N-acetylcysteine reduces the size and activity of von Willebrand factor in human plasma and mice
Bibliographic record
Abstract
Thrombotic thrombocytopenic purpura (TTP) is a life-threatening disease characterized by systemic microvascular thrombosis caused by adhesion of platelets to ultra-large vWF (ULVWF) multimers. These multimers accumulate because of a deficiency of the processing enzyme ADAMTS13. vWF protein forms long multimers from homodimers that first form through C-terminal disulfide bonds and then join through their N termini by further disulfide bonding. N-acetylcysteine (NAC) is an FDA-approved drug that has long been used to treat chronic obstructive lung disease and acetaminophen toxicity and is known to function in the former disorder by reducing mucin multimers. Here, we examined whether NAC could reduce vWF multimers, which polymerize in a manner similar to mucins. In vitro, NAC reduced soluble plasma-type vWF multimers in a concentration-dependent manner and rapidly degraded ULVWF multimer strings extruded from activated ECs. The effect was preceded by reduction of the intrachain disulfide bond encompassing the platelet-binding A1 domain. NAC also inhibited vWF-dependent platelet aggregation and collagen binding. Injection of NAC into ADAMTS13-deficient mice led to the rapid resolution of thrombi produced by ionophore treatment of the mesenteric venules and reduced plasma vWF multimers. These results suggest that NAC may be a rapid and effective treatment for patients with TTP.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".