The value of proteomics for the diagnosis of a platelet-related bleeding disorder
Bibliographic record
Abstract
Familial bleeding problems are frequently difficult to diagnose because currently used clinical tests cannot identify intracellular molecular defects of platelets. Using platelet proteomics, a comprehensive analytical tool, we diagnosed a family with severe bleeding problems of unknown origin with Quebec Platelet Disorder. Prior to proteomic analysis, we determined platelet counts, presence of glycoprotein (GP) Ib and GPIIb/IIIa, platelet aggregation, dense granule content and release, plasma levels of fibrinogen, Factor XIII and fibrin degradation products in four family members. Abnormalities were detected in platelet aggregation studies, which revealed variably reduced responses to ADP, collagen and epinephrine with concomitantly decreased ATP/serotonin secretion. In addition, D-dimer levels were significantly elevated 72 hours after in vitro thrombin stimulation of platelet-rich plasma. Together with the autosomal dominant inheritance and the delayed onset of bleeding in two of the four patients these results did not support any known platelet disorder. Therefore, the proteome of platelet lysates separated by one-dimensional SDS-PAGE was analysed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Platelet proteomics showed reduced amounts of alpha-granule proteins multimerin, fibrinogen and thrombospondin-1 in patient compared to control samples suggestive of Quebec Platelet Disorder. The diagnosis of Quebec Platelet Disorder was confirmed by urokinase-specific Western blots. Urokinase causes the degradation of alpha-granule proteins in this disorder. Diagnosis of rare bleeding disorders has important implications for prophylactic and acute treatment of bleeding patients. This is the first report using proteomics to identify a familial platelet defect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".