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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Background: Patients receiving massive transfusion protocol (MTP) are at risk for posttransfusion hypocalcemia and hyperkalemia. Previous retrospective analysis has suggested the potassium/ionized calcium (K/iCa) ratio as a prognostic indicator of mortality. This prospective study sought to validate the value of the K/iCa ratio as a predictor for mortality in patients receiving MTP. ⋯ Furthermore, it demonstrates that posttransfusion K levels along with iCa levels should be carefully monitored in the MTP setting. Level of Evidence: Level II. Study Type: Prognostic/epidemiological.
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Introduction: The compensatory reserve measurement (CRM) is a continuous noninvasive monitoring technology that provides an assessment of the integrated capacity of all physiological mechanisms associated with responses to a hypovolemic stressor such as hemorrhagic shock. No prior studies have analyzed its use for intraoperative resuscitation guidance. Methods: A prospective observational study was conducted of 23 patients undergoing orthotopic liver transplant. ⋯ XGBoost prediction models showed superior discriminatory capacity of CRM alone compared with the model with SBP and HR and no difference when all three were combined (CRM-HR-SBP). All XGBoost models outperformed equivalent linear regression models. Conclusion: These results demonstrate that CRM can provide an adjunctive clinical tool that can augment early and accurate of hemodynamic compromise and promote goal-directed resuscitation in the perioperative setting.
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Introduction: Gut microbiota dysbiosis is associated with susceptibility to sepsis and poor outcomes. However, changes to the intestinal microbiota during sepsis and their value as biomarkers are unclear. In this study, we compared the intestinal microbiota of patients with sepsis and healthy controls. ⋯ The genus Blautia was more abundant in controls than in the sepsis group, and Faecalibacterium less abundant in the nonsurvivor than in the other groups. Regression analysis associated low Shannon index with 6-month mortality. Conclusions: Survivors of sepsis, nonsurvivors, and healthy controls have different gut microbiomes, and a low Shannon index is a risk factor for 6-month mortality.
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Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of machine learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. ⋯ Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. Machine learning has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management.
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Background: Sepsis is a life-threatening systemic inflammatory disease that can cause many diseases, including acute kidney injury (AKI). Increasing evidence showed that a variety of circular RNAs were considered to be involved in the development of the disease. In this study, we aimed to elucidate the role and potential mechanism of circUSP42 in sepsis-induced AKI. ⋯ In addition, circUSP42 induced DUSP1 expression via sponging miR-182-5p to ameliorate LPS-induced HK2 cell damage. Conclusion : Our results showed that circUSP42 overexpression might attenuate LPS-induced HK2 cell injury by regulating miR-182-5p/DUSP1 axis. This might provide therapeutic strategy for the treatment of sepsis.