Platelets in hemostasis and thrombosis: Novel mechanisms of fibrinogen-independent platelet aggregation and fibronectinmediated protein wave of hemostasis
Bibliographic record
Abstract
Platelets are small anucleate cells generated from megakaryocytes in the bone marrow. Although platelet generation, maturation, and clearance are still not fully understood, significant progress has been made in the last 1-2 decades. In blood circulation, platelets can quickly adhere and aggregate at sites of vascular injury, forming the platelet plug (i.e. the first wave of hemostasis). Activated platelets can also provide negatively charged phosphatidylserinerich membrane surface that enhances cell-based thrombin generation, which facilitates blood coagulation (i.e. the second wave of hemostasis). Platelets therefore play central roles in hemostasis. However, the same process of hemostasis may also cause thrombosis and vessel occlusion, which are the most common mechanisms leading to heart attack and stroke following ruptured atherosclerotic lesions. In this review, we will introduce the classical mechanisms and newly discovered pathways of platelets in hemostasis and thrombosis, including fibrinogen-independent platelet aggregation and thrombosis, and the plasma fibronectin-mediated "protein wave" of hemostasis that precedes the classical first wave of hemostasis. Furthermore, we briefly discuss the roles of platelets in inflammation and atherosclerosis and the potential strategies to control atherothrombosis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".