Publications by authors named "P Moreno"

Background: Whether the high bleeding risk (HBR) criteria of the Academic Research Consortium (ARC) have a consistent predictive ability across different categories of body mass index (BMI) remains unclear.

Methods: Consecutive patients undergoing percutaneous coronary intervention (PCI) between 2012 and 2019 at Mount Sinai Hospital (New York, USA) were stratified into five BMI categories (18.5-24.

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Aim: Due to the absence of validated bleeding risk tools in cancer patients undergoing percutaneous coronary intervention (PCI), we aimed to validate an adapted version of the Academic Research Consortium (ARC) High Bleeding Risk (HBR) criteria.

Methods: Consecutive patients with active or remission cancer undergoing PCI between 2012 and 2022 at Mount Sinai Hospital (New York, USA) were included. Patients were considered at HBR if they met at least one of the major ARC-HBR criteria, other than cancer, or two minor criteria.

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As robots become integral to various sectors, improving human-robot collaboration is crucial, particularly in anticipating human actions to enhance safety and efficiency. Electroencephalographic (EEG) signals offer a promising solution, as they can detect brain activity preceding movement by over a second, enabling predictive capabilities in robots. This study explores how EEG can be used for action anticipation in human-robot interaction (HRI), leveraging its high temporal resolution and modern deep learning techniques.

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The impact of breast implants on the immune system has been debated since their introduction in the 1960s, linking silicone to systemic autoimmune diseases. Recent studies have shown that silicone gel can migrate from the implant capsule, triggering immune responses by proliferating immune cells and releasing cytokines, affecting T-cell function. Silicone particles can induce the release of IL-1β and activate the NALP3 inflammasome and B cells, causing an imbalance in regulatory T cells, responder T cells, and Th17 cells.

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