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To explore the feasibility and accuracy of predicting respiratory tract infections (RTIs) using physiological data obtained from consumer-grade smartwatches. The study used smartwatches and paired mobile applications to continuously collect physiological parameters while participants slept. A personalized baseline model was established using multi-day data, followed by the construction of RTIs risk prediction algorithm based on deviations from physiological parameter trends.
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Severe pneumonia remains a major threat to human health, particularly in patients who progress to sepsis, with immune dysregulation playing a central role in its pathophysiological mechanism. Although immunomodulatory therapies have evolved alongside our improved understanding of immune imbalance, conflicting clinical evidence persists. For example, agents targeting similar pathways may produce divergent outcomes, while those with opposing mechanisms of action may yield comparable results.
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The existence of free will has been called into question by Benjamin Libet's seminal experiment, who argued that our conscious decision is preceded by an unconscious decision reflected in the readiness potential (RP). Alternatively, it has been argue that the RP rather reflects a decision process in which different signals accumulate until they reach the intention threshold, at which point an agent experience their intention simultaneously. This raises the question what type of signal is accumulated given that no external information is provided.
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Department of Medical Microbiology, Faculty of Medicine, Ege University, Izmir 35100, Turkey.
Introduction: The aim of this study was to compare the performance of different clinical specimens-nasopharyngeal (NP) swabs collected by healthcare professionals (HCP-NP), self-collected nasal swabs (Sc-N), and saliva samples (S)-in diagnostic tests for investigating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA and influenza A/B RNA.
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