Cancer and platelet crosstalk: opportunities and challenges for aspirin and other antiplatelet agents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Platelets have long been recognized as key players in hemostasis and thrombosis; however, growing evidence suggests that they are also significantly involved in cancer, the second leading cause of mortality worldwide. Preclinical and clinical studies showed that tumorigenesis and metastasis can be promoted by platelets through a wide variety of crosstalk between platelets and cancer cells. For example, cancer changes platelet behavior by directly inducing tumor-platelet aggregates, triggering platelet granule and extracellular vesicle release, altering platelet phenotype and platelet RNA profiles, and enhancing thrombopoiesis. Reciprocally, platelets reinforce tumor growth with proliferation signals, antiapoptotic effect, and angiogenic factors. Platelets also activate tumor invasion and sustain metastasis via inducing an invasive epithelial-mesenchymal transition phenotype of tumor cells, promoting tumor survival in circulation, tumor arrest at the endothelium, and extravasation. Furthermore, platelets assist tumors in evading immune destruction. Hence, cancer cells and platelets maintain a complex, bidirectional communication. Recently, aspirin (acetylsalicylic acid) has been recognized as a promising cancer-preventive agent. It is recommended at daily low dose by the US Preventive Services Task Force for primary prevention of colorectal cancer. The exact mechanisms of action of aspirin in chemoprevention are not very clear, but evidence has emerged that suggests a platelet-mediated effect. In this article, we will introduce how cancer changes platelets to be more cancer-friendly and highlight advances in the modes of action for aspirin in cancer prevention. We also discuss the opportunities, challenges, and opposing viewpoints on applying aspirin and other antiplatelet agents for cancer prevention and treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it