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Utilizing a Wireless Radar Framework in Combination With Deep Learning Approaches to Evaluate Obstructive Sleep Apnea Severity in Home-Setting Environments.
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School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan.
Objective: Common examinations for diagnosing obstructive sleep apnea (OSA) are polysomnography (PSG) and home sleep apnea testing (HSAT). However, both PSG and HSAT require that sensors be attached to a subject, which may disturb their sleep and affect the results. Hence, in this study, we aimed to verify a wireless radar framework combined with deep learning techniques to screen for the risk of OSA in home-based environments.
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Department of Minimally Invasive Digestive Surgery, Antoine-Béclère Hospital, Assistance Publique-Hôpitaux de ParisClamart 92140, Haute-Seine, France.
Anastomotic leakage (AL) is a significant complication following rectal cancer surgery, adversely affecting both quality of life and oncological outcomes. Recent advancements in artificial intelligence (AI), particularly machine learning and deep learning, offer promising avenues for predicting and preventing AL. These technologies can analyze extensive clinical datasets to identify preoperative and perioperative risk factors such as malnutrition, body composition, and radiological features.
View Article and Find Full Text PDFCompSafeNano project: NanoInformatics approaches for safe-by-design nanomaterials.
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NovaMechanics Ltd, Nicosia 1070, Cyprus.
The CompSafeNano project, a Research and Innovation Staff Exchange (RISE) project funded under the European Union's Horizon 2020 program, aims to advance the safety and innovation potential of nanomaterials (NMs) by integrating cutting-edge nanoinformatics, computational modelling, and predictive toxicology to enable design of safer NMs at the earliest stage of materials development. The project leverages Safe-by-Design (SbD) principles to ensure the development of inherently safer NMs, enhancing both regulatory compliance and international collaboration. By building on established nanoinformatics frameworks, such as those developed in the H2020-funded projects NanoSolveIT and NanoCommons, CompSafeNano addresses critical challenges in nanosafety through development and integration of innovative methodologies, including advanced models, approaches including machine learning (ML) and artificial intelligence (AI)-driven predictive models and 1st-principles computational modelling of NMs properties, interactions and effects on living systems.
View Article and Find Full Text PDFFood supply toxicants and additives alter the gut microbiota and risk of metabolic disease.
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Department of Biochemistry and Biomedical Sciences.
Metabolic disease is rising along with both global industrialization and the use of new commercial, agricultural, and industrial chemicals and food additives. Exposure to these compounds may contribute to aspects of metabolic disease such as obesity, diabetes, and fatty liver disease. Ingesting compounds in the food supply is a key route of human exposure, resulting in the interaction between toxicants or additives and the intestinal microbiota.
View Article and Find Full Text PDFMulti-disciplinary research will be the key to stop, restore, and end MS.
Mult Scler
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Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.
The past 25 years have brought extraordinary advances in our understanding of MS pathogenesis and the subsequent development of effective therapies. Collaborative genetics efforts have uncovered the association of 236 common DNA variants with disease susceptibility and the first association with disease severity, paving the way to more effective therapies, particularly for progressive forms of the disease. In parallel, and in addition to established environmental disease triggers or modifiers, new collaborative work has revealed new associations with components of the gut microbiome.
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