Shrinking Weibel‐Palade bodies prevents high platelet recruitment in assays using thrombotic thrombocytopenic purpura plasma
Bibliographic record
Abstract
BACKGROUND: Thrombotic thrombocytopenic purpura (TTP), caused by a genetic or autoimmune-driven lack of ADAMTS-13 activity, leads to high levels of the ultra-large von Willebrand factor (VWF) multimers produced by endothelial cells, causing excess platelet recruitment into forming thrombi, often with mortal consequences. Treatments include plasma infusion or replacement to restore ADAMTS-13 activity, or prevention of platelet recruitment to VWF. OBJECTIVES: We tested a different approach, exploiting the unique cell biology of the endothelium. Upon activation, the VWF released by exocytosis of Weibel-Palade bodies (WPBs), transiently anchored to the cell surface, unfurls as strings into flowing plasma, recruiting platelets. Using plasma from patients with TTP increases platelet recruitment to the surface of cultured endothelial cells under flow. WPBs are uniquely plastic, and shortening WPBs dramatically reduces VWF string lengths and the recruitment of platelets. We wished to test whether the TTP plasma-driven increase in platelet recruitment would be countered by reducing formation of the longest WPBs that release longer strings. METHODS: Endothelial cells grown in flow chambers were treated with fluvastatin, one of 37 drugs shown to shorten WPBs, then activated under flow in the presence of platelets and plasma of either controls or patients with TTP. RESULT: We found that the dramatic increase in platelet recruitment caused by TTP plasma is entirely countered by treatment with fluvastatin, shortening the WPBs. CONCLUSIONS: This potential approach of ameliorating the endothelial contribution to thrombotic risk by intervening far upstream of hemostasis might prove a useful adjunct to more conventional and direct therapies.
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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".