiologists’ ratings of overall image quality, artifacts, noisiness, gray-white matter differentiation, and lesion conspicuity. Results: The acquisition times for the pre- and post-contrast scans were 191 and 135 seconds, respectively, for the conventional scan. With the accelerated protocol, these were reduced to 135 and 80 seconds, achieving time reductions of 29.3% and 40.7%, respectively. DL-based reconstruction significantly reduced the coefficient of variation, improved the aSNR, aCNR, and overall image quality, and reduced the number of artifacts compared with the conventional acquisition method (all P < 0.05). However, the lesion conspicuity remained similar between the two protocols. Conclusion Utilizing a DL-based reconstruction algorithm in accelerated 3D T1-weighted pediatric brain MRI can significantly shorten the acquisition time, enhance image quality, and reduce artifacts, making it a viable option for pediatric imaging.

Objective: This study evaluated the effect of an accelerated three-dimensional (3D) T1-weighted pediatric brain MRI protocol using a deep learning (DL)-based reconstruction algorithm on scan time and image quality. Materials and Methods: This retrospective study included 46 pediatric patients who underwent conventional and accelerated, pre- and post-contrast, 3D T1-weighted brain MRI using a 3T scanner (SIGNA Premier; GE HealthCare) at a single tertiary referral center between March 1, 2023, and April 30, 2023. Conventional scans were reconstructed using intensity Filter A (Conv), whereas accelerated scans were reconstructed using intensity Filter A (Fast_A) and a DL-based algorithm (Fast_DL).Image quality was assessed quantitatively based on the coefficient of variation, relative contrast, apparent signal-to-noise ratio (aSNR), and apparent contrast-to-noise ratio (aCNR) and qualitatively according to radiologists’ ratings of overall image quality, artifacts, noisiness, gray-white matter differentiation, and lesion conspicuity. Results: The acquisition times for the pre- and post-contrast scans were 191 and 135 seconds, respectively, for the conventional scan. With the accelerated protocol, these were reduced to 135 and 80 seconds, achieving time reductions of 29.3% and 40.7%, respectively. DL-based reconstruction significantly reduced the coefficient of variation, improved the aSNR, aCNR, and overall image quality, and reduced the number of artifacts compared with the conventional acquisition method (all P < 0.05). However, the lesion conspicuity remained similar between the two protocols. Conclusion Utilizing a DL-based reconstruction algorithm in accelerated 3D T1-weighted pediatric brain MRI can significantly shorten the acquisition time, enhance image quality, and reduce artifacts, making it a viable option for pediatric imaging.

Purpose: The global increase in forcibly displaced people, combined with insufficient aid, leaves many—in particular, people with epilepsy—in a dire medical state. Our study aimed to understand the demographics and clinical features of epilepsy in the Nakivale refugee settlement and to highlight our intervention through the ‘CARE FOR ALL’ project, which will run for 5 years. Methods: Between August 2022 and May 2023, we conducted four outreach visits across three locations in Uganda, consulting 161 patients. After excluding incomplete data, we analyzed the medical records of 81 epilepsy cases. Results: Of the 81 patients, most were male (65.4%), under 18 years old (77.8%), had low education levels (93.8%), and were predominantly Congolese (58.0%). The majority experienced focal onset seizures (51.8%), and epilepsy began before the age of one in 28.4% of patients. All patients had comorbidities, with intellectual impairment (70.4%) and cerebral palsy (27.2%) being the most common. Identified risk factors included antenatal complications, central nervous system infections, and war-related injuries. Before our intervention, the treatment gap was 76.5%; this was reduced to 0% after the project, which also significantly decreased seizure frequency (seizure freedom 30.9%, P<0.05). Carbamazepine was the most common antiseizure medication used (59.2%). Conclusion Refugees with epilepsy face major barriers to care that negatively impact their quality of life. A coordinated effort by governments and health agencies is crucial to overcome these challenges and improve outcomes for displaced individuals with epilepsy.