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.

Autosomal dominant polycystic kidney disease (ADPKD), one of the most common human monogenic diseases, is characterized by the presence of numerous fluid-filled renal cysts and is a leading cause of end-stage renal disease (ESRD). Urinary biomarkers may be useful for predicting the variable course of ADPKD progression from cyst growth to ESRD. Methods: To identify candidate urinary biomarkers of ADPKD progression, we used CRISPR/Cas9 genome editing to generate porcine fibroblasts with mono- and biallelic ADPKD gene knockout (PKD2+/– and PKD2–/–, respectively). We then performed RNA-sequencing analysis on these cells. Results: Levels of osteopontin (OPN), which is expressed by renal epithelial tubular cells and excreted into urine, were reduced in PKD2–/– cells but not in PKD2+/– cells. OPN levels were also reduced in the renal cyst cells of ADPKD patients. Next, we investigated whether OPN excretion was decreased in patients with ADPKD via enzyme-linked immunosorbent assay. OPN levels excreted into renal cyst cell culture media and urine from ADPKD patients were decreased. To investigate whether OPN can predict the rate of ADPKD progression, we compared urinary excretion of OPN in ADPKD patients with slow progression and those with rapid progression. Those with rapid progression had an estimated glomerular filtration rate of >60 mL/min/1.73 m2 . Urinary OPN excretion levels were lower in rapid progressors than in slow progressors. Conclusion: These findings suggest that OPN is a useful urinary biomarker for predicting ADPKD progression.

Background: In 2017, Korea implemented nationwide latent tuberculosis infection (LTBI) project targeting healthcare workers (HCWs). We aimed to assess its performance using the cascade of care model. Methods: We included 45,503 employees of medical institutions with positive interferongamma release assay result who participated between March 2017 and December 2018. We described percentages of LTBI participants completing each step in the cascade of care.Poisson regression model was conducted to assess individual characteristics and factors associated with not-visiting clinics for further care, not-initiating LTBI treatment, and notcompleting treatment. Results: Proportions of visiting clinics and initiating and completing treatment in HCWs were 54.9%, 38.5%, and 32.0%, respectively. Despite of less likelihood of visiting clinics and initiating LTBI treatment, older age ≥ 65 years were more likely to complete treatment (adjusted relative risk [aRR], 0.80; 95% confidence interval [CI], 0.64–0.99), compared to young age < 35 years. Compared to nurses, doctors were less likely to visit clinic; however, were more likely to initiate treatment (aRR, 0.88; 95% CI, 0.81–0.96). Those who visited public health centers were associated with not-initiating treatment (aRR, 1.34; 95% CI, 1.29–1.40). When treated at private hospitals, 9-month isoniazid monotherapy was less likely to complete treatment, compared to 3-month isoniazid and rifampicin combination therapy (aRR, 1.33; 95% CI, 1.16–1.53). Conclusion Among employees of medical institutions with LTBI, only one third completed treatment. Age, occupation, treatment center, and initial regimen were significantly related to LTBI treatment performance indicators. Rifampicin-based short treatment regimens were effective under standard of care.

Diabetic nephropathy (DN) can affect quality of life (QoL) because it requires arduous lifelong management. This study analyzed QoL differences between DN patients and patients with other chronic kidney diseases (CKDs). Methods: The analysis included subjects (n = 1,766) from the KNOW-CKD (Korean Cohort Study for Outcomes in Patients with Chronic Kidney Disease) cohort who completed the Kidney Disease Quality of Life Short Form questionnaire. After implementing propensity score matching (PSM) using factors that affect the QoL of DN patients, QoL differences between DN and non-DN participants were examined. Results: Among all DN patients (n = 390), higher QoL scores were found for taller subjects, and lower scores were found for those who were unemployed or unmarried, received Medical Aid, had lower economic status, had higher platelet counts or alkaline phosphatase levels, or used clopidogrel or insulin. After PSM, the 239 matched DN subjects reported significantly lower patient satisfaction (59.9 vs. 64.5, p = 0.02) and general health (35.3 vs. 39.1, p = 0.04) than the 239 non-DN subjects. Scores decreased in both groups during the 5-year follow-up, and the scores in the work status, sexual function, and role-physical domains were lower among DN patients than non-DN patients, though those differences were not statistically significant. Conclusion: Socioeconomic factors of DN were strong risk factors for impaired QoL, as were high platelet, alkaline phosphatase, and clopidogrel and insulin use. Clinicians should keep in mind that the QoL of DN patients might decrease in some domains compared with non-DN CKDs.

Purpose: The objectives of this study were to identify the types of recovered alcoholics based on the factors that influence recovery, using the Q methodology. Methods: Forty-six statements were selected from in-depth interviews with eight psychiatric healthcare professionals and four abstinent alcoholics and literature review. Then, 50 participants sorted these statements in a forced grid with normal distribution from -4 to 4. Finally, factor analysis was performed with the PC-QUANL program. Results: Three types of recovered alcoholics were identified. Firstly, the ‘positive-centered recovery type’ emphasized on self-affirmation and self-confidence and acted on the belief of the possibility of recovery from alcoholism. The second type was the ‘temperance-centered recovery type’, which focused on complete control over daily life to stay sober. Finally, the ‘acceptance-centered type’ was likely to admit the weakness of human beings and pursue spirituality. Conclusion This study found three types of recovered alcoholics, focusing on the factors that had an impact on recovery and analyzed their characteristics. These findings provided useful information to understand recovered alcoholics, and they can be used to develop a tailored intervention to prevent a relapse of alcoholism.