A proteomic map of thromboinflammatory signatures in antiphospholipid syndrome: results from antiphospholipid syndrome alliance for clinical trials and international networking (APS ACTION) registry
Bibliographic record
Abstract
Introduction: Antiphospholipid syndrome (APS) is an autoimmune disease with thromboembolic and obstetric morbidity arising via a model of immunothrombosis. Individuals with APS may present with thrombotic (TAPS), obstetric (OAPS), or microvascular (MAPS) disease, while many have circulating antiphospholipid antibodies (aPL) without APS classification (NoAPS). Multiple pathophysiologic mechanisms have been proposed in APS, including activation by aPL of platelets, endothelial and immune cells, as well as complement and coagulation pathways; however, the pathophysiology of APS, particularly transition of clinical APS from aPL remains unclear. Methods: Seeking to define the inflammatory signature of APS, we carried out an unbiased proteomic screen of persistently aPL-positive patients with different clinical phenotypes from the international APS Alliance for Clinical Trials and International Networking (ACTION) Registry and compared them to 10 healthy controls. 6398 unique proteins were estimated using an DNA aptamer-based assay. Subsequently, we validated our findings in 34 additional patients. Results: Our data show that the mere presence of aPL confers a distinct thromboinflammatory signature characterized by the activation of coagulation, complement, innate and adaptive immune response pathways shared by all APS subtypes. Pathway enrichment analysis revealed increasing enrichment with rising statistical significance of thrombosis, complement, neutrophil and other innate and adaptive immune activation, as well as extracellular matrix (ECM) organization with increasing clinical severity, suggesting a model of progressive thromboinflammation in evolution of APS from NoAPS to TAPS and MAPS. Conclusions: Our findings provide novel insights into the pathogenesis of APS and identify potential novel targets for diagnostic and therapeutic intervention in APS across its entire spectrum.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".