BACKGROUND/OBJECTIVES: Obesity is a complex metabolic disorder characterized by excessive adipose tissue accumulation, dysregulated lipid metabolism, and insulin resistance, leading to an increased risk of metabolic disease. While Withania somnifera (AS) and Chrysanthemum zawadskii Herbich var. latilobum (Maxim.) Kitamura (C) have traditionally been used for their metabolic regulatory properties, their combined effects on adipogenesis, lipogenesis, lipolysis, and insulin signaling remain unexplored. Therefore, this study aimed to evaluate the anti-obesity effects of AS, C, and their optimal combination (ASC) in 3T3-L1 adipocytes by investigating their impact on lipid metabolism and glucose homeostasis.MATERIALS/METHODS: Following adipogenic differentiation, 3T3-L1 adipocytes were treated with AS, C, and ASC at different concentrations. RESULTS: AS, C, and ASC significantly inhibited adipogenesis by downregulating cyclic adenosine monophosphate (cAMP) response element-binding protein, peroxisome proliferator-activated receptor gamma, CCAAT/enhancer-binding proteins alpha and beta phosphorylation, thereby reducing lipid accumulation in adipocytes. They also suppressed lipogenesis by downregulating the expression of dephosphorylated acetyl-CoA carboxylase, fatty acid synthase, and lipoprotein lipase. In contrast, they markedly enhanced lipolysis, as evidenced by increased hormone-sensitive lipase and protein kinase A expression, along with elevated glycerol release and cAMP levels. Furthermore, AS, C, and ASC activated energy metabolism pathways, as indicated by the upregulation of AMP-activated protein kinase, uncoupling protein 1, and carnitine palmitoyltransferase 1A, suggesting a transition toward enhanced mitochondrial fatty acid oxidation. Notably, AS, C, and ASC significantly improved insulin signaling by restoring insulin receptor substrate 1, phosphoinositide 3-kinase, and Akt phosphorylation, while upregulating glucose transporter type 4 expression, indicating enhanced glucose uptake. Among all treatments, ASC showed relatively greater efficacy compared to AS or C alone, suggesting a potential additive effect. CONCLUSION These findings demonstrate that ASC effectively modulates multiple metabolic pathways in adipocytes, including adipogenesis, lipogenesis, lipolysis, energy metabolism, and insulin signaling, to exert its anti-obesity effects.

Alzheimer’s disease (AD) is characterized by progressive cognitive deterioration and significant depression. However, the mechanisms linking depression to AD pathology remain unclear. Here, we investigated whether Notch2 signaling mediates depressionlike behaviors in presenilin-2 (PS2) N141I mutant mice, an early-onset AD model. PS2 wild-type (WT) and mutant (MT) mice aged 12-15 months were subjected to unpredictable chronic mild stress (UCMS) for 4 weeks, followed by sucrose preference, tail-hanging, and forced swimming tests. Behavioral assessments showed that UCMS exacerbated anhedonia and immobility only in PS2 MT mice. Molecular analysis revealed concomitant increases in plasma corticosterone, hippocampal γ-secretase activity, and Notch2 expression, and elevated total and phosphorylated glucocorticoid receptor levels in PS2 MT-UCMS mice. Gene expression profiling of human hippocampal datasets confirmed upregulation of NOTCH2 in Alzheimer’s disease and depression.Pharmacological inhibition of γ-secretase and Notch signaling with DAPT normalizes depressive behavior, reduces corticosterone release, attenuates GR phosphorylation, and inhibits Notch2 signaling in PS2 MT mice. These findings identify Notch2 as a pivotal mediator linking chronic stress to molecular changes associated with depression and AD, and suggest that targeting Notch2 signaling may provide therapeutic benefits for comorbid mood and neurodegenerative disorders.

Objective: Suicide is a complex issue influenced by various factors, including mental illness, economic, and cultural elements. Mental and behavioral disorders are significant contributors to suicide risk, and individuals who attempt self-harm often present with comorbidities. This study aims to identify the significance of characteristics and comorbidities among hospitalized patients who engaged in self-harm and have been diagnosed with mental and behavioral disorders. Methods: We targeted patients aged 19 or older who attempted self-harm and were hospitalized from the 2022 Korea National Hospital Discharge In-depth Injury Survey covering the period from 2008 to 2021. After applying sampling weights, the estimated total sample size was 10,140. The analysis was conducted using a general linear model for complex samples, incorporating analysis of variance and regression analyses. Additionally, network analysis was used to explore relationships among comorbidities. Results: The incidence of self-harm varied seasonally, peaking in spring, with higher rates observed in winter. Hospitalization duration was significantly longer when surgical interventions were required or when comorbidities were present. The average length of hospitalization was 20.52 days, but patients with alcohol-related addictions had a significantly longer stay (71.57 days). For each additional comorbidity, the hospitalization duration increased by 1.889 days. About 46.78% of patients had one or more comorbidities, with strong associations between mental disorders (F00–F99) and cases of poisoning or external injuries (S00–T98). Conclusion This study underscores the importance of managing comorbidities in patients with mental illness to reduce the clinical and social costs of self-harm.

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: Suicide is a complex issue influenced by various factors, including mental illness, economic, and cultural elements. Mental and behavioral disorders are significant contributors to suicide risk, and individuals who attempt self-harm often present with comorbidities. This study aims to identify the significance of characteristics and comorbidities among hospitalized patients who engaged in self-harm and have been diagnosed with mental and behavioral disorders. Methods: We targeted patients aged 19 or older who attempted self-harm and were hospitalized from the 2022 Korea National Hospital Discharge In-depth Injury Survey covering the period from 2008 to 2021. After applying sampling weights, the estimated total sample size was 10,140. The analysis was conducted using a general linear model for complex samples, incorporating analysis of variance and regression analyses. Additionally, network analysis was used to explore relationships among comorbidities. Results: The incidence of self-harm varied seasonally, peaking in spring, with higher rates observed in winter. Hospitalization duration was significantly longer when surgical interventions were required or when comorbidities were present. The average length of hospitalization was 20.52 days, but patients with alcohol-related addictions had a significantly longer stay (71.57 days). For each additional comorbidity, the hospitalization duration increased by 1.889 days. About 46.78% of patients had one or more comorbidities, with strong associations between mental disorders (F00–F99) and cases of poisoning or external injuries (S00–T98). Conclusion This study underscores the importance of managing comorbidities in patients with mental illness to reduce the clinical and social costs of self-harm.

Purpose: Since 1995, the Korean Gastric Cancer Association (KGCA) has been periodically conducting nationwide surveys on patients with surgically treated gastric cancer. This study details the results of the survey conducted in 2023. Materials and Methods: The survey was conducted from March to December 2024 using a standardized case report form. Data were collected on 86 items, including patient demographics, tumor characteristics, surgical procedures, and surgical outcomes. The results of the 2023 survey were compared with those of previous surveys. Results: Data from 12,751 cases were collected from 66 institutions. The mean patient age was 64.6 years, and the proportion of patients aged ≥71 years increased from 9.1% in 1995 to 31.7% in 2023. The proportion of upper-third tumors slightly decreased to 16.8% compared to 20.9% in 2019. Early gastric cancer accounted for 63.1% of cases in 2023.Regarding operative procedures, a totally laparoscopic approach was most frequently applied (63.2%) in 2023, while robotic gastrectomy steadily increased to 9.5% from 2.1% in 2014.The most common anastomotic method was the Billroth II procedure (48.8%) after distal gastrectomy and double-tract reconstruction (51.9%) after proximal gastrectomy in 2023.However, the proportion of esophago-gastrostomy with anti-reflux procedures increased to 30.9%. The rates of post-operative mortality and overall complications were 1.0% and 15.3%, respectively. Conclusions The results of the 2023 nationwide survey demonstrate the current status of gastric cancer treatment in Korea. This information will provide a basis for future gastric cancer research.

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.