Cell-Free DNA Modulates Clot Structure and Impairs Fibrinolysis in Sepsis
Bibliographic record
Abstract
OBJECTIVES: Sepsis is characterized by systemic activation of inflammation and coagulation in response to infection. In sepsis, activated neutrophils extrude neutrophil extracellular traps composed of cell-free DNA (CFDNA) that not only trap pathogens but also provide a stimulus for clot formation. Although the effect of CFDNA on coagulation has been extensively studied, much less is known about the impact of CFDNA on fibrinolysis. To address this, we (1) investigated the relationship between CFDNA levels and fibrinolytic activity in sepsis and (2) determined the mechanisms by which CFDNA modulates fibrinolysis. APPROACH AND RESULTS: Plasma was collected from healthy and septic individuals, and CFDNA was quantified. Clot lysis assays were performed in plasma and purified systems, and lysis times were determined by monitoring absorbance. Clot morphology was assessed using scanning electron microscopy. Clots formed in plasma from septic patients containing >5 µg/mL CFDNA were dense in structure and resistant to fibrinolysis, a phenomenon overcome by deoxyribonuclease addition. These effects were recapitulated in control plasma supplemented with CFDNA. In a purified system, CFDNA delayed fibrinolysis but did not alter tissue-type plasminogen activator-induced plasmin generation. Using surface plasmon resonance, CFDNA bound plasmin with a Kd value of 4.2±0.3 µmol/L, and increasing concentrations of CFDNA impaired plasmin-mediated degradation of fibrin clots via the formation of a nonproductive ternary complex between plasmin, CFDNA, and fibrin. CONCLUSIONS: Our studies suggest that the increased levels of CFDNA in sepsis impair fibrinolysis by inhibiting plasmin-mediated fibrin degradation, thereby identifying CFDNA as a potential therapeutic target for sepsis treatment.
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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 | 0.001 |
| 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) | 0.002 | 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".