DNASE-MEDIATED DISSOLUTION OF NEUTROPHIL EXTRACELLULAR TRAPS ACCELERATES IN VITRO THROMBIN GENERATION KINETICS IN TRAUMA PATIENTS
Bibliographic record
Abstract
ABSTRACT: Introduction: Neutrophil extracellular traps (NETs) trigger thrombin generation. We aimed to characterize the effects of deoxyribonuclease (DNAse) on NET components (cell-free DNA [cfDNA] and histones) and thrombin generation after trauma. Methods: Citrated plasma samples were collected from trauma patients and healthy volunteers. Thrombin generation (calibrated automated thrombogram) was measured as lag time (LT, in minutes), peak height (in nM), and time to peak thrombin generation (in minutes). Citrullinated histone 3 (CitH3) and 4 (CitH4) were measured by enzyme-linked immunosorbent assay; cfDNA by PicoGreen (all in nanograms per milliliter). Samples analyzed +/- DNAse (1,000 U/mL). Results expressed as median and quartiles [Q1, Q3], Wilcoxon testing, P < 0.05 significant. Results: We enrolled 46 patients (age, 48 [31, 67] years; 67% male) and 21 volunteers (age, 45 [28, 53] years; 43% male). Deoxyribonuclease treatment of trauma plasma led to shorter LT (3.11 [2.67, 3.52] min; 2.93 [2.67, 3.19] min), shorter time to peak thrombin generation (6.00 [5.30, 6.67] min; 5.48 [5.00, 6.00] min), greater peak height (273.7 [230.7, 300.5] nM; 288.7 [257.6, 319.2] nM), decreased cfDNA (576.9 [503.3, 803.1] ng/mL; 456.0 [393.5, 626.7] ng/mL), decreased CitH3 (4.54 [2.23, 10.01] ng/mL; 3.59 [1.93, 7.98] ng/mL), and increased H4 (1.30 [0.64, 6.36] ng/mL; 1.75 [0.83, 9.67] ng/mL), all P < 0.001. The effect of DNAse was greater on trauma patients as compared with volunteers for LT (ΔLT, -0.21 vs. -0.02 min, P = 0.007), cfDNA (ΔcfDNA -133.4 vs. -84.9 ng/mL, P < 0.001), and CitH3 (ΔCitH3, -0.65 vs. -0.11 ng/mL, P = 0.004). Conclusion: Deoxyribonuclease treatment accelerates thrombin generation kinetics in trauma patient samples as compared with healthy volunteers. These findings suggest that NETs may contribute to the hypercoagulable state observed in trauma patients.
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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".