Platelet factor 4 triggers thrombo‐inflammation by bridging innate and adaptive immunity
Bibliographic record
Abstract
Platelet factor 4 (PF4, synonym: CXCL4) is an evolutionary old chemokine with proposed roles in hemostasis and antimicrobial defense. In addition, PF4 has attracted considerable attention as a crucial mediator of one of the most prothrombotic adverse drug effects affecting blood cells, heparin-induced thrombocytopenia (HIT). Interest in PF4 substantially increased in 2021 when it was identified as the target antigen in the life-threatening adverse effect, vaccine-induced immune thrombotic thrombocytopenia (VITT). We address the concept that a major biological function of PF4-a strongly cationic chemokine-is to bind to negatively-charged prokaryotic microorganisms, resulting in structural changes in PF4 that trigger a danger signal recognized by the adaptive immune system. Application of biophysical tools has provided substantial insights into the molecular mechanisms by which PF4 becomes immunogenic, providing insights into a new mechanism of autoimmunity. Binding of autoantibodies with high affinity induces conformational change(s) in the endogenous protein, which are then recognized as foreign antigen, as exemplified by the prothrombotic disorders, autoimmune HIT and VITT. The final part of our review summarizes current assays for HIT and VITT, explaining how structural aspects of anti-PF4 pathobiology relate to assay design and performance characteristics. Currently, functional (platelet activation) assays using washed platelets detect HIT antibodies when heparin is added, and VITT antibodies when PF4 is added. Solid-phase PF4-dependent immunoassays using microtiter plates are sensitive for both HIT and VITT antibodies, while rapid immunoassays, in which the PF4/heparin antigen is coated on beads, are sensitive and specific for HIT, but not for VITT antibodies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".