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.

Objectives: Large language models (LLMs) demonstrate remarkable potential in healthcare communication. However, whether they can process complex, high-volume medical information, such as stroke-related content, remains insufficiently validated. This study aimed to evaluate the natural language processing capabilities of LLMs in handling such content and to develop an evaluation instrument. Methods: A survey compared educational materials generated by two LLMs (ChatGPT 4.0 and Claude 3) with neurologist-authored content on stroke. The materials were based on two clinical scenarios representing distinct stroke etiologies: cardioembolism and large-artery atherosclerosis. They were evaluated in terms of accuracy, legality, ethics, comprehensiveness, and information delivery. Scores for comprehensiveness and information delivery were compared according to participants’ agreement with the use of LLMs in healthcare. Results: ChatGPT received the highest scores across all domains, except for legality in Scenario 2. In Scenario 1, the ranking for accuracy and summarization of clinical information was, from highest to lowest, ChatGPT, Claude, and the neurologist (η2 = 0.140, p < 0.001; η2 = 0.175, p < 0.001). The same hierarchy was observed in Scenario 2 for accuracy (η2 = 0.077, p < 0.001) and summarization (η2 = 0.194, p < 0.001). Participants who agreed with the use of LLMs in healthcare assigned higher scores for the comprehensiveness (Scenario 1, p = 0.005; Scenario 2, p = 0.007) and information delivery (Scenario 1, p = 0.003; Scenario 2, p = 0.026) of ChatGPT-generated materials than participants who did not agree. Conclusions LLMs demonstrated adequate capability to convey complex content, such as stroke-related information, in an accessible and understandable manner for non-experts.

RESULTS: Resveratrol significantly reduced cardiac hypertrophy and remodeling in aging hearts. In addition, resveratrol significantly ameliorated aging-induced mitochondrial dysfunction (e.g., decreased oxygen respiration and increased hydrogen peroxide emission) and mitochondria-dependent apoptotic signaling (the Bax/Bcl-2 ratio, mitochondrial permeability transition pore opening sensitivity, and cleaved caspase-3 protein levels).Resveratrol also significantly attenuated aging-induced apoptosis (determined via cleaved caspase-3 staining and TUNEL-positive myonuclei) in cardiac muscles. CONCLUSION This study demonstrates that resveratrol treatment has a beneficial effect on aging-induced cardiac remodeling by ameliorating mitochondrial dysfunction and inhibiting mitochondria-mediated apoptosis in the heart.

Axial spondyloarthritis (axSpA) is a chronic inflammatory disorder affecting the sacroiliac joints and axial spine. Along with pharmacotherapy, non-pharmacological interventions for axSpA are crucial and constitute the cornerstone of treatment. Here, we review the evidence for non-pharmacological treatment of axSpA as a basis for the 2023 Korean treatment recommendations for patients with axSpA. The effectiveness of the core non-pharmacological approaches, such as education, smoking cessation, and exercise, has been reaffirmed. High-quality research on surgical treatment is limited. However, total hip replacement is advised in patients with ongoing pain or disability and visible structural damage to the hip on imaging. Urgent spinal intervention should be considered in cases of acute spinal pain with neurological deficiency or concurrent unstable fractures. Evidence for complementary therapies, including spas and acupuncture, remains insufficient.

Axial spondyloarthritis (axSpA) is a chronic inflammatory disorder affecting the sacroiliac joints and axial spine. Along with pharmacotherapy, non-pharmacological interventions for axSpA are crucial and constitute the cornerstone of treatment. Here, we review the evidence for non-pharmacological treatment of axSpA as a basis for the 2023 Korean treatment recommendations for patients with axSpA. The effectiveness of the core non-pharmacological approaches, such as education, smoking cessation, and exercise, has been reaffirmed. High-quality research on surgical treatment is limited. However, total hip replacement is advised in patients with ongoing pain or disability and visible structural damage to the hip on imaging. Urgent spinal intervention should be considered in cases of acute spinal pain with neurological deficiency or concurrent unstable fractures. Evidence for complementary therapies, including spas and acupuncture, remains insufficient.

Purpose: We investigated the results of endoscopic fenestration for deeply located intracranial cysts (DLICs), risk factors for reoperation, and symptom improvement. Materials and Methods: We included 51 patients with DLICs who underwent endoscopic fenestration between November 2006 and October 2022. The median age was 5±20 years (6 days–67 years), and 36 (70.6%) patients were aged <20 years. The male-tofemale ratio was 1.3:1. The ventriculoscope was used to fenestrate the cysts, which had diameters under 4.5 mm. The volume of DLICs was measured separately on serial magnetic resonance imaging, and the patients were followed up for 32±40 months. Results: The mean preoperative volume of DLICs was 63.5±87.4 cm, 3 , which decreased to 23.7±56.2 cm 3postoperatively, with a 45.4%±32.1% decrease rate in 32 months. All DLICs were approached appropriately, avoiding the eloquent areas. Overall, 39 (76.5%) patients showed symptom improvement after a single operation, which was preserved without recurrence, whereas 12 (23.5%) underwent a second operation [shunting (17.6%) or repeating the endoscopic fenestration (5.9%)] owing to symptom aggravation and recurrent cysts. Patients aged <12 months showed 7.4 times more re-operation rate (p>0.046) and 7.4 times less symptom improvement (p=0.038) compared to those with older age. Females showed 6.5 times more re-operation rate (p=0.037) and 7.1 times less symptom improvement (p=0.027) than males. No patients experienced complications such as cerebrospinal fluid leakage, postoperative hemorrhage, or infection. Conclusion Endoscopic surgery is feasible for the treatment of DLICs. Female sex and age <12 months are risk factors for re-operation and less symptom improvement.

Purpose: We investigated the results of endoscopic fenestration for deeply located intracranial cysts (DLICs), risk factors for reoperation, and symptom improvement. Materials and Methods: We included 51 patients with DLICs who underwent endoscopic fenestration between November 2006 and October 2022. The median age was 5±20 years (6 days–67 years), and 36 (70.6%) patients were aged <20 years. The male-tofemale ratio was 1.3:1. The ventriculoscope was used to fenestrate the cysts, which had diameters under 4.5 mm. The volume of DLICs was measured separately on serial magnetic resonance imaging, and the patients were followed up for 32±40 months. Results: The mean preoperative volume of DLICs was 63.5±87.4 cm, 3 , which decreased to 23.7±56.2 cm 3postoperatively, with a 45.4%±32.1% decrease rate in 32 months. All DLICs were approached appropriately, avoiding the eloquent areas. Overall, 39 (76.5%) patients showed symptom improvement after a single operation, which was preserved without recurrence, whereas 12 (23.5%) underwent a second operation [shunting (17.6%) or repeating the endoscopic fenestration (5.9%)] owing to symptom aggravation and recurrent cysts. Patients aged <12 months showed 7.4 times more re-operation rate (p>0.046) and 7.4 times less symptom improvement (p=0.038) compared to those with older age. Females showed 6.5 times more re-operation rate (p=0.037) and 7.1 times less symptom improvement (p=0.027) than males. No patients experienced complications such as cerebrospinal fluid leakage, postoperative hemorrhage, or infection. Conclusion Endoscopic surgery is feasible for the treatment of DLICs. Female sex and age <12 months are risk factors for re-operation and less symptom improvement.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.