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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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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Objective: This study aimed to test whether the prognostic value of tryptophanyl-tRNA synthetase 1 (WARS1) for 28-day mortality in patients with sepsis was affected by monocytopenia. Methods: A prospective analysis of retrospectively collected samples from 74 sepsis patients was performed. WARS1, C-reactive protein (CRP), and procalcitonin were measured at admission and 24 and 72 h after admission. ⋯ The AUROCs of WARS1 at admission and 24 h for mortality were significantly higher in patients without monocytopenia (0.830, 0.818) than in patients with monocytopenia (0.232, 0.196; P < 0.001, both). When patients without monocytopenia were analyzed, the AUROCs of WARS1 for mortality were 0.830 and 0.818 at admission and 24 h, respectively, which were significantly higher than those of CRP (0.586, 0.653) and procalcitonin (0.456, 0.453) at the same time points ( P = 0.024 and 0.034, respectively). Conclusion: WARS1 is a useful biomarker for prognosis in sepsis patients without monocytopenia.
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Sepsis is an organ dysfunction caused by a dysregulated host response to infection and remains an ongoing threat to human health worldwide. Septic shock is the most severe subset of sepsis as characterized by abnormalities in cells, circulation, and metabolism. As a time-dependent condition, early recognition allowing appropriate therapeutic measures to be started in a timely manner becomes the most effective way to improve prognosis. ⋯ DDX47 showed preferable diagnostic value in various scenarios, especially in patients with common infections or sepsis and septic shock. Here we also show that hub genes may regulate immune function and immune cell counts through the interaction of different apoptotic pathways and immune checkpoints based on the high correlation. DDX47 is closely associated with B cells according to single-cell sequencing results.
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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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Objective: To investigate whether pediatric sepsis phenotypes are stable in time. Methods: Retrospective cohort study examining children with suspected sepsis admitted to a Pediatric Intensive Care Unit at a large freestanding children's hospital during two distinct periods: 2010-2014 (early cohort) and 2018-2020 (late cohort). K-means consensus clustering was used to derive types separately in the cohorts. ⋯ Despite low mortality, this type had the longest PICU length of stay. Conclusions: This single center study identified four pediatric sepsis phenotypes in an earlier epoch but five in a later epoch, with the new type having a large proportion of characteristics associated with medical complexity, particularly technology dependence. Personalized sepsis therapies need to account for this expanding patient population.