Neutrophil extracellular traps (NETs) promote disseminated intravascular coagulation in sepsis.
Bibliographic record
Abstract
Abstract Neutrophil extracellular traps (NETs; webs of DNA coated in anti-microbial proteins) are released into the vasculature during sepsis and contribute to organ damage. Various components of NETs have been shown to modulate the coagulation cascade in vitro. We hypothesized that NETs released into the vasculature during sepsis activate intravascular coagulation, leading to microvascular hypoperfusion and end-organ damage. Multi-color confocal intravital microscopy was used in mouse models of sepsis (endotoxemia and E. coli peritonitis) to visualize and quantify neutrophil and platelet trafficking, NETs production, real-time perfusion analysis, and intravascular thrombin activity (using novel in vivo zymography). In vivo imaging of the liver microcirculation in septic mice revealed spatial co-localization between NETs and intravascular coagulation (thrombin activity, fibrin deposition, and platelet aggregation). Inhibition of NETs using i.v. DNase infusion (to digest NETs), or PAD4-deficient mice (that have impaired NETs production) resulted in significantly lower quantities of intravascular thrombin activity, and improved microvascular perfusion. In a model of Gram-negative sepsis (E. coli peritonitis), PAD4-deficient mice had reduced markers of disseminated intravascular coagulation (intravascular thrombin, PAI-1), and end-organ damage (serum lactate, ALT) compared to wild-type controls. Together, these data demonstrate that NETs activate coagulation in vivo, and promote disseminated intravascular coagulation in sepsis. Inhibition of NETs in septic animals reduces intravascular coagulation, improves microvascular perfusion, and attenuates end-organ damage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".