Thrombocytopathy leading to impaired in vivo haemostasis and thrombosis in platelet type von Willebrand disease
Bibliographic record
Abstract
Summary Platelet defects due to hyper-responsive GPIbα causing enhanced VWF interaction, counter-intuitively result in bleeding rather than thrombosis. The historical explanation of platelet/VWF clearance fails to explain mechanisms of impaired haemostasis particularly in light of reported poor platelet binding to fibrinogen. This study aimed to evaluate the defects of platelets with hyper-responsive GPIbα and their contribution to impaired in vivo thrombosis. Using the PT-VWD mouse model, platelets from the hTgG233V were compared to control hTgWT mice. Platelets’ pro-coagulant capacity was evaluated using flowcytometry assessment of P-selectin and annexin V. Whole blood platelet aggregation in response to ADP, collagen and thrombin was tested. Clot kinetics using laser injury thrombosis model and the effect of GPIbα inhibition in vivo using 6B4; a monoclonal antibody, were evaluated. Thrombin-induced platelet P-selectin and PS exposure were significantly reduced in hTgG233V compared to hTgWT and not signifi-cantly different when compared to unstimulated platelets. The hTgG233V platelets aggregated normally in response to collagen, and had a delayed response to ADP and thrombin, when compared to hTgWT platelets. Laser injury showed significant impairment of in vivo thrombus formation in hTgG233V compared to hTgWT mice. There was a significant lag in in vitro clot formation in turbidity assay but no impairment in thrombin generation was observed using thromboelastography. The in vivo inhibition of GPIbα facilitated new – unstable – clot formation but did not improve the lag. We conclude platelets with hyper-responsive GPIbα have complex intrinsic defects beyond the previously described mechanisms. Abnormal signalling through GPIbα and potential therapy using inhibitors require further investigations. Supplementary Material to this article is available online at www.thrombosis-online.com.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".