Shear stress and platelet‐induced tensile forces regulate ADAMTS13‐localization within the platelet thrombus
Bibliographic record
Abstract
BACKGROUND: The multimeric glycoprotein von Willebrand factor (VWF) mediates platelet adhesion and aggregation at the site of vessel injury. The adhesive activity of VWF is influenced by its multimer length which is regulated by the metalloprotease ADAMTS13. The ability of ADAMTS13 to regulate platelet thrombus growth in a shear-dependent manner has been described, however, the mechanistic basis of this action has not been well characterized. METHODS: We developed an mCherry-tagged murine ADAMTS13 protein and utilized an ex vivo flow chamber system to visualize the localization of ADAMTS13 within the platelet thrombus under different conditions of shear. Using this system, we also assessed the influence of platelet-mediated tensile force on ADAMTS13 localization within the thrombus using gain-of-function GPIb binding and loss-of-function GPIIbIIIa binding mutants in VWF/ADAMTS13 DKO mice. RESULTS: , platelet-mediated tensile force, involving GPIb but not GPIIbIIIa receptors, influenced localization of ADAMTS13 to the thrombus under conditions of shear. CONCLUSIONS: Tensile force applied on VWF produced by shear stress and platelet GPIb binding has a crucial role in ADAMTS13 activity at the site of thrombus formation. These results suggest that ADAMTS13 activity at the site of platelet thrombus formation is regulated by a shear stress and platelet-dependent feedback mechanism to prevent vessel occlusion and pathological thrombosis.
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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 | 0.000 |
| 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".