Platelets and neutrophil extracellular traps collaborate to promote intravascular coagulation during sepsis in mice
Bibliographic record
Abstract
Neutrophil extracellular traps (NETs; webs of DNA coated in antimicrobial proteins) are released into the vasculature during sepsis where they contribute to host defense, but also cause tissue damage and organ dysfunction. Various components of NETs have also been implicated as activators of coagulation. Using multicolor confocal intravital microscopy in mouse models of sepsis, we observed profound platelet aggregation, thrombin activation, and fibrin clot formation within (and downstream of) NETs in vivo. NETs were critical for the development of sepsis-induced intravascular coagulation regardless of the inciting bacterial stimulus (gram-negative, gram-positive, or bacterial products). Removal of NETs via DNase infusion, or in peptidylarginine deiminase-4-deficient mice (which have impaired NET production), resulted in significantly lower quantities of intravascular thrombin activity, reduced platelet aggregation, and improved microvascular perfusion. NET-induced intravascular coagulation was dependent on a collaborative interaction between histone H4 in NETs, platelets, and the release of inorganic polyphosphate. Real-time perfusion imaging revealed markedly improved microvascular perfusion in response to the blockade of NET-induced coagulation, which correlated with reduced markers of systemic intravascular coagulation and end-organ damage in septic mice. Together, these data demonstrate, for the first time in an in vivo model of infection, a dynamic NET-platelet-thrombin axis that promotes intravascular coagulation and microvascular dysfunction in sepsis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".