Long coronavirus disease (COVID) is a condition in which coronavirus disease 2019 (COVID-19) symptoms persist for over 3 months, and currently poses a global public health challenge. Due to varying manifestations and lack of standardized definitions, diagnostic methods, and treatments, comprehensive clinical guidelines are required. This review article, summarizing research and expert consensus up to June 2023, provides recommendations for diagnosis and long-term management of long COVID symptoms. It emphasizes thorough patient evaluation, including medical history, physical examinations, and tests, and advocates vaccination and antiviral treatments to reduce risk. Guidelines for long COVID will be updated as new knowledge emerges.

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: As the demand for community mental health services continues to grow, the need for well-equipped and organized services has become apparent. This study aimed to optimize the roles and infrastructure of mental health services, by establishing, among other initiatives, standardized operating models. Methods: The study was conducted in multiple phases from May 12, 2021, to December 29, 2021. Stakeholders within South Korea and metropolitan mental health welfare centers were targeted, but addiction management support centers, including officials, patients, and their families, were integrated as well. A literature review and survey, focus group interviews, a Delphi survey, and expert consultation contributed to comprehensive revisions and improvements of the mental health service model. Results: The proposed model for community mental health welfare centers emphasizes the expansion of personnel and infrastructure, with a focus on severe mental illnesses and suicide prevention. The model for metropolitan mental health welfare centers delineates essential tasks in areas such as project planning and establishment, community research, and education about severe mental illnesses. The establishment of a 24-hour emergency intervention center was a crucial feature. In the integrated addiction support center model, the need to promote addiction management is defined as an essential task and the establishment of national governance for addiction policies is recommended. Conclusion This study proposed standard operating models for three types of mental health service centers. To meet the increasing need for community care, robust mental health service delivery systems are of primary importance.

The prevalence of prediabetes has steadily increased in Korea, driven by rising obesity rates and lifestyle changes. A structured lifestyle modification program has been shown to significantly reduce the prevalence of diabetes among adults with prediabetes, with this effect sustained over time. Lifestyle modifications including medical nutrition, exercise, and behavioral psychological therapy are effective for preventing and managing diabetes, with medical nutrition therapy playing a crucial role. This study reviews the latest academic guidelines and medical nutrition therapy for diabetes prevention and to introduce the KDPS-hLSM (Korean Diabetes Prevention Study hospital-based lifestyle modification).

Gestational diabetes mellitus (GDM) affects 2~10% of pregnant women worldwide, with an increasing prevalence in South Korea that reached 18.2% in 2021. Unmanaged GDM can lead to serious complications for both mother and fetus. Effective management requires blood glucose monitoring and lifestyle changes, but many women lack sufficient knowledge about the condition. This study explores the use of smart platforms, like continuous glucose monitoring devices and mobile apps, to improve GDM education. Devices such as Dexcom G7 and FreeStyle Libre 2 enable real-time blood glucose monitoring, while apps like Health2Sync help patients share data with healthcare providers. Although these technologies offer major benefits, challenges remain in patient engagement, data accuracy, and accessibility. In conclusion, while smart platforms can enhance GDM management, they must be combined with ongoing professional support to improve health outcomes.