Shock : molecular, cellular, and systemic pathobiological aspects and therapeutic approaches : the official journal the Shock Society, the European Shock Society, the Brazilian Shock Society, the International Federation of Shock Societies
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The adenosine concentration and forkhead box protein (Foxp3) expression in T regulatory cells (T regs ) are increased during sepsis. However, the mechanism by which adenosine induces Foxp3 expression is incompletely understood. A cecal ligation and puncture (CLP) model was constructed using C57BL/J mice. ⋯ A2aR blockade or inhibition of CREB expression inhibited Foxp3 expression in T regs. In the CLP model, use of CREB inhibitors could inhibit Foxp3 expression and reduce the bacterial load. In summary, adenosine in sepsis promotes CREB phosphorylation via A2aR which, in turn, upregulates Foxp3 expression in T regs .
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Sepsis is a life-threatening organ dysfunction caused by an unregulated host response to infection. It is an important clinical problem in acute and critical care. ⋯ Great progress has been made in the study of sepsis-associated rodent models and in vitro cellular models. However, the evidence of curcumin in the clinical management practice of sepsis is still insufficient; hence, it is very important to systematically summarize the study of curcumin and sepsis pathogenesis.
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Background: Hemolysis is a frequent complication in patients with sepsis, ARDS, or extracorporeal membrane oxygenation (ECMO). Haptoglobin (Hp) can scavenge released cell-free hemoglobin (CFH). Hemolysis and low plasma concentrations of Hp may be independently associated with mortality in critically ill patients. ⋯ Patients with initial Hp <0.66 g/L had higher risks for Hp depletion than patients with initial Hp ≥0.66 g/L. Conclusion: Patients with Hp depletion within the first week of ECMO therapy might benefit from close monitoring of hemolysis with early detection and elimination of the underlying cause. They might be potential candidates for future Hp supplementation therapy to prevent overload of the CFH-scavenger system.
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The delayed diagnosis of invasive fungal infection (IFI) is highly correlated with poor prognosis in patients. Early identification of high-risk patients with invasive fungal infections and timely implementation of targeted measures is beneficial for patients. The objective of this study was to develop a machine learning-based predictive model for invasive fungal infection in patients during their intensive care unit (ICU) stay. ⋯ Importance: Invasive fungal infections are characterized by high incidence and high mortality rates characteristics. In this study, we developed a clinical prediction model for invasive fungal infections in critically ill patients based on machine learning algorithms. The results show that the machine learning model based on 20 clinical features has good predictive value.