Platelet desialylation correlates with efficacy of first-line therapies for immune thrombocytopenia
Bibliographic record
Abstract
Immune thrombocytopenia (ITP) is a common autoimmune bleeding disorder. Despite considerable investigation, the pathogenesis of ITP remains incompletely understood, and for many patients, effective therapy is still unavailable. Using murine models and in vitro studies of human blood samples, we recently identified a novel Fc-independent platelet clearance pathway, whereby antibody-mediated desialylated platelets can be cleared in the liver via asialoglycoprotein receptors, leading to decreased response to standard first-line therapies targeting Fc-dependent platelet clearance. Here, we evaluated the significance of this finding in 61 ITP patients through correlation of levels of platelet desialylation with the efficacy of first-line therapies. We found that desialylation levels between different responses to treatment groups were statistically significant (p < 0.01). Importantly, correlation analysis indicated response to treatment and platelet desialylation were related (p < 0.01), whereby non-responders had significantly higher levels of platelet desialylation. Interestingly, we also found secondary ITP and certain non-ITP thrombocytopenias also exhibited significant platelet desialylation compared to healthy controls. These findings designate platelet desialylation as an important biomarker in determining response to standard treatment for ITP. Furthermore, we show for the first time platelet desialylation in other non-ITP thrombocytopenias, which may have important clinical implications and deserve further investigation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".