Background/Aims: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent nature. This study aims to develop reliable artificial intelligence (AI) algorithms to detect early signs of AF in patients with normal sinus rhythm (NSR) using a 12-lead electrocardiogram (ECG). Methods: Between 2013 and 2020, 552,372 ECG traces from 318,321 patients were collected and split into training (n = 331,422), validation (n = 110,475), and test sets (n = 110,475). Deep neural networks were then trained to predict AF onset within one month of NSR. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). An explainable AI technique was employed to identify the inference evidence underlying the predictions of deep learning models. Results: The AUROC for early diagnosis of PAF was 0.905 ± 0.007. The findings reveal that the vicinity of the T wave, including the ST segment and S-peak, significantly influences the ability of the trained neural network to diagnose PAF. Additionally, comparing the summarized ECG in NSR with those in PAF revealed that nonspecific ST-T abnormalities and inverted T waves were associated with PAF. Conclusions Deep learning can predict AF onset from NSR while detecting key features that influence decisions. This suggests that identifying undetected AF may serve as a predictive tool for PAF screening, offering valuable insights into cardiac dysfunction and stroke risk.

Background/Aims: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent nature. This study aims to develop reliable artificial intelligence (AI) algorithms to detect early signs of AF in patients with normal sinus rhythm (NSR) using a 12-lead electrocardiogram (ECG). Methods: Between 2013 and 2020, 552,372 ECG traces from 318,321 patients were collected and split into training (n = 331,422), validation (n = 110,475), and test sets (n = 110,475). Deep neural networks were then trained to predict AF onset within one month of NSR. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). An explainable AI technique was employed to identify the inference evidence underlying the predictions of deep learning models. Results: The AUROC for early diagnosis of PAF was 0.905 ± 0.007. The findings reveal that the vicinity of the T wave, including the ST segment and S-peak, significantly influences the ability of the trained neural network to diagnose PAF. Additionally, comparing the summarized ECG in NSR with those in PAF revealed that nonspecific ST-T abnormalities and inverted T waves were associated with PAF. Conclusions Deep learning can predict AF onset from NSR while detecting key features that influence decisions. This suggests that identifying undetected AF may serve as a predictive tool for PAF screening, offering valuable insights into cardiac dysfunction and stroke risk.

Platelet-derived growth factors (PDGFs) ligands and their corresponding receptors, PDGF receptor (PDGFR)α and PDGFRβ, play a crucial role in controlling diverse biological functions, including cell growth, viability and migration. These growth factors bind to PDGFRs, which are receptor tyrosine kinases present on the surface of target cells. The interaction between PDGFs and PDGFRs induces receptor dimerization and subsequent activation through auto-phosphorylation, which in turn triggers a cascade of intracellular signaling pathways. PDGF/PDGFR signaling is essential for maintaining normal physiological functions, including tissue regeneration and growth. However, dysregulation of this signaling pathway leads to pathological conditions, including fibrosis, atherosclerosis, and cancer development in various organs. The pathological impact of PDGF/PDGFR signaling primarily stems from its capacity to promote excessive cell proliferation, enhanced migration, and increased extracellular matrix deposition, resulting in tissue overgrowth, scarring, and abnormal vessel formation. These processes are integral to the pathogenesis of fibrotic, neoplastic, and vascular disorders. Therefore, understanding these pathways is crucial for developing targeted treatments designed to inhibit PDGF/PDGFR signaling in these diseases. This review delves into the dual role of PDGF/PDGFR signaling in both physiological and pathophysiological contexts across different organs and provides insights into current pharmacological therapies designed to target the PDGF signaling pathway.

Background and Objectives: SH3 and multiple ankyrin repeat domains 3 (Shank3) proteins play crucial roles as neuronal postsynaptic scaffolds. Alongside neuropsychiatric symptoms, individuals with SHANK3 mutations often exhibit symptoms related to dysfunctions in other organs, including the heart. However, detailed insights into the cardiac functions of Shank3 remain limited. This study aimed to characterize the cardiac phenotypes of Shank3-overexpressing transgenic mice and explore the underlying mechanisms. Methods: Cardiac histological analysis, electrocardiogram and echocardiogram recordings were conducted on Shank3-overexpressing transgenic mice. Electrophysiological properties, including action potentials and L-type Ca2+ channel (LTCC) currents, were measured in isolated cardiomyocytes. Ca2+ homeostasis was assessed by analyzing cytosolic Ca2+transients and sarcoplasmic reticulum Ca2+ contents. Depolarization-induced cell shortening was examined in cardiomyocytes. Immunoprecipitation followed by mass spectrometrybased identification was employed to identify proteins in the cardiac Shank3 interactome.Western blot and immunocytochemical analyses were conducted to identify changes in protein expression in Shank3-overexpressing transgenic cardiomyocytes. Results: The hearts of Shank3-overexpressing transgenic mice displayed reduced weight and increased fibrosis. In vivo, sudden cardiac death, arrhythmia, and contractility impairments were identified. Shank3-overexpressing transgenic cardiomyocytes showed prolonged action potential duration and increased LTCC current density. Cytosolic Ca2+ transients were increased with prolonged decay time, while sarcoplasmic reticulum Ca2+ contents remained normal. Cell shortening was augmented in Shank3-overexpressing transgenic cardiomyocytes. The cardiac Shank3 interactome comprised 78 proteins with various functions. Troponin I levels were down-regulated in Shank3-overexpressing transgenic cardiomyocytes. Conclusions This study revealed cardiac dysfunction in Shank3-overexpressing transgenic mice, potentially attributed to changes in Ca2+ homeostasis and contraction, with a notable reduction in troponin I.

Background and Objectives: The Fractional Flow Reserve and Intravascular UltrasoundGuided Intervention Strategy for Clinical Outcomes in Patients with Intermediate Stenosis (FLAVOUR) trial demonstrated non-inferiority of fractional flow reserve (FFR)-guided percutaneous coronary intervention (PCI) compared with intravascular ultrasound (IVUS)-guided PCI. We sought to investigate the cost-effectiveness of FFR-guided PCI compared to IVUS-guided PCI in Korea. Methods: A 2-part cost-effectiveness model, composed of a short-term decision tree model and a long-term Markov model, was developed for patients who underwent PCI to treat intermediate stenosis (40% to 70% stenosis by visual estimation on coronary angiography).The lifetime healthcare costs and quality-adjusted life-years (QALYs) were estimated from the healthcare system perspective. Transition probabilities were mainly referred from the FLAVOUR trial, and healthcare costs were mainly obtained through analysis of Korean National Health Insurance claims data. Health utilities were mainly obtained from the Seattle Angina Questionnaire responses of FLAVOUR trial participants mapped to EQ-5D. Results: From the Korean healthcare system perspective, the base-case analysis showed that FFR-guided PCI was 2,451 U.S. dollar lower in lifetime healthcare costs and 0.178 higher in QALYs compared to IVUS-guided PCI. FFR-guided PCI remained more likely to be cost-effective over a wide range of willingness-to-pay thresholds in the probabilistic sensitivity analysis. Conclusions Based on the results from the FLAVOUR trial, FFR-guided PCI is projected to decrease lifetime healthcare costs and increase QALYs compared with IVUS-guided PCI in intermediate coronary lesion, and it is a dominant strategy in Korea.

Spontaneous coronary artery dissection (SCAD) is a condition in which an intramural hematoma within the coronary artery leads to acute coronary syndrome without atherosclerosis, trauma, or iatrogenic causes. It predominantly affects middle-aged women and is associated with several predisposing conditions, including fibromuscular dysplasia, systemic inflammatory disorders, connective tissue diseases, and coronary artery spasms. We report the case of a 64-year-old male with a history of hypertension who died of SCAD. His death occurred suddenly and without preceding trauma while the decedent was working overtime at a construction site. On gross examination, a thrombus-like material was identified in a branch of the left anterior descending artery and was initially presumed to be a postmortem clot. However, microscopic examination revealed an intramural hemorrhage, medial dissection, and formation of a false lumen within the coronary artery. This case report highlights the importance of a thorough histopathological examination of the coronary arteries during autopsy, even in the absence of atherosclerosis.

Background: and Purpose: This clinical practice guideline provides evidence-based recommendations for treatment of dementia, focusing on cholinesterase inhibitors and N-methyl-D-aspartate (NMDA) receptor antagonists for Alzheimer’s disease (AD) and other types of dementia. Methods: Using the Population, Intervention, Comparison, Outcomes (PICO) framework, we developed key clinical questions and conducted systematic literature reviews. A multidisciplinary panel of experts, organized by the Korean Dementia Association, evaluated randomized controlled trials and observational studies. Recommendations were graded for evidence quality and strength using Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology. Results: Three main recommendations are presented: (1) For AD, cholinesterase inhibitors (donepezil, rivastigmine, galantamine) are strongly recommended for improving cognition and daily function based on moderate evidence; (2) Cholinesterase inhibitors are conditionally recommended for vascular dementia and Parkinson’s disease dementia, with a strong recommendation for Lewy body dementia; (3) For moderate to severe AD, NMDA receptor antagonist (memantine) is strongly recommended, demonstrating significant cognitive and functional improvements. Both drug classes showed favorable safety profiles with manageable side effects. Conclusions This guideline offers standardized, evidence-based pharmacologic recommendations for dementia management, with specific guidance on cholinesterase inhibitors and NMDA receptor antagonists. It aims to support clinical decision-making and improve patient outcomes in dementia care. Further updates will address emerging treatments, including amyloid-targeting therapies, to reflect advances in dementia management.

Platelet-derived growth factors (PDGFs) ligands and their corresponding receptors, PDGF receptor (PDGFR)α and PDGFRβ, play a crucial role in controlling diverse biological functions, including cell growth, viability and migration. These growth factors bind to PDGFRs, which are receptor tyrosine kinases present on the surface of target cells. The interaction between PDGFs and PDGFRs induces receptor dimerization and subsequent activation through auto-phosphorylation, which in turn triggers a cascade of intracellular signaling pathways. PDGF/PDGFR signaling is essential for maintaining normal physiological functions, including tissue regeneration and growth. However, dysregulation of this signaling pathway leads to pathological conditions, including fibrosis, atherosclerosis, and cancer development in various organs. The pathological impact of PDGF/PDGFR signaling primarily stems from its capacity to promote excessive cell proliferation, enhanced migration, and increased extracellular matrix deposition, resulting in tissue overgrowth, scarring, and abnormal vessel formation. These processes are integral to the pathogenesis of fibrotic, neoplastic, and vascular disorders. Therefore, understanding these pathways is crucial for developing targeted treatments designed to inhibit PDGF/PDGFR signaling in these diseases. This review delves into the dual role of PDGF/PDGFR signaling in both physiological and pathophysiological contexts across different organs and provides insights into current pharmacological therapies designed to target the PDGF signaling pathway.

Background/Aims: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent nature. This study aims to develop reliable artificial intelligence (AI) algorithms to detect early signs of AF in patients with normal sinus rhythm (NSR) using a 12-lead electrocardiogram (ECG). Methods: Between 2013 and 2020, 552,372 ECG traces from 318,321 patients were collected and split into training (n = 331,422), validation (n = 110,475), and test sets (n = 110,475). Deep neural networks were then trained to predict AF onset within one month of NSR. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). An explainable AI technique was employed to identify the inference evidence underlying the predictions of deep learning models. Results: The AUROC for early diagnosis of PAF was 0.905 ± 0.007. The findings reveal that the vicinity of the T wave, including the ST segment and S-peak, significantly influences the ability of the trained neural network to diagnose PAF. Additionally, comparing the summarized ECG in NSR with those in PAF revealed that nonspecific ST-T abnormalities and inverted T waves were associated with PAF. Conclusions Deep learning can predict AF onset from NSR while detecting key features that influence decisions. This suggests that identifying undetected AF may serve as a predictive tool for PAF screening, offering valuable insights into cardiac dysfunction and stroke risk.