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Mediators of Disseminated Intravascular Coagulation: Molecular Mechanisms

2017· article· en· W3146282831 on OpenAlexaff
Patricia C. Liaw

Bibliographic record

VenueBlood · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicNeutrophil, Myeloperoxidase and Oxidative Mechanisms
Canadian institutionsMcMaster UniversityThrombosis and Atherosclerosis Research Institute
Fundersnot available
KeywordsNeutrophil extracellular trapsDisseminated intravascular coagulationSepsisHMGB1CoagulationPlateletImmunologyMedicineHemostasisPlatelet activationInflammationPathologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Disseminated intravascular coagulation (DIC) is characterized by a spectrum of haemorrhage and microvascular thrombosis complicating many conditions including sepsis and trauma. In recent years, there is growing evidence that damage associated molecular patterns (DAMPs) play a crucial role in the pathogenesis of DIC. Upon cell death and/or cell activation, extracellular DNA as well as DNA binding proteins (e.g. histones and high mobility group box 1 protein) are released into the circulation. These molecules can influence hemostasis by promoting coagulation via the contact pathway, inducing platelet aggregation, activating endothelial cells, and inhibiting fibrinolysis. Extracellular HMGB1 also activates neutrophils to induce the release of neutrophil extracellular traps, which may further contribute to tissue injury and organ dysfunction. Cell-free DNA (cfDNA) from nuclear, mitochondrial, and bacterial sources has varying pro-inflammatory effects, although all three have similar procoagulant and platelet-stimulating potential. Elevated levels of cfDNA and histones are predictive of poor outcome in sepsis and trauma, with neutrophils being the major source of DNA released from whole blood in vitro. In septic patients, endogenous cfDNA correlates positively with thrombin generation potential, and addition of recombinant DNase attenuates thrombin generation. In a cecal ligation and puncture (CLP) model of sepsis, plasma cfDNA levels rise within a few hours and are accompanied by elevations in IL-6 and thrombin-antithrombin complexes. Delayed (ie. 6 hours post-CLP surgery) but not early administration of recombinant DNase decreases bacterial load in the lungs, blood, and peritoneal cavity, and attenuates organ damage. Thus, the timing of DNase administration may be a crucial element in future investigations of the therapeutic potential of DNase in sepsis. With respect to potential therapeutic inhibitors of histones, activated protein C (APC) cleaves histones H2A, H3, and H4. Co-injection of APC with histones rescues mice from death. C-reactive protein (CRP) is an acute phase protein that reduces histone-induced endothelial cell damage and platelet aggregation. Administration of histones and CRP to mice reduces endothelial damage, alleviates thrombocytopenia, and attenuates coagulation activation. Heparin can also bind histones and prevent histone-mediated cytotoxicity of endothelial cells. In vivo, non-anticoagulant heparin reduces mortality from sterile inflammation and from sepsis in mouse models. Translational studies of septic patients have shown that the prognostic utility of clinical scores can be enhanced by combining it with cfDNA and protein C levels, suggesting that inclusion of cfDNA and protein C in risk stratification tools may be valuable for monitoring response to treatment, enhancing confidence in clinical decision making, or for inclusion in trials of new anti-sepsis therapies. In summary, cfDNA and DNA-binding proteins are critically involved in the pathogenesis of DIC. Strategies that inhibit or neutralize the harmful effects of cfDNA and histones may have great therapeutic potential. Disclosures No relevant conflicts of interest to declare.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.232
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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