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Enregistrement W2921133565 · doi:10.1182/blood-2018-99-111297

Neutrophil Extracellular Traps in the Development of Sepsis-Induced Disseminated Intravascular Coagulation

2018· article· en· W2921133565 sur OpenAlexaff
Nicholas Leo Jackson Chornenki, Dhruva J. Dwivedi, Andrew C. Kwong, Nasim Zamir, Alison Fox‐Robichaud, Patricia C. Liaw

Notice bibliographique

RevueBlood · 2018
Typearticle
Langueen
DomaineImmunology and Microbiology
ThématiqueNeutrophil, Myeloperoxidase and Oxidative Mechanisms
Établissements canadiensThrombosis and Atherosclerosis Research InstituteUniversity of SaskatchewanMcMaster University
Organismes subventionnairesnon disponible
Mots-clésNeutrophil extracellular trapsDisseminated intravascular coagulationSepsisMedicineSystemic inflammatory response syndromeImmunologyCoagulationMyeloperoxidaseFibrinolysisOrgan dysfunctionInflammationPathologyInternal medicine

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction: Disseminated intravascular coagulation (DIC) is an acquired syndrome characterized by widespread intravascular activation of coagulation complicating many conditions including sepsis and traumatic injuries. Early recognition and treatment of DIC is of paramount importance. However, to date no useful markers have been identified that can differentiate "pre-DIC" (which is destined to lead to DIC) from "without-DIC" (in which the hypercoagulable state is transient and does not lead to DIC). Activation of neutrophils by inflammatory stimuli or microbes results in the release of neutrophil extracellular traps (NETs). NETs are web-like structures consisting of cell-free DNA (cfDNA), histones, myeloperoxidase (MPO), and anti-microbial proteins. Although NETs aid in the host response to infection by sequestering pathogens, excessive production of NETs can exert collateral damage to the host by activating coagulation, inhibiting fibrinolysis, and causing endothelial cell death. Recently, sepsis-induced DIC has been shown to correlate with circulating levels of DNA-associated MPO, suggesting that the release of NETs by neutrophils plays a critical role in the onset of DIC. Our objective was to attempt to identify a mechanistic role for NETosis in the development DIC in sepsis and use this information to identify 'pre-DIC' signatures. Methods: Clinical data and biological samples from 357 septic patients who were part of the DNA as a Prognostic Marker in ICU patient (DYNAMICS) study were used. Incidence of DIC was determined using the International Society on Thrombosis and Haemostasis (ISTH) scoring system on Day 1 and each subsequent day. We quantified levels of Citrullinated Histone H3 (H3Cit), a biomarker of NETosis, as well as levels of cfDNA. We also measured levels of Protein C (PC), a natural anticoagulant that prevents blood clotting in the microcirculation. Increased consumption of PC is a hallmark of sepsis and may lead to microvascular thrombosis and DIC. Results: Of the 357 patients included from the DYNAMICS study, 121 were classified as having DIC during the study period: 79 on Day 1 ('overt-DIC') and 42 on a subsequent day ('pre-DIC'). Baseline characteristics of patients are shown in Table 1. Those with DIC had significantly higher baseline APACHE II scores and were significantly more likely to be on vasopressors at admission or have a history of chronic liver disease. DIC was associated with significantly increased mortality (HR= 2.53; 95% CI = 1.62 - 3.93; p < 0.001) even when age and past medical history were controlled for. Levels of PC were significantly reduced in patients with DIC at all time points compared to those without DIC (p < 0.01). However, cfDNA levels did not differ between patients with and without DIC at any timepoint. As cfDNA may be released by multiple mechanisms and sources, H3 Cit was quantified on Day 1 as a marker of NETosis. Levels of H3 Cit on Day 1 were significantly higher in "pre-DIC" and "overt-DIC" patients compared to those 'without DIC' (p<0.05), non-septic, non-trauma ICU Controls (p<0.01), and healthy volunteers (p<0.001) (Figure 1). With respect to differentiating 'pre-DIC' from 'overt-DIC', using Day 1 H3 Cit provided an AUC of 0.66 (0.59-0.74). Higher H3 Cit levels were also correlated with lower PC levels in septic patients (r = -0.124; p = 0.02). In a comparison group of non-septic trauma patients also from the DYNAMICS study; (n=6) patients with DIC did not have significantly different Day 1 H3 Cit from (n=25) trauma patients without DIC (p=0.62) or ICU controls (p=0.99). Conclusion: In sepsis, DIC pathophysiology reflects a consumptive process as indicated by reduced PC levels. NETosis may contribute to this process by producing pro-coagulant stimuli and may prove useful in identifying patients who will develop DIC. As a regulated and targetable process investigations involving NETosis may yield therapies for early treatment of DIC in sepsis. Disclosures No relevant conflicts of interest to declare.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,004

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,021
Tête enseignante GPT0,248
Écart entre enseignants0,227 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2018
Routes d'admission1
Résumé présentoui

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