The effects of chemotherapeutic agents on the regulation of thrombin on cell surfaces
Bibliographic record
Abstract
Thromboembolic disorders are common in cancer patients. Two major contributing factors are central venous catheters for drug delivery and the use of l-aparaginase, which decreases the plasma antithrombin level, but the causes of the hypercoagulable state in these patients are not fully understood. In this study, the T24/83 cell line was used as a model to investigate the effects of chemotherapeutic agents on cell surface thrombin regulation. Plasma thrombin generation and prothrombin consumption was increased in most of the treated cells, particularly vincristine- and adriamycin-treated cells (P < 0.05), compared with controls. However, no free thrombin generation or prothrombin consumption was observed in factor VII (FVII)-depleted plasma. No significant differences in the levels of thrombin-alpha2-macroglobulin (IIa-alpha2M) and thrombin-anti-thrombin (TAT) were observed between controls and any of the treatments, except for vincristine- and adriamycin-treated cells, which showed a significant difference in TAT production (P < 0.05). Also, there was an upregulation in tissue factor (TF) mRNA expression in etoposide-, methotrexate- and vincristine-treated monolayers compared with controls, as well as an upregulation in TF protein production in vincristine-treated cells. The data suggests that thrombin generation occurs via the extrinsic (TF-dependent) coagulation pathway on cell surfaces and that some chemotherapeutic agents are able to upregulate TF mRNA and protein expression in T24/83 cells.
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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".