Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Objective: We aimed to investigate the associations of the triglyceride-glucose index, which measures insulin resistance, and the incidence of Parkinson’s disease. Methods: Our study used the Health Screening Cohort database of the National Health Insurance Service of South Korea (2002–2019). We included 310,021 participants who had no previous history of Parkinson’s disease and for whom more than 3 triglyceride-glucose index measurements were available. A diagnosis of Parkinson’s disease was determined via the International Classification of Diseases Tenth edition (G20) with a specific reimbursement code for rare intractable diseases and a history of prescriptions for anti-Parkinsonism drugs. Results: During a median of 9.64 years (interquartile range 8.72–10.53), 4,587 individuals (1.5%) had Parkinson’s disease. Based on a multivariable time-dependent Cox proportional hazards model, a per-unit increase in triglyceride-glucose index score was associated with a significantly increased risk of Parkinson’s disease (hazard ratio [HR]: 1.062; 95% confidence interval [CI] 1.007–1.119). In a sensitivity analysis, the triglyceride-glucose index was associated with the incidence of Parkinson’s disease in a non–diabetes mellitus cohort (HR: 1.093; 95% CI 1.025–1.165), but not in the diabetes mellitus cohort (HR: 0.990; 95% CI 0.902–1.087). In a restricted cubic spline analysis, the association between the triglyceride-glucose index and the incidence risk of Parkinson’s disease showed a nonlinear increasing (J-shaped) trend. Conclusion Our study demonstrated that higher triglyceride-glucose index scores were associated with the incidence of Parkinson’s disease in the general population, particularly in a nondiabetic mellitus cohort.

Objective: The Huntington’s Disease Quality of Life Battery for Carers (HDQoL-C) is used to evaluate caregiver quality of life. This study aimed to develop and validate the Korean version of the HDQoL-C (K-HDQoL-C) to assess the burden on Korean caregivers of Huntington’s disease (HD) patients. Methods: A total of 19 HD caregivers (7 females, mean age 55.4±14.6 years) participated in this study. The K-HDQoL-C, a translation of the English version, consisted of demographic information, caring aspects, life satisfaction, and feelings about life. It was administered twice, 2 weeks apart. Internal consistency was evaluated using Cronbach’s α, and test-retest reliability was assessed with intraclass correlation coefficients. The relationship with the Zarit Burden Interview-12 (ZBI-12) was analyzed. Results: The internal consistencies of the K-HDQoL-C were 0.771 (part 2), 0.938 (part 3), and 0.891 (part 4). The test-retest reliability ranged from 0.908 to 0.936. Part 3 was negatively correlated with the ZBI-12, and part 4 was positively correlated with the ZBI-12 (r=-0.780, 0.923; p<0.001). Conclusion The K-HDQoL-C effectively evaluates the challenges faced by HD caregivers, particularly in terms of care aspects and life satisfaction.

and Relevance: Severe necrosis and mild fibrosis were observed in the liver and forelimb skeletal muscles. Based on histological analysis, we diagnosed iron overload myopathy by secondary hemochromatosis. Secondary hemochromatosis with severe muscle atrophy and myositis is very rare, and this is the first report of iron-overload myopathy in a dog.

To investigate the relationship between depression and AD, water avoidance stress (WAS) was induced for 10 days in both Tg2576 mice and wild-type (WT) mice. After WAS, memory function and depressive-like behavior were investigated in Tg2576 mice. Tg2576 WAS mice exhibited more depressive-like behaviors than WT WAS and Tg2576 control (CON) mice. Strikingly, Tg2576 CON mice showed more depressive-like behaviors than WT mice. Moreover, corticosterone and phospho-glucocorticoid receptor (p-GR) levels were also higher in Tg2576 WAS mice in comparison to Tg2576 CON mice. Spinster homologue 2 (SPNS2) is a member of non-ATP-dependent transporter. The role of SPNS2 was widely known as a sphingosine-1-phosphate (S1P) transporter, which export intracellular S1P from cells. Using GEO database to analyze SPNS2 gene expression changes in patients with AD and depression, we show that SPNS2 gene expression correlates with AD and depression. Interestingly, Tg2576 WAS mice displayed significantly increased levels of SPNS2 w1hen compared to Tg2576 CON counterparts. SPNS2 levels were also higher in Tg2576 CON mice in comparison with WT CON mice. Remarkably, we found a decrease in S1P brain levels and an increase in S1P serum levels of Tg2576 WAS mice in comparison with Tg2576 CON mice. Accordingly, WAS induced group further decreased S1P levels in the brains. However, the level in the serum further increased in comparison with non-induced group. Therefore, these results suggest that AD and depression could be associated, and that Tg2576 transgenic mice are more susceptible to stress-induced depression through the release of S1P by SPNS2 up-regulation.

Purpose: This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns. Materials and Methods: As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both 18F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features. Results: Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively). Conclusion Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

Purpose: In Korea, the Act on Hospice and Palliative Care and Decisions on Life-Sustaining Treatment (LST) was implemented on February 4, 2018. We aimed to investigate relevant factors and clinical changes associated with LST decisions after law enforcement. Materials and Methods: This single-center retrospective study included patients who completed LST documents using legal forms at Asan Medical Center from February 5, 2018, to June 30, 2020. Results: 5,896 patients completed LST documents, of which 2,704 (45.8%) signed the documents in person, while family members of 3,192 (54%) wrote the documents on behalf of the patients. Comparing first year and following year of implementation of the act, the self-documentation rate increased (43.9% to 47.2%, p=0.014). Moreover, the number of LST decisions made during or after intensive care unit admission decreased (37.8% vs. 35.2%, p=0.045), and the completion rate of LST documents during chemotherapy increased (6.6% vs. 8.9%, p=0.001). In multivariate analysis, age < 65 (odds ratio [OR], 1.724; 95% confidence interval [CI], 1.538 to 1.933; p < 0.001), unmarried status (OR, 1.309; 95% CI, 1.097 to 1.561; p=0.003), palliative care consultation (OR, 1.538; 95% CI, 1.340 to 1.765; p < 0.001), malignancy (OR, 1.864; 95% CI, 1.628 to 2.133; p < 0.001), and changes in timing on the first year versus following year (OR, 1.124; 95% CI, 1.003 to 1.260; p=0.045) were related to a higher self-documentation rate. Conclusion Age < 65 years, unmarried status, malignancy, and referral to a palliative care team were associated with patients making LST decisions themselves. Furthermore, the subject and timing of LST decisions have changed with the LST act.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

and Relevance: Severe necrosis and mild fibrosis were observed in the liver and forelimb skeletal muscles. Based on histological analysis, we diagnosed iron overload myopathy by secondary hemochromatosis. Secondary hemochromatosis with severe muscle atrophy and myositis is very rare, and this is the first report of iron-overload myopathy in a dog.