Procoagulant effects of lung cancer chemotherapy
Bibliographic record
Abstract
Lung cancer is the second leading type of cancer, with venous thromboembolism being the second leading cause of death. Studies have shown increased levels of microparticles and cell-free DNA (CFDNA) in cancer patients, which can activate coagulation through extrinsic and intrinsic pathways, respectively. However, the impact of lung cancer chemotherapy on microparticle and/or CFDNA generation is not completely understood. The aim of the study was to study the effects of platinum-based chemotherapeutic agents on generation of procoagulant microparticles and CFDNA in vitro and in vivo. Microparticles were isolated from chemotherapy-treated monocytes, human umbilical vein endothelial cells, or cancer cells. Tissue factor (TF) and phosphatidylserine levels were characterized and thrombin/factor Xa generation assays were used to determine microparticle procoagulant activity. CFDNA levels were isolated from cell supernatants and plasma. A murine xenograft model of human lung carcinoma was used to study the procoagulant effects of TF microparticles and CFDNA in vivo. In vitro, platinum-based chemotherapy induced TF/phosphatidylserine microparticle shedding from A549 and A427 lung cancers cells, which enhanced thrombin generation in plasma in a FVII-dependent manner. CFDNA levels were increased in supernatants of chemotherapy-treated neutrophils and plasma of chemotherapy-treated mice. TF microparticles were elevated in plasma of chemotherapy-treated tumour-bearing mice. Plasma CFDNA levels are increased in chemotherapy-treated tumour-free mice and correlate with increased thrombin generation. In tumour-bearing mice, chemotherapy increases plasma levels of CFDNA and TF/phosphatidylserine microparticles. Platinum-based chemotherapy induces the shedding of TF/phosphatidylserine microparticles from tumour cells and the release of CFDNA from host neutrophils.
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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".