Postmortem Evidence of Brain Inflammatory Markers and Injury in Septic Patients: A Systematic Review

Barichello, Tatiana; Generoso, Jaqueline S.; Dominguini, Diogo; Córneo, Emily; Giridharan, Vijayasree V.; Sahrapour, Taha A; Simões, Lutiana R.; Rosa, Maria Inês da; Petronilho, Fabrícia; Ritter, Cristiane; Sharshar, Tarek; Dal‐Pizzol, Felipe

doi:10.1097/ccm.0000000000005307

Cited by 15 publications

(11 citation statements)

References 131 publications

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“…Biomarkers may help stratify septic patients into biological phenotypes, for example, hyperinflammatory versus immunosuppressive. Biomarkers can also be used to identify gut permeability, blood-brain barrier (BBB) permeability, probability of hospital readmission, and longer-term outcomes [4,5].…”

Section: Introductionmentioning

confidence: 99%

Biomarkers for sepsis: more than just fever and leukocytosis—a narrative review

et al. 2022

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A biomarker describes a measurable indicator of a patient's clinical condition that can be measured accurately and reproducibly. Biomarkers offer utility for diagnosis, prognosis, early disease recognition, risk stratification, appropriate treatment (theranostics), and trial enrichment for patients with sepsis or suspected sepsis. In this narrative review, we aim to answer the question, "Do biomarkers in patients with sepsis or septic shock predict mortality, multiple organ dysfunction syndrome (MODS), or organ dysfunction?" We also discuss the role of pro- and anti-inflammatory biomarkers and biomarkers associated with intestinal permeability, endothelial injury, organ dysfunction, blood–brain barrier (BBB) breakdown, brain injury, and short and long-term mortality. For sepsis, a range of biomarkers is identified, including fluid phase pattern recognition molecules (PRMs), complement system, cytokines, chemokines, damage-associated molecular patterns (DAMPs), non-coding RNAs, miRNAs, cell membrane receptors, cell proteins, metabolites, and soluble receptors. We also provide an overview of immune response biomarkers that can help identify or differentiate between systemic inflammatory response syndrome (SIRS), sepsis, septic shock, and sepsis-associated encephalopathy. However, significant work is needed to identify the optimal combinations of biomarkers that can augment diagnosis, treatment, and good patient outcomes.

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Section: Introductionmentioning

confidence: 99%

Biomarkers for sepsis: more than just fever and leukocytosis—a narrative review

et al. 2022

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“…Biomarkers may help to stratify patients with sepsis based on biological phenotypes, such as high inflammation and immunosuppression. Biomarkers can also be used to evaluate intestinal permeability, blood–brain barrier (BBB) permeability, readmission probability, and long-term outcomes (Yende et al 2019 ; Barichello et al 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Use of machine learning-based integration to develop a monocyte differentiation-related signature for improving prognosis in patients with sepsis

Ning

Sun

Wang

et al. 2023

Mol Med

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Background Although significant advances have been made in intensive care medicine and antibacterial treatment, sepsis is still a common disease with high mortality. The condition of sepsis patients changes rapidly, and each hour of delay in the administration of appropriate antibiotic treatment can lead to a 4–7% increase in fatality. Therefore, early diagnosis and intervention may help improve the prognosis of patients with sepsis. Methods We obtained single-cell sequencing data from 12 patients. This included 14,622 cells from four patients with bacterial infectious sepsis and eight patients with sepsis admitted to the ICU for other various reasons. Monocyte differentiation trajectories were analyzed using the “monocle” software, and differentiation-related genes were identified. Based on the expression of differentiation-related genes, 99 machine-learning combinations of prognostic signatures were obtained, and risk scores were calculated for all patients. The “scissor” software was used to associate high-risk and low-risk patients with individual cells. The “cellchat” software was used to demonstrate the regulatory relationships between high-risk and low-risk cells in a cellular communication network. The diagnostic value and prognostic predictive value of Enah/Vasp-like (EVL) were determined. Clinical validation of the results was performed with 40 samples. The “CBNplot” software based on Bayesian network inference was used to construct EVL regulatory networks. Results We systematically analyzed three cell states during monocyte differentiation. The differential analysis identified 166 monocyte differentiation-related genes. Among the 99 machine-learning combinations of prognostic signatures constructed, the Lasso + CoxBoost signature with 17 genes showed the best prognostic prediction performance. The highest percentage of high-risk cells was found in state one. Cell communication analysis demonstrated regulatory networks between high-risk and low-risk cell subpopulations and other immune cells. We then determined the diagnostic and prognostic value of EVL stabilization in multiple external datasets. Experiments with clinical samples demonstrated the accuracy of this analysis. Finally, Bayesian network inference revealed potential network mechanisms of EVL regulation. Conclusions Monocyte differentiation-related prognostic signatures based on the Lasso + CoxBoost combination were able to accurately predict the prognostic status of patients with sepsis. In addition, low EVL expression was associated with poor prognosis in sepsis.

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“…The etiology of SAE is multifactorial, ischemic/hemorrhagic lesions, blood–brain barrier (BBB) destruction, cerebrovascular dysfunction, and metabolic changes are the main pathomechanisms involved in the development of SAE [ 3 ]. All of these mechanisms are collectively mediated by a variety of immune factors, which orchestrate genetic program that controls diverse cellular and molecular pathways, including immune response pathways [ 4 ]. In terms of sepsis, inflammatory responses in the brain can be divided into two categories: primary inflammatory responses induced by resident immune cells and secondary inflammatory responses induced by peripheral immune cells.…”

Section: Introductionmentioning

confidence: 99%

The Key Drivers of Brain Injury by Systemic Inflammatory Responses after Sepsis: Microglia and Neuroinflammation

et al. 2022

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Sepsis is a leading cause of intensive care unit admission and death worldwide. Most surviving patients show acute or chronic mental disorders, which are known as sepsis-associated encephalopathy (SAE). Although accumulating studies in the past two decades focused on the pathogenesis of SAE, a systematic review of retrospective studies which exclusively focuses on the inflammatory mechanisms of SAE has been lacking yet. This review summarizes the recent advance in the field of neuroinflammation and sheds light on the activation of microglia in SAE. Activation of microglia predominates neuroinflammation. As the gene expression profile changes, microglia show heterogeneous characterizations throughout all stages of SAE. Here, we summarize the systemic inflammation following sepsis and also the relationship of microglial diversity and neuroinflammation. Moreover, a collection of neuroinflammation-related dysfunction has also been reviewed to illustrate the possible mechanisms for SAE. In addition, promising pharmacological or non-pharmacological therapeutic strategies, especially those which target neuroinflammation or microglia, are also concluded in the final part of this review. Collectively, clarification of the vital relationship between neuroinflammation and SAE-related mental disorders would significantly improve our understanding of the pathophysiological mechanisms in SAE and therefore provide potential targets for therapies of SAE aimed at inhibiting neuroinflammation.

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Postmortem Evidence of Brain Inflammatory Markers and Injury in Septic Patients: A Systematic Review

Cited by 15 publications

References 131 publications

Biomarkers for sepsis: more than just fever and leukocytosis—a narrative review

Biomarkers for sepsis: more than just fever and leukocytosis—a narrative review

Use of machine learning-based integration to develop a monocyte differentiation-related signature for improving prognosis in patients with sepsis

The Key Drivers of Brain Injury by Systemic Inflammatory Responses after Sepsis: Microglia and Neuroinflammation

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