Decreased von Willebrand factor protease activity associated with thrombocytopenic disorders
Bibliographic record
Abstract
Recent studies investigating thrombotic thrombocytopenic purpura (TTP) have implicated abnormal plasma von Willebrand factor (vWF)-cleaving metalloprotease activity in this disorder. It has been proposed that a metalloprotease cleaves unusually large (UL) multimers of vWF, which enter the circulation from the endothelium. Abnormal metalloprotease activity could result in ULvWF, which could participate in TTP. However, the diagnostic specificity of abnormalities in the plasma metalloprotease activity has not been established. A prospective study of vWF protease activity was performed using samples from 20 healthy controls, 20 patients with acute TTP, 20 patients with immune idiopathic thrombocytopenic purpura (ITP), 10 patients with disseminated intravascular thrombocytopenia (DIC), 10 patients with systemic lupus erythematosus (SLE,) and 5 thrombocytopenic patients with leukemia. Studies were performed blinded to the diagnosis. Samples from hospitalized patients with normal platelet counts were also tested. The vWF digests and multimer analysis were done using previously described methods. Six laboratory personnel independently scored each of the multimer gels. Reduced protease activity was observed in 9 of 20 patients with TTP. Reduced activity was also observed in 6 of 20 patients with ITP, 6 of 10 patients with DIC, 5 of 10 patients with SLE, 1 of 5 patients with leukemia, 2 of 20 healthy controls, and 3 of 25 hospitalized patients. This study indicates that abnormalities of vWF protease activity are not restricted to patients with the diagnosis of TTP.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".