This study examined the associations of fall experiences with cognitive function and activities of daily living (ADL) disability among older adults. Methods: Using data from the 2023 National Survey of Older Koreans, a total of 9,816 individuals aged 65 years or older were analyzed. Independent samples t-tests, ANOVA, chi-square tests, linear regression, and logistic regression analyses were conducted. Additionally, subgroup regression analyses were conducted to identify socially vulnerable groups according to age group, education level, and caregiving expenses. Results: The analysis showed that cognitive function tended to be lower in individuals with fall experience (β=-0.44, p=.026), and the odds of ADL disability were higher among individuals with fall experience (odds ratio [OR]=2.04, 95% confidence interval [95% CI]=1.63~2.54). In addition, subgroup analyses showed that cognitive function was lower among individuals with fall experience in the ≥85 years group (β=- 1.30, p=.020) and among those with education at elementary school or below (β=-0.88, p=.001). The odds of ADL disability among those with fall experience were higher among those aged 65~74 years (vs. 75~84; OR=2.80, 95% CI=1.99~3.94) and ≥85 years (vs. 75~84; OR=2.59, 95% CI=1.38~4.84), those with higher education (vs. lower; OR=4.95, 95% CI=1.19~20.60), and those with no caregiving expenses (vs. any; OR=2.06, 95% CI=1.63~2.60). Conclusion: These findings provide important foundational data for both policy development and academic research. They highlight the need for a multifaceted approach to fall prevention and underscore its importance in enhancing the quality of life for the older adults.

Purpose: Due to its rarity, treatment guidelines for appendiceal cancer have traditionally followed those established for colorectal cancer, despite showing distinct histologic and clinical features. This study aimed to compare the clinicopathologic characteristics and long-term oncologic outcomes of appendiceal cancer with those of right-sided colon cancers. Methods: We retrospectively reviewed the records of patients with stage I–III appendiceal, cecal, or ascending colon cancer who underwent curative resection between 2010 and 2020 at our center. A 1:3:3 exact matching for age, sex, TNM stage, and adjuvant chemotherapy was performed. Survival outcomes were analyzed using the Kaplan-Meier and Cox regression methods. Results: Overall, 245 patients with appendiceal cancer (n = 35), ascending colon cancer (n = 105), and cecal cancer (n = 105) were analyzed. Appendiceal cancer exhibited a higher proportion of T4 tumors and fewer harvested lymph nodes compared with ascending or cecal cancers. The mean follow-up duration was 9.5 years. The 5- and 10-year overall survival rates were lower in appendiceal cancer (66.2% and 52.9%) than in ascending (91.2% and 78.4%) or cecal cancer (88.5% and 78.3%). Similarly, the 10-year disease-free survival rate was lower in appendiceal cancer (59.2%) compared with ascending (83.1%) and cecal cancers (78.4%). Cox regression analysis identified age (≥65 years), perforation, nodal metastasis, and lymphovascular invasion as independent predictors of poor prognosis. Conclusion Appendiceal cancer exhibited significantly worse long-term survival compared to cecal or ascending colon cancer. Tumor perforation, nodal metastasis, and lymphovascular invasion were adverse prognostic factors for overall and disease-free survival.

Age-related macular degeneration (AMD) is a leading cause of blindness in people over 65 years old. Anti-vascular endothelial growth factor (VEGF) therapy is the first-line treatment for neovascular AMD (nAMD); however, fibrosis remains an unmet medical need due to the lack of effective medications. This study evaluated eight natural products from Celastrus orbiculatus, which has been reported to have anti-inflammatory effects, for their effects on the fibrotic pathological process as well as choroidal neovascularization (CNV). We investigated the half-maximal inhibitory concentration (IC 50) values of these compounds on VEGF and alphasmooth muscle actin (α-SMA, a fibrosis marker) expression-induced by human acute monocytic cell (THP-1) conditioned media in retinal pigment epithelium cells (ARPE-19). Four compounds reduced both VEGF and α-SMA at 10 μM, with three—Celafolin A-1, COFH5645, and COFH543435—showing the highest potency. Intravitreal injections of the compound in a mouse model of CNV induced by laser photocoagulation confirmed its efficacy. Celafolin A-1 significantly reduced α-SMA and VEGF expression and decreased hyper-reflective lesions and CNV areas. Binding affinity measurements using biolayer interferometry identified an interaction between Celafolin A-1 and crystallin alpha B (Cryab), a protein involved in stress responses and fibrosis. Celafolin A-1 reduced the expression levels of Cryab as well as its phosphorylated form at Ser45, indicating that its mechanism involves the regulation of Cryab phosphorylation. Taken together, Celafolin A-1 exhibits dual inhibitory effects on VEGF and fibrosis, suggesting it as a candidate for the treatment of nAMD.

A 43-year-old woman presented with bilateral lower extremity weakness due to an intradural extramedullary spinal cord tumor. Surgery revealed Ewing sarcoma, a rare presentation known as primary intradural extramedullary Ewing sarcoma (PIEES). Despite initial treatment with radiation and chemotherapy, tumor recurrence occurred after 17 months. Further interventions included additional surgery, radiation, and chemotherapy. The disease progressed to leptomeningeal metastases along the spinal cord, prompting various treatments including targeted spinal radiation and systemic therapies. Brain metastases subsequently developed, necessitating whole-brain radiation and intrathecal chemotherapy. This case highlights the aggressive nature of PIEES, its potential for widespread leptomeningeal metastasis, and the challenges in its management, underscoring the need for multidisciplinary approaches in treating this rare and aggressive malignancy.

Objective: Sleep disturbances are common and debilitating nonmotor symptoms (NMS) in Parkinson’s disease (PD) patients and profoundly affect quality of life. Despite emerging evidence suggesting that monoamine oxidase B (MAO-B) and catechol-O-methyltransferase (COMT) inhibitors may alleviate NMS, their specific effects on sleep remain unclear. The aim of this network meta-analysis (NMA) was to compare the efficacy of these inhibitors related to sleep problems in PD patients. Methods: Following a systematic search of PubMed, Cochrane, and Embase, studies comparing MAO-B or COMT inhibitors and assessing sleep outcomes in PD patients were identified. An NMA was conducted using data from the seven studies that met our inclusion criteria. The outcomes included subjective sleep quality, daytime sleepiness, and objective polysomnography (PSG) parameters. Results: No statistically significant differences were found between the effects of the MAO-B and COMT inhibitors on improving subjective sleep quality or daytime sleepiness. However, analyses of objective PSG data revealed that, compared with rasagiline and placebo, safinamide significantly increased rapid eye movement sleep duration (mean difference, 5.70 min [95% CI, 2.26 to 9.14]) and decreased wake time after sleep onset (mean difference, -10.20 min [-19.38 to -1.02]). Conclusion These findings suggest that safinamide may offer additional value for managing sleep disruptions beyond its known motor benefits in patients with PD. Given the limited number and small scale of available trials, the overall evidence should be interpreted cautiously. Nonetheless, this analysis highlights the need for future high-quality trials focused on sleep outcomes to guide the personalized use of MAO-B and COMT inhibitors for sleep disturbances in PD patients.

Vaccines are highly effective, but rare or delayed adverse events following immunization (AEFIs) require post-licensure surveillance beyond clinical trials. Korea lacks a comprehensive, active, database-based framework, yet key assets exist: nationwide claims databases (National Health Insurance Service/Health Insurance Review and Assessment Service), the national immunization registry (Korea Disease Control and Prevention Agency’s Immunization Registry Information System) for National Immunization Program (NIP) and non-NIP vaccines, and increasingly standardized hospital electronic health records.We propose a federated, code to data architecture with data linkages between these data.Implementation should adopt a common data model (CDM), standardized case definitions, latency accounting, and transparent public reporting under strong privacy governance. Major challenges include multi step administrative approvals for data linkage, incomplete capture of adult non-NIP vaccinations, heterogeneous hospital data structures, and strict data protection constraints. Strategic priorities are to streamline statutory and administrative processes for public health use, mandate or enable claims-based capture of adult vaccinations, enhance CDM based interoperability, and develop secure hubs for aggregated outputs. With these measures, Korea will be well positioned to establish a scalable active surveillance system capable of detecting rare AEFIs, supporting transparent and evidence-based communication, and ensuring equitable injury compensation grounded in domestic data.

Purpose: This study evaluated the clinical outcomes and prognostic factors in patients with borderline resectable pancreatic cancer (BRPC) and locally advanced pancreatic cancer (LAPC) treated with upfront FOLFIRINOX followed by local-regional therapy (LRT), surgical resection (SR), and radiotherapy (RT). We aimed to identify specific patient subgroups for which RT may serve as a reasonable alternative to SR for local tumor control. Methods: We retrospectively analyzed 116 patients (SR group, n= 70; RT group, n= 46) at a single center between 2015 and 2020. Survival outcomes were compared based on LRT modalities, focusing on identifying subgroups in which RT provided an efficacy comparable to that of SR. Results: Among 116 patients, the SR group achieved a significantly higher 5-year overall survival (OS) than the RT group (27.1% vs. 8.7%, P< 0.0001), despite similar progression-free survival (P= 0.23). Significant prognostic factors for OS included carbohydrate antigen 19-9 (CA19-9) response in BRPC (P= 0.02) and radiologic partial response in LAPC (P= 0.05). Subgroup analysis revealed that, while SR provided a survival advantage in CA19-9 responders, no significant difference in OS was observed between SR and RT in CA19-9 non-responders (P= 0.37). Conclusion Although surgery remains the gold standard, RT may be considered a justifiable local alternative for CA19-9 non-responders and surgically ineligible patients with LAPC, yielding comparable outcomes in these specific, biologically unfavorable subgroups.

Electroencephalography (EEG) is a non-invasive neurophysiological monitoring technique. It records the electrical activity of the cerebral cortex using electrodes placed on the scalp surface. Owing to its high safety, portability, and millisecond-level temporal resolution, EEG has been widely utilized in a variety of fields, including clinical diagnosis, brain-computer interfaces (BCIs), and cognitive neuroscience research. However, due to its microvolt-level amplitude, EEG is highly susceptible to various artifacts, including electrooculographic (EOG), electrocardiographic (ECG), electromyographic (EMG), and power line interference (PLI). These artifacts can obscure genuine neural activity and introduce spurious electrophysiological features. Consequently, they may compromise EEG signal quality, thereby reducing the reliability of downstream analyses. To address this issue, numerous EEG artifact removal methods have been developed, including both traditional denoising techniques and deep learning-based approaches. Traditional EEG denoising methods have long served as the primary solutions for artifact removal. Representative approaches include filtering, regression, and blind source separation. Although these methods have demonstrated effectiveness in specific scenarios, they suffer from several inherent limitations. Filtering assumes that artifacts and EEG signals can be separated in the frequency domain, but many artifacts, such as EOG and EMG, overlap with EEG spectra, which may lead to the loss of valuable neural information. Regression methods require high-quality artifact references to estimate and subtract contaminations, limiting their effectiveness in reference-free scenarios. Blind source separation can remove artifacts without external references, but it typically requires the number of EEG channels to exceed the number of sources, restricting its application in single- or low-channel EEG recordings. Deep learning-based EEG denoising methods address these limitations effectively. First, they learn the nonlinear mapping between contaminated and clean EEG directly from data in an end-to-end manner. This approach does not rely on assumptions about spectral separability, thereby preserving neural activity more completely. Second, the reference information is incorporated during the training phase, allowing the trained model to perform artifact removal independently without external references. Third, deep learning models can be flexibly designed to accommodate various recording setups, achieving robust denoising for both high-density and single-channel EEG. Collectively, these advantages enable deep learning-based methods to overcome the main challenges of traditional approaches, providing more accurate and reliable EEG signal recovery. The superior denoising performance of deep learning-based EEG denoising methods has attracted increasing attention in EEG artifact removal research. As a result, many deep learning-based denoising methods have been developed and successfully applied in neural engineering areas. However, a systematic review of the techniques and applications in this field is still lacking. To address this gap, this paper reviews recent advances in deep learning-based EEG denoising from four perspectives: technical principle, benchmark dataset, denoising model, and evaluation method. Representative applications in neural signal analysis and BCI decoding are also summarized. Furthermore, the advantage, existing challenge, and future research direction of deep learning-based EEG denoising are discussed. This review aims to provide valuable theoretical insights and technical guidance for researchers. It is also expected to promote further advances and broader applications of deep learning-based EEG denoising techniques.