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Background: As iatrogenic hyperoxia has been related to adverse outcomes in critically ill patients, guidelines advise to titrate oxygen to physiological levels. In the prehospital setting where partial arterial oxygen (PaO) values are often not readily available, titration of oxygen is based on peripheral oxygen saturations (SpO2). In this study we aimed to investigate the efficacy of SpO guided oxygen titration in the prevention of hyperoxia.

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Background: Infrared thermography technology is a diagnostic imaging modality that converts temperature information on the surface of the human body into visualised thermograms. This technology has the capacity to intuitively detect the presence of certain abnormal conditions or foci in the human body. In recent years, the application of this technology in medicine has become increasingly extensive, especially in the areas of auxiliary diagnosis and early screening of diseases.

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Should you hold onto the treadmill handrails or not? Cortical evidence at different walking speeds.

J Neuroeng Rehabil

January 2025

Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa, Italy.

Background: Treadmill-based gait training is part of rehabilitation programs focused on walking abilities. The use of handrails embedded in treadmill systems is debated, and current literature only explores the issue from a behavioral perspective.

Methods: We examined the cortical correlates of treadmill walking in healthy participants using functional near-infrared spectroscopy.

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Background: Undernutrition remains a global crisis and is a focus of Sustainable Development Goals. While there are multiple known, effective interventions, complex interactions between prevention and treatment and resource constraints can lead to difficulties in allocating funding. Simulation studies that use in silico simulation can help illuminate the interactions between interventions and provide insight into the cost-effectiveness of alternative packages of options.

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Background: Despite the adverse health outcomes associated with longer duration diarrhea (LDD), there are currently no clinical decision tools for timely identification and better management of children with increased risk. This study utilizes machine learning (ML) to derive and validate a predictive model for LDD among children presenting with diarrhea to health facilities.

Methods: LDD was defined as a diarrhea episode lasting ≥ 7 days.

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