Platelets and platelet adhesion molecules: novel mechanisms of thrombosis and anti-thrombotic therapies
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 are central mediators of thrombosis and hemostasis. At the site of vascular injury, platelet accumulation (i.e. adhesion and aggregation) constitutes the first wave of hemostasis. Blood coagulation, initiated by the coagulation cascades, is the second wave of thrombin generation and enhance phosphatidylserine exposure, can markedly potentiate cell-based thrombin generation and enhance blood coagulation. Recently, deposition of plasma fibronectin and other proteins onto the injured vessel wall has been identified as a new "protein wave of hemostasis" that occurs prior to platelet accumulation (i.e. the classical first wave of hemostasis). These three waves of hemostasis, in the event of atherosclerotic plaque rupture, may turn pathogenic, and cause uncontrolled vessel occlusion and thrombotic disorders (e.g. heart attack and stroke). Current anti-platelet therapies have significantly reduced cardiovascular mortality, however, on-treatment thrombotic events, thrombocytopenia, and bleeding complications are still major concerns that continue to motivate innovation and drive therapeutic advances. Emerging evidence has brought platelet adhesion molecules back into the spotlight as targets for the development of novel anti-thrombotic agents. These potential antiplatelet targets mainly include the platelet receptors glycoprotein (GP) Ib-IX-V complex, β3 integrins (αIIb subunit and PSI domain of β3 subunit) and GPVI. Numerous efforts have been made aiming to balance the efficacy of inhibiting thrombosis without compromising hemostasis. This mini-review will update the mechanisms of thrombosis and the current state of antiplatelet therapies, and will focus on platelet adhesion molecules and the novel anti-thrombotic therapies that target them.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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