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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Randomized Controlled Trial
Renal protective effect and clinical analysis of vitamin B6 in patients with sepsis.
Objective: To investigate the protective effect and possible mechanisms of vitamin B 6 against renal injury in patients with sepsis. Methods: A total of 128 patients with sepsis who met the entry criteria in multiple centers were randomly divided into experimental (intravenous vitamin B 6 therapy) and control (intravenous 0.9% sodium chloride therapy) groups based on usual care. Clinical data, the inflammatory response indicators interleukin 6 (IL-6), interleukin 8 (IL-8), tumor necrosis factor (TNF-α), and endothelin-1 (ET-1), the oxidative stress response indicators superoxide dismutase, glutathione and malondialdehyde, and renal function (assessed by blood urea nitrogen, serum creatinine, and renal resistance index monitored by ultrasound) were compared between the two groups. ⋯ There was no statistical difference between the two groups in the rate of renal replacement therapy and 28 d mortality ( P > 0.05). However, the intensive care unit length of stay and the total hospitalization expenses in the experimental group were significantly lower than those in the control group ( P < 0.05). Conclusion: The administration of vitamin B 6 in the treatment of patients with sepsis attenuates renal injury, and the mechanism may be related to pyridoxine decreasing the levels of inflammatory mediators and their regulation by redox stress.
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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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Objective: We conducted a two-sample bidirectional Mendelian randomization (MR) study to investigate the causal relationships between herpes viruses and sepsis. Methods: Publicly available genome-wide association study data were used. Four viruses, HSV-1, HSV-2, EBV, and CMV, were selected, with serum positivity and levels of antibody in serum as the herpes virus data. ⋯ Varied effects of EBV and CMV antibodies on sepsis severity are noted. Severe sepsis results in a decline in CMV antibody levels. Our results help prognostic and predictive enrichment and offer valuable information for precision sepsis treatment.
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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.