Background: Asthma exacerbations in children pose a significant burden on healthcare systems and families. While traditional risk assessment tools exist, artificial intelligence (AI) offers the potential for enhanced prediction models.
Objective: This study aims to systematically evaluate and quantify the performance of machine learning (ML) algorithms in predicting the risk of hospitalisation and emergency department (ED) admission for acute asthma exacerbations in children.
In late December 2019, SARS-CoV-2 was identified as the cause of a new pneumonia (COVID-19), leading to a global pandemic declared by the WHO on 11 March 2020, with significant human, economic, and social costs. Although most COVID-19 cases are asymptomatic or mild, 14% progress to severe disease, and 5% develop critical illness with complications such as interstitial pneumonia, acute respiratory distress syndrome (ARDS), and multiple organ dysfunction syndrome (MODS). SARS-CoV-2 primarily targets the respiratory system but can affect multiple organs due to the widespread presence of angiotensin-converting enzyme 2 (ACE2) receptors, which the virus uses to enter cells.
View Article and Find Full Text PDFIn December 2019, a SARS-CoV-2 virus, coined Coronavirus Disease 2019 (COVID-19), discovered in Wuhan, China, affected the global population, causing more than a million and a half deaths. Since then, many studies have shown that the hyperinflammatory response of the most severely affected patients was primarily related to a higher concentration of the pro-inflammatory cytokine interleukin-6, which directly correlated with disease severity and high mortality. Our study analyzes IL-6 and its soluble receptor complex (sIL-6R and sgp130) in critically ill COVID-19 patients who suffered severe respiratory failure from the perspective of the second COVID wave of 2020.
View Article and Find Full Text PDFThe Romans knew of Nitrodi's spring on the island of Ischia more than 2000 years ago. Although the health benefits attributed to Nitrodi's water are numerous, the underlying mechanisms are still not understood. In this study, we aim to analyze the physicochemical properties and biological effects of Nitrodi's water on human dermal fibroblasts to determine whether the water exerts in vitro effects that could be relevant to skin wound healing.
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