Beyond Hemostasis: The Role of Platelets in Inflammation, Malignancy and Infection
Bibliographic record
Abstract
Platelets play a complex role in hemostasis and thrombosis. The expression of multiple membrane receptors, both constitutive and activation-dependent, mediates platelet adhesion and aggregation at sites of vascular lesion. Platelet activation leads to exocytosis of granular constituents, release of newly synthesized mediators, and discharge of membrane-bound transcellular signaling molecules. Many of the same mechanisms that play a role in hemostasis and thrombosis facilitate platelet participation in other physiological or pathological processes including inflammation, malignancy and the immune response. Platelet receptors such as GPIb/IX/V, P-selectin, P-selectin glycoprotein ligand 1, CD40 and the alphaIIbbeta3 integrin, crucial to hemostasis, have been implicated in the progression of such inflammatory conditions as atherosclerosis, rheumatoid arthritis and inflammatory bowel disease, in the progression and metastatic spread of malignancies, and in the immune response to bacterial challenge. The release of platelet granular contents, including adhesive proteins, growth factors and chemokines/cytokines, that serve to facilitate hemostasis and wound repair, also function in acute and chronic inflammatory disease and in tumor cell activation and growth. Platelets contribute to host defence as they recognise bacteria, recruit traditional immune cells to the site of infection and secrete bactericidal mediators. The primary focus of this review is the "non-haemostatic" functions of platelets in physiological and pathological states.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".