Publications by authors named "M D Arnaud"

Article Synopsis
  • The exact causes of common atrial flutter are not fully understood, particularly regarding arrhythmia triggers and the impact of slow-conducting heart tissue.
  • A detailed electrophysiological study was conducted on a patient to investigate how this arrhythmia starts and is maintained, utilizing techniques like electro-anatomical mapping.
  • The study found that common atrial flutter begins with a unidirectional conduction block at the septal cavo-tricuspid isthmus, resulting in a counter-clockwise activation pattern and stabilization near specific heart regions, without any slowing of conduction.
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Currently, pulmonary vein isolation (PVI) is the gold standard in catheter ablation for atrial fibrillation (AF). However, PVI alone may be insufficient in the management of persistent AF, and complementary methods are being explored. One such method takes an anatomical approach-improving both its success rate and lesion durability may lead to improved treatment outcomes.

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The accurate identification and prioritization of antigenic peptides is crucial for the development of personalized cancer immunotherapies. Publicly available pipelines to predict clinical neoantigens do not allow direct integration of mass spectrometry immunopeptidomics data, which can uncover antigenic peptides derived from various canonical and noncanonical sources. To address this, we present an end-to-end clinical proteogenomic pipeline, called NeoDisc, that combines state-of-the-art publicly available and in-house software for immunopeptidomics, genomics and transcriptomics with in silico tools for the identification, prediction and prioritization of tumor-specific and immunogenic antigens from multiple sources, including neoantigens, viral antigens, high-confidence tumor-specific antigens and tumor-specific noncanonical antigens.

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Article Synopsis
  • The study examines the challenges of manually analyzing root dynamics using images, highlighting issues like time consumption and annotator bias, especially in complex forest soils.
  • AI tools, specifically a convolutional neural network (CNN), were tested for their ability to analyze root lengths in a diverse forest setting, but showed limitations in accuracy and precision compared to human experts.
  • Results indicated that less experienced annotators overestimate root lengths, while the CNN model, though faster, still lacked the accuracy needed for ecological research, suggesting the need for further refinement of AI tools for natural environments.*
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