Characterization of large in-frame von Willebrand factor deletions highlights differing pathogenic mechanisms
Bibliographic record
Abstract
Copy number variation (CNV) is known to cause all von Willebrand disease (VWD) types, although the associated pathogenic mechanisms involved have not been extensively studied. Notably, in-frame CNV provides a unique opportunity to investigate how specific von Willebrand factor (VWF) domains influence the processing and packaging of the protein. Using multiplex ligation-dependent probe amplification, this study determined the extent to which CNV contributed to VWD in the Molecular and Clinical Markers for the Diagnosis and Management of Type 1 von Willebrand Disease cohort, highlighting in-frame deletions of exons 3, 4-5, 32-34, and 33-34. Heterozygous in vitro recombinant VWF expression demonstrated that, although deletion of exons 3, 32-34, and 33-34 all resulted in significant reductions in total VWF (P < .0001, P < .001, and P < .01, respectively), only deletion of exons 3 and 32-34 had a significant impact on VWF secretion (P < .0001). High-resolution microscopy of heterozygous and homozygous deletions confirmed these observations, indicating that deletion of exons 3 and 32-34 severely impaired pseudo-Weibel-Palade body (WPB) formation, whereas deletion of exons 33-34 did not, with this variant still exhibiting pseudo-WPB formation similar to wild-type VWF. In-frame deletions in VWD, therefore, contribute to pathogenesis via moderate or severe defects in VWF biosynthesis and secretion.
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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 | 0.001 |
| 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) | 0.001 | 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".