Update on platelet procoagulant mechanisms in health and in bleeding disorders
Bibliographic record
Abstract
Platelet procoagulant mechanisms are emerging to be complex and important to achieving haemostasis. The mechanisms include the release of procoagulant molecules from platelet storage granules, and strong agonist-induced expression of procoagulant phospholipids on the outer platelet membrane for tenase and prothrombinase assembly. The release of dense granule polyphosphate is important to platelet procoagulant function as it promotes the activation of factors XII, XI and V, inhibits tissue factor pathway inhibitor and fibrinolysis, and strengthens fibrin clots. Platelet procoagulant function also involves the release of partially activated factor V from platelets. Scott syndrome has provided important insights on the mechanisms that regulate procoagulant phospholipids expression on the external platelet membrane, which require strong agonist stimulation that increase cystolic calcium levels, mitochondrial calcium uptake, the loss of flippase function and activation of the transmembrane scramblase protein anoctamin 6. There have been advances in the methods used to directly and indirectly assess platelet procoagulant function in health and disease. Assessments of thrombin generation with platelet rich plasma samples has provided new insights on how platelet procoagulant function is altered in inherited platelet disorders, and how platelets influence the bleeding phenotype of a number of severe coagulation factor deficiencies. Several therapies, including desmopressin and recombinant factor VIIa, improve thrombin generation by platelets. There is growing interest in targeting platelet procoagulant function for therapeutic benefit. This review highlights recent advances in our understanding of platelet-dependent procoagulant mechanisms in health and in bleeding disorders.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".