Secondary rhabdoid tumors (RTs) with atypical teratoid/rhabdoid tumor-like features rarely arise from, or coexist with, pleomorphic xanthoastrocytomas (PXAs), and their clinicopathological and molecular characteristics remain poorly understood. We report a 17-year-old girl with a temporal lobe mass that, upon gross total resection, pathologically contained both RT and PXA components. Immunohistochemistry revealed loss of INI1 expression restricted to the RT component, while the PXA area retained INI1. Next-generation sequencing identified a shared BRAF::TRIM24 fusion and homozygous deletion of CDKN2A/2B in both components, indicating a shared clonal origin. Additionally, a germline ATM frameshift mutation (c.5288_5289insGA) was identified in both tumor components, making the first such report in central nervous system tumors. SMARCB1 loss was confined to the RT component, further supporting the hypotheses of clonal evolution and secondary transformation. Despite gross total resection, craniospinal irradiation, and chemotherapy, the patient developed rapid leptomeningeal dissemination and died 5 months after surgery. This case provides clinicopathological and molecular evidence for clonal evolution and secondary transformation of PXA into an RT. The presence of germline ATM mutation may have therapeutic and biological relevance. Further studies are required to clarify the pathogenesis and optimal management of these rare and aggressive tumors.

Background: and Purpose: Clinicians typically use 2 methods to determine impairment in a specific cognitive domain based on the Seoul Neuropsychological Screening Battery, 2nd Edition: (1) identifying impairment when one or more subtests fall below the normal range (< mean −1.5 standard deviation), and (2) using the cognitive domain score. Because agreement between these methods may differ by the severity of cognitive impairment, this study examined their concordance in the overall sample and across Clinical Dementia Rating (CDR) levels. Methods: A total of 1,086 patients (age 74.27±9.62 years; education 9.07±4.77 years) were included. Concordance between subtest-based and domain score-based classifications was assessed for each cognitive domain using cross-tabulation analyses and Cohen’s kappa statistics. To assess the influence of the severity of cognitive impairment, analyses were conducted across CDR levels. Results: When impairment was defined as having at least one abnormal subtest, concordance rates were 90.8% for Attention, 88.8% for Language, 84.6% for Visuospatial Function, 74.3% for Memory, and 73.6% for Frontal/Executive Function. In the Frontal/ Executive Function domain, requiring 2 or more abnormal subtests increased concordance to 90.9%. CDR subgroup analyses showed that Memory concordance was particularly low in the CDR 0.5 group (65.8%) compared with the CDR 1 (85.5%) and CDR 2 (98.5%) groups. Conclusions Concordance between the 2 classification methods was moderately high for Attention, Language, and Visuospatial Function but substantially lower for Memory and Frontal/Executive Function, especially in individuals with mild cognitive impairment. These findings highlight the need to consider both subtest-level performance and domain scores when determining impairment in memory or frontal/executive function.

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.

The maxillary anterior region is critical for esthetics and function, requiring precise analysis and restoration when damaged by trauma. This case report presents the restorative process using a digital workflow for a patient with anterior trauma. A 22-year-old female exhibited fractures and extrusive luxation of maxillary central incisors, diagnosed with pulp necrosis. After endodontic treatment, intraoral and facial scans were performed, and provisional and definitive restorations were digitally designed. Various shade-matching techniques were employed to achieve natural esthetics. The definitive prosthesis improved harmony between the incisal edge and lower lip, as well as tooth axis, length, and zenith position, fulfilling the patient’s esthetic demands. This case suggests that combining digital technology and gingivectomy with a minimally invasive approach enables esthetic and functional restoration of the maxillary anterior region.

The sixth edition of the Korean Medication Algorithm Project for Depressive Disorder (KMAP-DD) was published in 2025. This review compared KMAP-DD 2025 with four major international clinical practice guidelines: Canadian Network for Mood and Anxiety Treatments Clinical Guidelines for the Management of Major Depressive Disorders, National Institute for Health and Care Excellence Depression Guideline, Royal Australian and New Zealand College of Psychiatrists Clinical Practice Guidelines for Mood Disorders, and British Association for Psychopharmacology Guideline. While KMAP-DD is based on expert consensus, and others on evidence-based methods, overall treatment strategies for depressive episodes were fairly consistent. Especially, KMAP-DD 2025 offers more structured recommendations in areas lacking strong evidence, such as premenstrual dysphoric disorder, perinatal depression, and depression with medical comorbidities. KMAP-DD 2025 also reflected Korean clinical practice patterns emphasizing rapid symptom relief and early use of combination strategies. Despite limitations as a consensus-based guideline, KMAP-DD 2025 complements evidence-based approaches and provides practical, situation-specific guidance for real-world clinical decision-making in Korea.

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.

Background: Left bundle branch block (LBBB) significantly increases the risk of left ventricular systolic dysfunction (LVSD) due to cardiac dyssynchrony. Although artificial intelligence–enabled electrocardiography (AI-ECG) mod‑ els show promise in detecting LVSD, their performance in LBBB patients remains underexplored. We hypothesized that an AI-ECG model clinically validated for detecting LVSD would accurately detect LVSD and predict future clinical outcomes in LBBB patients. Methods: In this retrospective multicenter study, 5,689 expert-validated LBBB ECGs collected from 2,813 patients between 2016 and 2024 were analyzed using a previously developed and validated AI-ECG model. LVSD was defined as an ejection fraction of ≤ 40%. Model performance was assessed using the area under the receiver operating char‑ acteristic curve (AUROC), the area under the precision-recall curve (AUPRC), sensitivity, and specificity. Patients were stratified into high- and low-risk groups based on a threshold that achieved 90% sensitivity. A Kaplan–Meier analysis was used to compare clinical outcomes. Results: Among the 2,813 LBBB patients (mean age, 70.7 years; male sex, 43.7%), hypertension and a history of heart failure were common. The AiTiALVSD model showed strong diagnostic performance for LVSD (AUROC, 0.930 [95% CI, 0.924–0.937]; AUPRC, 0.913 [95% CI, 0.902–0.923]; sensitivity, 0.979; specificity, 0.473). During the mean follow-up of 4.1 years, high-risk patients had significantly higher hazards than low-risk patients for all-cause mortality (adjusted hazard ratio [HR], 1.87; 95% CI, 1.53–2.28), implantable cardioverter defibrillator/cardiac resynchronization therapy implantation (adjusted HR, 15.2; 95% CI, 7.51–30.77), and cardiovascular hospitalization (adjusted HR, 1.11; 95% CI, 0.96–1.28). Conclusions AiTiALVSD effectively detects LVSD and stratifies long-term cardiovascular risk in LBBB patients, support‑ ing its clinical utility for early detection and patient management.

Abrupt vessel closure (AVC) is a complication of percutaneous coronary intervention (PCI) and is defined as the sudden cessation of forward coronary blood flow due to a variety of mechanisms. The incidence of AVC has markedly decreased from approximately 3.0% in the early era to about 0.3% today. Nevertheless, cardiovascular interventionists must remain prepared to manage AVC effectively, given its significant impact on clinical outcomes.AVC can arise from several mechanisms, including dissection, intramural hematoma, intracoronary thrombosis, air embolism, vasospasm, and no-reflow. The clinical impact of AVC varies depending on the extent of myocardium affected, ranging from asymptomatic events or transient ischemic chest pain to cardiogenic shock or cardiac arrest. Both a general approach for stabilizing hemodynamic consequences and an etiology-specific management strategy are essential. Hasty decision-making may worsen the situation, whereas a structured approach—focused on hemodynamic stabilization, logical diagnostic processes, and targeted interventions—can improve outcomes. In this narrative review, we describe the mechanisms underlying each etiology, outline mechanism-specific management strategies, and discuss general approaches to managing patients with AVC through logical diagnostic and treatment processes. We hope this review offers valuable insights into this uncommon but significant complication and enhances readers’ skills in managing AVC more effectively during PCI.