Articles: sepsis.
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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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Intensive care medicine · Jan 2024
Determinants of fluid use and the association between volume of fluid used and effect of balanced solutions on mortality in critically ill patients: a secondary analysis of the BaSICS trial.
Fluid use could modulate the effect of balanced solutions (BS) on outcome of intensive care unit (ICU) patients. It is uncertain whether fluid use practices are driven more by patient features or local practices. It is also unclear whether a "dose-response" for the potential benefits of balanced solutions exists. ⋯ Baseline patient characteristics collected in the BaSICS trial explain less of the variance of fluid use during the first 3 days than the enrolling site. Volume of fluid used and the effects of BS appear to interact, mostly in the sepsis subgroup where there was a strong association between fluid use after enrollment and the effect of BS on 90-day 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.
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Derivation and External Validation of the Ottawa Bloodstream Infection Model for Acutely Ill Adults.
Knowing the probability that patients have a bloodstream infection (BSI) could influence the ordering of blood cultures and interpretation of their preliminary results. Many previous BSI probability models have limited applicability and accuracy. This study used currently recommended modeling techniques and a large sample to derive and validate the Ottawa BSI Model. ⋯ The Ottawa BSI Model uses commonly available data to return an expected BSI probability for acutely ill patients. However, it cannot exclude BSI and its complexity requires computational assistance to use.