Platelets in Thrombosis and Hemostasis: Old Topic with New Mechanisms
Bibliographic record
Abstract
Platelets are small anucleate cells generated from megakaryocytes in the bone marrow. After being released into the circulation, platelets play key roles in the surveillance of vascular injury, and can quickly adhere and aggregate at the site of injury, which are critical events for vascular repair and hemostasis. However, the same biological processes of platelet adhesion and aggregation may also cause thrombotic disorders. The formation of a platelet plug at sites of atherosclerotic lesion rupture is the most common mechanism leading to myocardial or cerebral infarction. Platelet-related deep vein thrombosis is also one of the leading causes of mortality worldwide. The contribution of several platelet receptors and their ligands has been highlighted in these processes. In platelet adhesion, particularly at high shear stress, GPIbα-von Willebrand factor (VWF) interaction may initiate this event, which is followed by GPVI signalling and firm platelet adhesion mediated by members of the integrin family, such as β3 (αIIbβ3) and β1 (α2β1, α5β1) integrins. In platelet aggregation, although GPIbα-VWF, P selectin-sulfatides, and other molecules, may be involved, the process is mainly mediated by β3 (αIIbβ3) integrin and its ligands, such as fibrinogen and VWF. It is intriguing that platelet adhesion and aggregation still occur in mice lacking both fibrinogen and VWF, suggesting that other unforeseen molecule(s) may also be important in these processes. Identification and characterization of these molecules will enrich our knowledge in the basic science of hemostasis and thrombosis, and may lead to the development of new therapies against bleeding disorders and thrombotic diseases.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".