α1-adrenoceptor stimulation ameliorates lipopolysaccharide-induced lung injury by inhibiting alveolar macrophage inflammatory responses through NF-κB and ERK1/2 pathway in ARDS

IntroductionCatecholamines such as norepinephrine or epinephrine have been reported to participate in the development of acute respiratory distress syndrome (ARDS) by activating adrenergic receptors (ARs). But the role of α1-AR in this process has yet to be elucidated.MethodsIn this study, ARDS mouse model was induced by intratracheal instillation of lipopolysaccharide. After treatment with α1-AR agonist phenylephrine or antagonist prazosin, lung pathological injury, alveolar barrier disruption and inflammation, and haemodynamic changes were evaluated. Cytokine levels and cell viability of alveolar macrophages were measured in vitro. Nuclear factor κB (NF-κB), mitogen-activated protein kinase, and Akt signalling pathways were analysed by western blot.ResultsIt showed that α1-AR activation alleviated lung injuries, including reduced histopathological damage, cytokine expression, and inflammatory cell infiltration, and improved alveolar capillary barrier integrity of ARDS mice without influencing cardiovascular haemodynamics. In vitro experiments suggested that α1-AR stimulation inhibited secretion of TNF-α, IL-6, CXCL2/MIP-2, and promoted IL-10 secretion, but did not affect cell viability. Moreover, α1-AR stimulation inhibited NF-κB and enhanced ERK1/2 activation without significantly influencing p38, JNK, or Akt activation.DiscussionOur studies reveal that α1-AR stimulation could ameliorate lipopolysaccharide-induced lung injury by inhibiting NF-κB and promoting ERK1/2 to suppress excessive inflammatory responses of alveolar macrophages

Development and Trends in Artificial Intelligence in Critical Care Medicine: A Bibliometric Analysis of Related Research over the Period of 2010–2021

Background: The intensive care unit is a center for massive data collection, making it the best field to embrace big data and artificial intelligence. Objective: This study aimed to provide a literature overview on the development of artificial intelligence in critical care medicine (CCM) and tried to give valuable information about further precision medicine. Methods: Relevant studies published between January 2010 and June 2021 were manually retrieved from the Science Citation Index Expanded database in Web of Science (Clarivate), using keywords. Results: Research related to artificial intelligence in CCM has been increasing over the years. The USA published the most articles and had the top 10 active affiliations. The top ten active journals are bioinformatics journals and are in JCR Q1. Prediction, diagnosis, and treatment strategy exploration of sepsis, pneumonia, and acute kidney injury were the most focused topics. Electronic health records (EHRs) were the most widely used data and the “-omics” data should be integrated further. Conclusions: Artificial intelligence in CCM has developed over the past decade. With the introduction of constantly growing data volume and novel data types, more investigation on artificial intelligence ethics and model correctness and extrapolation should be performed for generalization