Background and Objectives: Heart failure (HF) imposes a significant global burden, yet awareness and implementation of guideline-directed management remain suboptimal. This study evaluated HF awareness, diagnostic patterns, and therapeutic strategies among physicians in Korea. Methods: A nationwide online survey was conducted from July to August 2019, targeting primary care physicians (PCPs; n=8,000) and hospital-based cardiology specialists (n=1,339) across Korea. The questionnaire comprised 23 items covering baseline characteristics, perceptions of HF prognosis, diagnostic approaches, and treatment practices. Results: A total of 543 physicians responded (209 PCPs and 334 cardiology specialists). While the majority correctly defined HF, a substantial proportion underestimated the lifetime risk and poor prognosis. Cardiology specialists were more likely than PCPs to use objective diagnostic tools, including echocardiography (97% vs. 61%, p<0.001) and natriuretic peptide testing (90% vs. 57%, p<0.001). Among PCPs, 29% reported diagnosing HF based on symptoms and physical findings without objective testing, and 27% reported referring patients to general hospitals for diagnostic evaluation. Prescription rates for guideline-directed medical therapies, including renin-angiotensin system blockers and beta-blockers, were consistently higher among cardiology specialists.PCPs more frequently cited advanced age as a barrier to prescribing evidence-based medications. Conclusions Physicians’ awareness and clinical practices regarding HF in Korea remain suboptimal. Given the growing burden, high mortality, and substantial costs of HF, early detection, appropriate diagnostic evaluation, and timely referral are essential. Targeted education and system-level strategies to improve guideline implementation, particularly in primary care settings, are warranted.

In patients with differentiated thyroid cancer (DTC), initial recurrence risk stratification based on clinical, histopathological, and perioperative data remains the key determinant for guiding management strategies during the first 1-2 years post-treatment. However, the adoption of ongoing risk stratification (ORS), which dynamically reassesses risk by integrating longitudinal clinical data and treatment response, enables more precise long-term prognostic assessment and facilitates highly individualized management. Building upon recent guidelines, the 2026 KTA guideline has been further refined by incorporating robust evidence from large-scale national cohorts and comprehensive systematic reviews. These updated recommendations outline contemporary concepts of ORS, risk-adapted TSH suppression targets, optimized surveillance modalities for recurrence detection, and disease-specific long-term follow-up strategies. Reflecting the paradigm shift toward de-escalated treatment, this revision integrates evolved perspectives on TSH suppression intensity, the clinical interpretation of thyroglobulin levels, and tailored follow-up intervals. These evidence-based recommendations aim to minimize unnecessary treatment and excessive surveillance in the large proportion of patients with excellent prognosis after initial therapy, while ensuring that each patient receives appropriately tailored and effective long-term management.

Japanese encephalitis virus (JEV) is a zoonotic arbovirus transmitted by mosquitoes and poses a significant public health concern. JEV is classified into 5 genotypes (G1–G5). Since 2010, JEV genotype 5 (G5) has been predominant in the Republic of Korea; however, a specific antigen for hemagglutination (HA) inhibition (HI) assays has been lacking. This study aimed to develop a novel antigen to enable genotype-specific HI antibody detection for JEV G5. The Sangju strain of JEV G5 was serially passaged 30 times in Vero and C6/36 cells, resulting in Sangju-30, which exhibited 2 envelope protein mutations and an HA titer of 8, increasing to 512 following polyethylene glycol 8000 concentration. The antigen demonstrated a strong correlation (R = 0.95, p < 0.05, 95% confidence interval, 6.3–31.9) with HI antibody levels in pig sera. These findings suggest that the Sangju-30 antigen is a viable alternative for JEV G5 serological monitoring, enhancing diagnostic accuracy and offering a replacement for traditional murine-derived antigen production.

Background: The question of whether large language models (LLMs) possess consciousness has been increasingly debated. Integrated information theory (IIT) offers a quantitative framework for assessing consciousness through a measure of integrated information. Methods: This study applied IIT principles to the architecture of transformer-based LLMs, focusing on causal integration, temporal persistence, and system irreducibility. Ablation experiments on Generative Pretrained Transformer 2 (GPT-2) were performed, selectively removing individual attention heads and measuring changes in perplexity as a behavioral proxy for integrated information to empirically approximate the measure of integrated information. Results: The ablation study of a single attention head produced minimal or negative changes in perplexity in four out of five representative sentences, indicating redundancy or noise. Only one sentence revealed a significant increase in perplexity change (ΔPPL +11.29), reflecting a localized but nonessential contribution. A comparison with biological systems demonstrated that LLMs meet the IIT criterion of differentiation, but fail to meet the criteria of integration, causal closure, and temporal persistence. These findings confirm that LLMs are architecturally decomposable, lack persistent internal states, and do not sustain global causal irreducibility. Philosophical considerations, including Searle’s Chinese Room argument, further support the idea that the linguistic fluency of LLMs arises from syntactic manipulation rather than semantic understanding. Conclusion Current LLMs do not satisfy the structural and informational requirements of consciousness under IIT. Although capable of simulating intelligent language, LLMs remain unconscious systems with a negligible amount of integrated information, underscoring the distinction between linguistic competence and conscious experience.

Spinocerebellar ataxia type 3 (SCA3) is an autosomal-dominant neurodegenerative disorder caused by an expanded polyglutamine repeat in the ataxin-3 gene. The resulting mutant ataxin-3 protein forms intraneuronal inclusions that lead to neurodegeneration in the cerebellum and other brain regions. This study aimed to develop a novel nonhuman primate model of SCA3 to address the limitations of existing knock-in and transgenic models using an adeno-associated virus (AAV) to deliver the mutant gene. AAV viral vectors carrying mutant ataxin-3 were stereotaxically injected into the cerebellum of monkeys. The animals were monitored over an 8-week period, during which behavioral and neuroimaging assessments were conducted. This was followed by a detailed pathological examination. The AAV vector successfully spread throughout the cerebellum, and the expression of mutant ataxin-3 was confirmed. Neuroimaging revealed a reduction in N-acetylaspartate (NAA) levels, whereas histological analysis showed significant damage to the Purkinje cell layer. Notably, the monkeys exhibited sleep disturbances, a prodromal symptom commonly observed in human patients with SCA3. AAV-mediated delivery of mutant ataxin-3 can effectively replicate the key pathological and clinical features of SCA3 in primates. This approach offers a promising new model for studying disease mechanisms and evaluating potential therapies.

The increasing detection of papillary thyroid microcarcinoma (PTMC) has raised concerns about overtreatment.For low-risk PTMC, either immediate surgery or active surveillance (AS) can be considered. To support AS implementation, the Korean Thyroid Association convened a multidisciplinary panel and developed the first Korean guideline. AS is recommended to adults with pathologically proven Bethesda V-VI PTMC without clinical evidence of lymph node or distant metastasis, gross extrathyroidal extension, tracheal or recurrent laryngeal nerve invasion, or aggressive histology. Baseline assessment requires high‑resolution cervical ultrasound by experienced operators to rule out extrathyroidal extension, tracheal or recurrent laryngeal nerve invasion, and lymph node metastasis;contrast‑enhanced neck computed tomography is optional. Patient characteristics such as age, comorbidities, and capacity for long-term follow-up should be assessed. Shared decision-making should weigh the benefits and risks of surgery and AS, expected oncologic outcomes, complications, quality of life, anxiety, medical cost, and patient preference. Follow-up includes cervical ultrasound and thyroid function test every six months for two years, then annually. Disease progression, defined as significant tumor growth or newly detected nodal or distant metastasis, warrants surgery. Despite remaining uncertainties, this guideline offers a framework to ensure oncologic safety and support patient-centered active surveillance.

Purpose: This study explores the potential of artificial intelligence (AI) in optimizing radiotherapy protocols for personalized cancer treatment. Specifically, it investigates the role of AI-based segmentation tools in improving accuracy and efficiency across various anatomical regions. Methods: A dataset of 500 anonymized patient computed tomography scans from Chungbuk National University Hospital was used to develop and validate AI models for segmenting organs-atrisk. The models were tailored for five anatomical regions: head and neck, chest, abdomen, breast, and pelvis. Performance was evaluated using Dice Similarity Coefficient (DSC), Mean Surface Distance, and the 95th Percentile Hausdorff Distance (HD95). Results: The AI models achieved high segmentation accuracy for large, well-defined structures such as the brain, lungs, and liver, with DSC values exceeding 0.95 in many cases. However, challenges were observed for smaller or complex structures, including the optic chiasm and rectum, with instances of segmentation failure and infinity values for HD95. These findings highlight the variability in performance depending on anatomical complexity and structure size. Conclusions AI-based segmentation tools demonstrate significant potential to streamline radiotherapy workflows, reduce inter-observer variability, and enhance treatment accuracy. Despite challenges with smaller structures, the integration of AI enables dynamic, patient-specific adaptations to anatomical changes, contributing to more precise and effective cancer treatments.Future work should focus on refining models for anatomically complex structures and validating these methods in diverse clinical settings.