Introduction: Electrograms exhibit a wide variety of morphologies during atrial fibrillation (AF). The basis of these time courses, however, is not completely understood. In this study, data from computer models were studied to relate features of the signals to the underlying dynamics and tissue substrate.
Methods And Results: A computer model of entire human atria with a gross fiber architecture based on histology and membrane kinetics based on the Courtemanche et al. atrial model was developed to simulate paced activation and simulated AF. Unipolar electrograms were computed using a current source approximation at 256 sites in right atrium, to simulate a mapping array. The results show the following: (1) In a homogeneous and isotropic tissue, the presence of highly asymmetric electrograms is rare (<2%), although there is a marked variability in amplitude and symmetry. (2) The introduction of anisotropy increases this variability in symmetry and amplitude of the, electrograms especially for propagation across fibers. The percentage of highly asymmetric electrograms increases to 12% to 15% for anisotropy ratios greater than 3:1. (3) Multiphasic and fractionated electrograms are rarely seen in the model with uniform properties but are more common (15%-17%) in a model including regions with abrupt changes in conductivity. Beat-to-beat variations in the occurrence of multiphasic signals are possible with fixed anatomic heterogeneity, due to beat-to-beat variations in the direction of the wavefront relative to the heterogeneity.
Conclusion: Analysis of the amplitude and symmetry of unipolar atrial electrograms can provide information about the electrophysiologic substrate maintaining AF.
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http://dx.doi.org/10.1046/j.1540.8167.90308.x | DOI Listing |
In the context of Chinese clinical texts, this paper aims to propose a deep learning algorithm based on Bidirectional Encoder Representation from Transformers (BERT) to identify privacy information and to verify the feasibility of our method for privacy protection in the Chinese clinical context. We collected and double-annotated 33,017 discharge summaries from 151 medical institutions on a municipal regional health information platform, developed a BERT-based Bidirectional Long Short-Term Memory Model (BiLSTM) and Conditional Random Field (CRF) model, and tested the performance of privacy identification on the dataset. To explore the performance of different substructures of the neural network, we created five additional baseline models and evaluated the impact of different models on performance.
View Article and Find Full Text PDFSyst Biol Reprod Med
December 2025
Department of Biosciences and Technology for Food, Agriculture and Environment, University of Teramo, Teramo, Italy.
MicroRNAs (miRNAs) have acquired an increased recognition to unravel the complex molecular mechanisms underlying Diminished Ovarian Reserve (DOR), one of the main responsible for infertility. To investigate the impact of miRNA profiles in granulosa cells and follicular fluid, crucial players in follicle development, this study employed a computational network theory approach to reconstruct potential pathways regulated by miRNAs in granulosa cells and follicular fluid of women suffering from DOR. Available data from published research were collected to create the FGC_MiRNome_MC, a representation of miRNA target genes and their interactions.
View Article and Find Full Text PDFPharm Biol
December 2025
The Affiliated Hospital, Changchun University of Chinese Medicine, Changchun, China.
Context: The decline in ovarian reserve is a major concern in female reproductive health, often associated with oxidative stress and mitochondrial dysfunction. Although ginsenoside Rg1 is known to modulate mitophagy, its effectiveness in mitigating ovarian reserve decline remains unclear.
Objective: To investigate the role of ginsenoside Rg1 in promoting mitophagy to preserve ovarian reserve.
Stat Med
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U.S. Food and Drug Administration, Silver Spring, Maryland.
The recent U.S. Food and Drug Administration guidance on complex innovative trial designs acknowledges the use of Bayesian strategies to incorporate historical information based on clinical expertise and data similarity.
View Article and Find Full Text PDFViruses
January 2025
Department of Immunology and Microbiology, Scripps Research Institute, La Jolla, CA 92037, USA.
Lassa fever (LF), a viral hemorrhagic fever disease with a case fatality rate that can be over 20% among hospitalized LF patients, is endemic to many West African countries. Currently, no vaccines or therapies are specifically licensed to prevent or treat LF, hence the significance of developing therapeutics against the mammarenavirus Lassa virus (LASV), the causative agent of LF. We used in silico docking approaches to investigate the binding affinities of 2015 existing drugs to LASV proteins known to play critical roles in the formation and activity of the virus ribonucleoprotein complex (vRNP) responsible for directing replication and transcription of the viral genome.
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