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.

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.

Tolvaptan reduces height-adjusted total kidney volume (htTKV) and renal function decline in autosomal dominant polycystic kidney disease (ADPKD). This study was aimed at investigating the efficacy and safety of tolvaptan in Korean patients with ADPKD during the titration period. Methods: This study is a multicenter, single-arm, open-label phase 4 study. We enrolled 108 patients with ADPKD (age, 19–50 years) with an estimated glomerular filtration rate (eGFR) of >30 mL/min/1.73 m2 and factors defined as indicative of rapid disease progression. After tolvaptan titration, we evaluated efficacy and side effects and assessed factors associated with the effects. Results: After titration for 4 weeks, eGFR and htTKV decreased by 6.4 ± 7.9 mL/min/1.73 m2 and 16 ± 45 mL/m, respectively. No serious adverse drug reactions were observed during the titration period. The greatest eGFR decline was observed in the first week, with a starting tolvaptan dose of 45 mg. Multivariate linear regression for htTKV decline showed that the greater the change in urine osmolality (Uosm), the greater the decrease in htTKV (β, 0.436; p = 0.009) in the 1D group stratified by the Mayo Clinic image classification. Higher baseline eGFR was related to a higher htTKV reduction rate in the 1E group (β, –0.642; p = 0.009). Conclusion: We observed short-term effects and safety during the tolvaptan titration period. The decline of htTKV can be predicted as a short-term effect of tolvaptan by observing Uosm changes from baseline to end of titration in 1D and baseline eGFR in 1E groups.

Plastic cannulae have attracted increasing interest as an alternative to traditional metal needles with the aim of reducing cannulation-related complications. We investigated whether the substitution volumes during hemodiafiltration differ using these two types of needles in dialysis patients. Methods: An intervention study involving 26 hemodialysis patients was conducted in Korea between March and September in 2021. Patients first received online hemodiafiltration using traditional metal needles, and thereafter plastic cannulae were used in a stepwise protocol. Repeated-measures design and linear mixed-effect models were used to compare substitution volumes between the two needle types with the same inner diameter. Results: The mean patient age was 62.7 years, and their mean dialysis vintage was 95.2 months. Most patients (92.3%) had an arteriovenous fistula as the vascular access. The substitution volume increased as blood flow and needle size increased for both plastic cannulae and metal needles. The substitution volume was significantly higher with 17-gauge (G) plastic cannulae than with 16-G metal needles at blood flow rates of 280, 300, and 330 mL/min. Similar results were obtained for 15-G metal needles and 16-G plastic cannulae at a blood flow rate of 330 mL/min. However, the patient ratings of pain on a visual analogue scale were higher for plastic cannulae. Conclusion: Higher substitution volumes were obtained at the same prescribed blood flow rate with plastic cannulae than with metal needles during online hemodiafiltration. Plastic cannulae are an option for achieving high-volume hemodiafiltration for patients with low blood flow rates.

OBJECTIVES: The reported effects of digital media overuse on physical activity among adolescents are inconsistent. This study examined the association between hours of digital media use and lack of moderate-intensity physical exercise (mPE) according to the type of digital media. METHODS: This study included 1,837 middle school students from the iCURE (Internet user Cohort for Unbiased Recognition of gaming disorder in Early Adolescence) study conducted in Korea. Hours spent using digital media were measured by self-reported daily usage time for Internet games, messengers, social media, and watching game streaming on weekdays. Lack of mPE was defined as performing a minimum of 30 minutes at a time less than twice weekly. Multivariable logistic regression analysis stratified by sex was performed. RESULTS: Among male students, the group with the highest hours of using either Internet games or watching game streaming was more likely to lack mPE than each non-user group. In contrast, among male students, the group using either messengers or social media had a higher rate of mPE compared to each non-user group. Female students showed no association between hours spent using Internet games, messengers, social media, or watching game streaming and a lack of mPE. CONCLUSIONS Among male middle school students in Korea, the excessive use of Internet games or watching game streaming was associated with a lack of mPE. Thus, guidelines should be established regarding adolescent use of internet games and watching game streaming.

OBJECTIVES: Coronavirus disease 2019 (COVID-19) and the associated social distancing, limited freedom, and fear of an uncertain future are expected to have substantial mental health effects. We investigated mental health responses in the community during the first year of the COVID-19 pandemic in Korea. METHODS: We used 2016-2019 and 2020 data from the Korea National Health and Nutrition Examination Survey (KNHANES) to assess pre-pandemic and pandemic mental health status, respectively, in terms of perceived severe stress, depression, and suicidal plans. All analyses were gender-stratified. Pre-specified subgroup analyses were performed according to age, employment status, and household income. RESULTS: The percentage of Korean adults with suicidal plans increased significantly from 1.3%p (95% confidence interval [CI], 1.1 to 1.5) in 2016-2019 to 1.8%p (95% CI, 1.4 to 2.1) in 2020. Individuals in their 20s and 40s showed a marked increase in suicidal plans (1.2%p; 95% CI, 0.0 to 2.3 and 0.9%p; 95% CI, 0.0 to 1.8, respectively). In men, depression and perceived severe stress increased significantly from pre-COVID-19 to 2020. There was a 2.4%p (95% CI, 0.8 to 4.0) increase in depression among standard workers and a 2.9%p increase in depression in individuals in the second-highest quintile of household income from 2016 and 2018 to 2020. CONCLUSIONS As COVID-19 continued, mental health issues such as suicidal plans, depression, and severe stress increased significantly in young men and people in the second-highest quintile of household income. Proactive community mental health efforts are needed to prevent increases in the suicide rate resulting from prolonged exposure to the COVID-19 pandemic.

A survival prediction model has recently been developed to evaluate the prognosis of resected nonmetastatic pancreatic ductal adenocarcinoma based on a Cox model using two nationwide databases: Surveillance, Epidemiology and End Results (SEER) and Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP). In this study, we applied two machine learning methods—random survival forests (RSF) and support vector machines (SVM)—for survival analysis and compared their prediction performance using the SEER and KOTUS-BP datasets. Three schemes were used for model development and evaluation. First, we utilized data from SEER for model development and used data from KOTUS-BP for external evaluation. Second, these two datasets were swapped by taking data from KOTUS-BP for model development and data from SEER for external evaluation. Finally, we mixed these two datasets half and half and utilized the mixed datasets for model development and validation. We used 9,624 patients from SEER and 3,281 patients from KOTUS-BP to construct a prediction model with seven covariates: age, sex, histologic differentiation, adjuvant treatment, resection margin status, and the American Joint Committee on Cancer 8th edition T-stage and N-stage. Comparing the three schemes, the performance of the Cox model, RSF, and SVM was better when using the mixed datasets than when using the unmixed datasets. When using the mixed datasets, the C-index, 1-year, 2-year, and 3-year time-dependent areas under the curve for the Cox model were 0.644, 0.698, 0.680, and 0.687, respectively. The Cox model performed slightly better than RSF and SVM.