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: Traumatic brain injury (TBI) is a head trauma usually associated with death and endothelial glycocalyx damage. Syndecan-1 (SDC-1)-a biomarker of glycocalyx degradation-has rarely been reported in meta-analyses to determine the clinical prognostic value in TBI patients. Methods: We looked into PubMed, EMBASE, Cochrane Library, and Web of Science databases from January 1, 1990, to May 1, 2023, to identify eligible studies. ⋯ Isotrauma TBI patients with higher SDC-1 level were at a higher risk of 30-day in-hospital mortality (odds ratio = 3.32; 95% CI: 1.67-6.60; P = 0.0006). Conclusion: This meta-analysis suggests that SDC-1 could be a biomarker of endotheliopathy and coagulopathy in TBI, as it was increased in isotrauma TBI patients and was higher in multitrauma TBI patients. There is a need for additional research into the use of SDC-1 as a prognostic biomarker in TBI, especially in isotrauma TBI patients.
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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.
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Multicenter Study Observational Study
Coagulopathy Parameters Predictive of Outcomes in Sepsis-induced Acute Respiratory Distress Syndrome: A Sub-Analysis of the Two Prospective Multicenter Cohort Studies.
Background: Although coagulopathy is often observed in acute respiratory distress syndrome (ARDS), its clinical impact remains poorly understood. Objectives: This study aimed to clarify the coagulopathy parameters that are clinically applicable for prognostication and to determine anticoagulant indications in sepsis-induced ARDS. Method: This study enrolled patients with sepsis-derived ARDS from two nationwide multicenter, prospective observational studies. ⋯ Although patients without TEP coagulopathy showed significant improvements in oxygenation over the first 4 days, patients with TEP coagulopathy showed no significant improvement (ΔPaO 2 /FiO 2 ratio, 24 ± 20 vs. 90 ± 9; P = 0.026). Furthermore, anticoagulant use was significantly correlated with mortality and oxygenation recovery in patients with TEP coagulopathy but not in patients without TEP coagulopathy. Conclusion: Thrombocytopenia and elongated prothrombin time coagulopathy is closely associated with better outcomes and responses to anticoagulant therapy in sepsis-induced ARDS, and our coagulopathy criteria may be clinically useful.
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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.