Janus kinase (JAK) inhibitors are an important treatment option for ulcerative colitis, providing rapid onset of action, oral administration, and efficacy even after biologic failure. The 3 approved agents—tofacitinib, filgotinib, and upadacitinib—differ in JAK isoform selectivity, leading to clinically meaningful differences in efficacy and safety. Evidence from network meta-analyses, clinical trials, and real-world studies consistently shows that upadacitinib provides the highest efficacy for induction and maintenance of remission, whereas filgotinib demonstrates the most favorable safety profile. The strong efficacy of upadacitinib and tofacitinib is particularly relevant in patients with severe disease, including acute severe ulcerative colitis, and upadacitinib maintains high efficacy regardless of prior advanced therapy exposure. JAK inhibitors also benefit extraintestinal manifestations. Although risks such as herpes zoster, serious infection, thromboembolism, and major cardiovascular events differ among agents, long-term data suggest generally acceptable safety when used appropriately. Intraclass JAK-to-JAK cycling is feasible, with about half of patients achieving steroid-free clinical remission in retrospective cohorts. Based on mechanistic, clinical, and real-world evidence, filgotinib may be a first-line option for patients with lower disease activity or when safety is a priority, whereas upadacitinib or tofacitinib may be preferred in higher disease activity. Strategically selecting agents may improve durability and outcomes.

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.

This study aimed to investigate changes in candidemia incidence, species distribution, antifungal susceptibility, initial antifungal use, and mortality trends in Korea before and during the COVID-19 pandemic. A retrospective analysis was conducted on candidemia cases from two tertiary care hospitals in Korea between 2017 and 2022. Data were compared between the pre-pandemic (2017-2019) and pandemic (2020-2022) periods. Statistical methods included incidence rate ratios (IRRs) and multivariate Cox regression to assess 30-day mortality risk factors. A total of 470 candidemia cases were identified, with 48.7% occurring pre-pandemic and 51.3% during the pandemic. While the overall incidence of candidemia remained similar across the two periods (IRR 1.15;p=0.13), the incidence in intensive care units (ICUs) significantly increased during the pandemic (IRR 1.50; p<0.01). The distribution of Candida species did not differ significantly between the two periods. Fluconazole non-susceptibility in C. albicans markedly decreased (10.0% vs. 0.9%, p<0.01), whereas C. glabrata exhibited a significant rise in caspofungin non-susceptibility during the pandemic (0% vs. 22.4%, p<0.01).Echinocandin use increased (21.8% vs. 34.4%; p<0.01), while fluconazole use declined (48.0% vs. 32.8%; p<0.01). Although the 30-day mortality rate was higher during the pandemic (60.2% vs. 57.2%), the difference was not statistically significant (p=0.57).The findings highlight the need for region-specific surveillance and tailored management strategies to improve candidemia outcomes, especially during healthcare disruptions like the COVID-19 pandemic.

Gastric cancer is one of the most common cancers in both Korea and worldwide. Since 2004, the Korean Practice Guidelines for Gastric Cancer have been regularly updated, with the 4th edition published in 2022. The 4th edition was the result of a collaborative work by an interdisciplinary team, including experts in gastric surgery, gastroenterology, endoscopy, medical oncology, abdominal radiology, pathology, nuclear medicine, radiation oncology, and guideline development methodology. The current guideline is the 5th version, an updated version of the 4th edition. In this guideline, 6 key questions (KQs) were updated or proposed after a collaborative review by the working group, and 7 statements were developed, or revised, or discussed based on a systematic review using the MEDLINE, Embase, Cochrane Library, and KoreaMed database. Over the past 2 years, there have been significant changes in systemic treatment, leading to major updates and revisions focused on this area.Additionally, minor modifications have been made in other sections, incorporating recent research findings. The level of evidence and grading of recommendations were categorized according to the Grading of Recommendations, Assessment, Development and Evaluation system. Key factors for recommendation included the level of evidence, benefit, harm, and clinical applicability. The working group reviewed and discussed the recommendations to reach a consensus. The structure of this guideline remains similar to the 2022 version.Earlier sections cover general considerations, such as screening, diagnosis, and staging of endoscopy, pathology, radiology, and nuclear medicine. In the latter sections, statements are provided for each KQ based on clinical evidence, with flowcharts supporting these statements through meta-analysis and references. This multidisciplinary, evidence-based gastric cancer guideline aims to support clinicians in providing optimal care for gastric cancer patients.

Background: Barriers to treatment with intravenous thrombolysis (IVT) for patients with acute ischemic stroke (AIS) in South Korea remain incompletely characterized. We analyze a nationwide prospective cohort to determine patient-level features associated with delayed presentation and non-treatment of potential IVT-eligible patients. Methods: We identified consecutive patients with AIS from 01/2011 to 08/2023 from a multicenter and prospective acute stroke registry in Korea. Patients were defined as IVT candidates if they presented within 4.5 hours from the last known well, had no lab evidence of coagulopathy, and had National Institute of Health Stroke Scale (NIHSS) ≥ 4. Multivariable generalized linear mixed regression models were used to investigate the associations between their characteristics and the IVT candidates or the use of IVT among the candidates. Results: Among 84,103 AIS patients, 41.0% were female, with a mean age of 69 ± 13 years and presentation NIHSS of 4 [interquartile range, 1–8]. Out of these patients, 13,757 (16.4%) were eligible for IVT, of whom 8,179 (59.5%) received IVT. Female sex (adjusted risk ratio [RR], 0.90; 95% confidence interval [CI], 0.86–0.94) and lower years of education (adjusted RR, 0.90; 95% CI, 0.84–0.97 for 0–3 years, compared to ≥ 13 years) were associated with a decreased likelihood of presenting as eligible for IVT after AIS; meanwhile, young age (adjusted RR, 1.12; 95% CI, 1.01–1.24 for ≤ 44 years, compared to 75–84 years) was associated with an increased likelihood of being an IVT candidate. Among those who were eligible for IVT, only age was significantly associated with the use of IVT (adjusted RR, 1.09; 95% CI, 1.03–1.16 for age 65–74 and adjusted RR, 0.83; 95% CI, 0.76–0.90 for ≥ 85 years, respectively). Conclusion Most patients with AIS present outside IVT eligibility in South Korea, and only 60% of eligible patients were ultimately treated. We identified increased age, female sex and lower education as key features on which to focus interventions for improving IVT utilization.

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.

This study aimed to investigate changes in candidemia incidence, species distribution, antifungal susceptibility, initial antifungal use, and mortality trends in Korea before and during the COVID-19 pandemic. A retrospective analysis was conducted on candidemia cases from two tertiary care hospitals in Korea between 2017 and 2022. Data were compared between the pre-pandemic (2017-2019) and pandemic (2020-2022) periods. Statistical methods included incidence rate ratios (IRRs) and multivariate Cox regression to assess 30-day mortality risk factors. A total of 470 candidemia cases were identified, with 48.7% occurring pre-pandemic and 51.3% during the pandemic. While the overall incidence of candidemia remained similar across the two periods (IRR 1.15;p=0.13), the incidence in intensive care units (ICUs) significantly increased during the pandemic (IRR 1.50; p<0.01). The distribution of Candida species did not differ significantly between the two periods. Fluconazole non-susceptibility in C. albicans markedly decreased (10.0% vs. 0.9%, p<0.01), whereas C. glabrata exhibited a significant rise in caspofungin non-susceptibility during the pandemic (0% vs. 22.4%, p<0.01).Echinocandin use increased (21.8% vs. 34.4%; p<0.01), while fluconazole use declined (48.0% vs. 32.8%; p<0.01). Although the 30-day mortality rate was higher during the pandemic (60.2% vs. 57.2%), the difference was not statistically significant (p=0.57).The findings highlight the need for region-specific surveillance and tailored management strategies to improve candidemia outcomes, especially during healthcare disruptions like the COVID-19 pandemic.