Platelet–Cancer Interactions
Bibliographic record
Abstract
Platelets play a crucial role in the pathophysiological processes of hemostasis and thrombosis. Increasing evidence indicates that they fulfill much broader roles in balancing health and disease. The presence of tumor cells affects platelets both numerically, through a wide variety of mediators and cytokines, or functionally through tumor cell-induced platelet activation, the first step toward cancer-induced thrombosis. This induction results from signaling events through the different platelet receptors, or may be cytokine-mediated. Reciprocally, upon activation, the platelets will release a myriad of growth factors from their dense and α-granules and peroxisomes; these will directly impact tumor growth, tethering, and spread. A similar cross-talk is initiated between tumor microvesicles stimulating the platelets and platelet microparticles, promoting both thrombosis and tumor growth. A vicious loop of activation thereafter takes place. Platelets directly and indirectly promote tumor growth, and enable a molecular mimicry coating the malignant growth and allowing metastasizing cells to escape T-cell-mediated immunity and natural killer cell surveillance. Breaking this vicious activation loop with nonspecific platelet inhibitors, such as aspirin, or by targeting specific sites on the activation cascade may offer a mean to reduce both the risks of development and progression of cancer and the risk of 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".