Background: We hypothesized that high-resolution activation mapping during sinus rhythm (SR) in Koch's triangle (KT) can be used to describe the most delayed atrial potential around the atrioventricular node and evaluated whether ablation targeting of this potential is safe and effective for the treatment of patients with typical atrioventricular nodal reentrant tachycardia (AVNRT).
Methods: We conducted a prospective, non-randomized, observational study using high-resolution activation mapping from the sinus node to KT with a PENTARAY or OCTARAY catheter using the CARTO 3 cardiac mapping system (Biosense Webster) during SR in 62 consecutive patients (22 men; age [mean ± standard deviation] = 55 ± 14 years) treated for typical AVNRT at our institution from August 2021 to March 2023.
Results: In all cases, the most delayed atrial potential was observed near the His potential within KT. Ablation targeting of this potential helped successfully treat each case of AVNRT, with a junctional rhythm observed at the ablation site. Initial ablation was deemed successful in 55/62 patients (89%); in the remaining seven patients, lesion expansion resolved AVNRT. One procedural complication occurred, namely, a transient atrioventricular block lasting 45 s. One patient experienced a transient tachycardic episode by the 1-month follow-up, but no further episodes were noted up to the 1-year follow-up.
Conclusion: Activation mapping at KT during SR with the high-resolution CARTO system clearly revealed the most delayed atrial potential near the His potential within KT. Targeting this potential was a safe and effective treatment method for patients with typical AVNRT in our study.
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http://dx.doi.org/10.1007/s10840-023-01688-5 | DOI Listing |
J Chem Theory Comput
January 2025
Preferred Networks, Inc., Tokyo 100-0004, Japan.
Mapping the chemical reaction pathways and their corresponding activation barriers is a significant challenge in molecular simulation. Given the inherent complexities of 3D atomic geometries, even generating an initial guess of these paths can be difficult for humans. This paper presents an innovative approach that utilizes neural networks to generate initial guesses for reaction pathways based on the initial state and learning from a database of low-energy transition paths.
View Article and Find Full Text PDFImplement Sci Commun
January 2025
Center for Health Equity Research, School of Medicine, University of North Carolina at Chapel Hill, 333 South Columbia Street, MacNider Hall Ste 323, Chapel Hill, NC, 27599, USA.
Background: African Americans experience cardiovascular disease (CVD) disparities, and the burden is greatest in the rural south. Although evidence-based CVD prevention and management programs have been tailored to this context, implementation has been limited and not sustained long-term. To understand how to implement and sustain evidence-based CVD programs at scale, we must explore the perspectives of organizations serving rural African American communities and situate findings within foundational Implementation Science frameworks.
View Article and Find Full Text PDFBackground: Immunization clinics present an opportunity for passive screening for malnutrition among young children through plotting of growth charts. Passive screening for malnutrition can enable timely interventions and improve morbidity and mortality of under-five children. Therefore, we aimed to increase the plotting of growth charts (weight-for-age) to 90%, among under-five children attending immunization clinics in an Urban Health Centre (UHC) in south Delhi over three months.
View Article and Find Full Text PDFNat Commun
January 2025
The Faculty of Data and Decisions Sciences, Technion - Israel Institute of Technology, Haifa, Israel.
Large Language Models (LLMs) have shown success in predicting neural signals associated with narrative processing, but their approach to integrating context over large timescales differs fundamentally from that of the human brain. In this study, we show how the brain, unlike LLMs that process large text windows in parallel, integrates short-term and long-term contextual information through an incremental mechanism. Using fMRI data from 219 participants listening to spoken narratives, we first demonstrate that LLMs predict brain activity effectively only when using short contextual windows of up to a few dozen words.
View Article and Find Full Text PDFTransl Psychiatry
January 2025
Department of Psychology, Goldsmiths University of London, London, UK.
Bipolar disorder (BD) involves altered reward processing and decision-making, with inconsistencies across studies. Here, we integrated hierarchical Bayesian modelling with magnetoencephalography (MEG) to characterise maladaptive belief updating in this condition. First, we determined if previously reported increased learning rates in BD stem from a heightened expectation of environmental changes.
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