Symptomatic atrial fibrillation (AF) or clinical AF is associated with impaired quality of life, higher risk of stroke, heart failure, and increased mortality. Current clinical classification of AF is based on the duration of AF episodes and the recurrence over time. Appropriate management strategy should follow guidelines of Scientific Societies. The last decades have been marked by the advances in mechanism comprehension, better management of symptomatic AF, particularly regarding stroke prevention with the use of direct oral anticoagulants and a wider use of AF catheter or surgical ablations. The advent of new tools for detection of asymptomatic AF including continuous monitoring with implanted electronic devices and the use of implantable cardiac monitors and recently wearable devices or garments have identified what is called "subclinical AF" encompassing atrial high-rate episodes (AHREs). New concepts such as "AF burden" have resulted in new management challenges. Oral anticoagulation has proven to reduce substantially stroke risk in patients with symptomatic clinical AF but carries the risk of bleeding. Management of detected asymptomatic atrial arrhythmias and their relation to clinical AF and stroke risk is currently under evaluation. Based on a review of recent literature, the validity of current clinical classification has been reassessed and appropriate updates are proposed. Current evidence supporting the inclusion of subclinical AF within current clinical classification is discussed as well as the need for controlled trials which may provide responses to current therapeutic challenges particularly regarding the subsets of asymptomatic AF patients that might benefit from oral anticoagulation.
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http://dx.doi.org/10.1007/s10840-020-00859-y | DOI Listing |
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Departmental Unit of Molecular and Genomic Diagnostics, Genomics Core Facility, G-STeP, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
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Sci Rep
January 2025
Department of Computer Science and Engineering, E.G.S. Pillay Engineering College, Nagapattinam, 611002, Tamil Nadu, India.
In response to the pressing need for the detection of Monkeypox caused by the Monkeypox virus (MPXV), this study introduces the Enhanced Spatial-Awareness Capsule Network (ESACN), a Capsule Network architecture designed for the precise multi-class classification of dermatological images. Addressing the shortcomings of traditional Machine Learning and Deep Learning models, our ESACN model utilizes the dynamic routing and spatial hierarchy capabilities of CapsNets to differentiate complex patterns such as those seen in monkeypox, chickenpox, measles, and normal skin presentations. CapsNets' inherent ability to recognize and process crucial spatial relationships within images outperforms conventional CNNs, particularly in tasks that require the distinction of visually similar classes.
View Article and Find Full Text PDFJMIR Public Health Surveill
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Institute of Infectious Disease and Vaccine, School of Public Health, Zhejiang Chinese Medical University, Hangzhou, China.
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