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 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.
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Background: Intermediate-risk pulmonary embolism (PE) patients in the intensive care unit (ICU) are at a higher risk of hemodynamic deterioration than those in the general ward. This study aimed to construct a machine learning (ML) model to accurately identify the tendency for hemodynamic deterioration in the ICU patients with intermediate-risk PE. Method: A total of 704 intermediate-risk PE patients from the MIMIC-IV database were retrospectively collected. ⋯ Simplified XGBoost model demonstrated the best predictive performance with an area under the curve of 0.866 (95% confidence interval, 0.800-0.925), and after recalibrated by isotonic regression, the area under the curve improved to 0.885 (95% confidence interval, 0.822-0.935). Based on the simplified XGBoost model, a web app was developed to identify the tendency for hemodynamic deterioration in ICU patients with intermediate-risk PE. Conclusion: A simplified XGBoost model can accurately predict the occurrence of hemodynamic deterioration for intermediate-risk PE patients in the ICU, assisting clinical workers in providing more personalized management for PE patients in the ICU.
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The analysis of the single-cell transcriptome has emerged as a powerful tool to gain insights on the basic mechanisms of health and disease. It is widely used to reveal the cellular diversity and complexity of tissues at cellular resolution by RNA sequencing of the whole transcriptome from a single cell. Equally, it is applied to discover an unknown, rare population of cells in the tissue. ⋯ And with the development of numerous packages in R and Python, new directions in the computational analysis of single-cell transcriptomes can be taken to characterize healthy versus diseased tissues to obtain novel pathological insights. Downstream analysis such as differential gene expression analysis, gene ontology term analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, cell-cell interaction analysis, and trajectory analysis has become standard practice in the workflow of single-cell transcriptome analysis to further examine the biology of different cell types. Here, we provide a broad overview of single-cell transcriptome analysis in health and disease conditions currently applied in various studies.