Tumor-derived tissue factor activates coagulation and enhances thrombosis in a mouse xenograft model of human pancreatic cancer
Bibliographic record
Abstract
Cancer patients often have an activated clotting system and are at increased risk for venous thrombosis. In the present study, we analyzed tissue factor (TF) expression in 4 different human pancreatic tumor cell lines for the purpose of producing derivative tumors in vivo. We found that 2 of the lines expressed TF and released TF-positive microparticles (MPs) into the culture medium. The majority of TF protein in the culture medium was associated with MPs. Only TF-positive cell lines activated coagulation in nude mice, and this activation was abolished by an anti-human TF Ab. Of the 2 TF-positive lines, only one produced detectable levels of human MP TF activity in the plasma when grown orthotopically in nude mice. Surprisingly, < 5% of human TF protein in plasma from tumor-bearing mice was associated with MPs. Mice with TF-positive tumors and elevated levels of circulating TF-positive MPs had increased thrombosis in a saphenous vein model. In contrast, we observed no difference in thrombus weight between tumor-bearing and control mice in an inferior vena cava stenosis model. The results of the present study using a xenograft mouse model suggest that tumor TF activates coagulation, whereas TF on circulating MPs may trigger venous thrombosis.
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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 | 0.001 | 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 | 0.001 |
| 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".