Low molecular weight heparin and direct oral anticoagulants influence tumour formation, growth, invasion and vascularisation by separate mechanisms
Bibliographic record
Abstract
The bidirectional association between coagulation and cancer has been established. However, anticoagulant therapies have been reported to have beneficial outcomes by influencing the vascularisation of the tumours. In this study the influence of a set of anticoagulants on tumour formation, invasion and vascularisation was examined. WM-266-4 melanoma and AsPC-1 pancreatic cancer cell lines were treated with LMWH (Tinzaparin and Dalteparin), and DOAC (Apixaban and Rivaroxaban) and the rate of tumour formation, growth and invasion were measured in vitro. In addition, the influence of these anticoagulants on vascularisation was examined using the chorioallantoic membrane assay (CAM) model and compared to the outcome of treatment with Bevacizumab. Using this model the influence of pharmacological concentrations of the anticoagulant on the growth, invasion and vascularisation of tumours derived from WM-266-4 and AsPC-1 cells was also measured in vivo. Tinzaparin and Daltepain reduced tumour formation and invasion by the cell lines in vitro, but with dissimilar potencies. In addition, treatment of CAM with LMWH reduced the local vascular density beyond that achievable with Bevacizumab, particularly suppressing the formation of larger-diameter blood vessels. In contrast, treatment with DOAC was largely ineffective. Treatment of CAM-implanted tumours with LMWH also reduced tumour vascularisation, while treatment of tumours with Apixaban reduced tumour growth in vivo. In conclusion, LMWH and DOAC appear to have anti-cancer properties that are exerted through different mechanisms.
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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) | 0.002 | 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".