Alteration of blood clotting and lung damage by protamine are avoided using the heparin and polyphosphate inhibitor UHRA
Bibliographic record
Abstract
Anticoagulant therapy-associated bleeding and pathological thrombosis pose serious risks to hospitalized patients. Both complications could be mitigated by developing new therapeutics that safely neutralize anticoagulant activity and inhibit activators of the intrinsic blood clotting pathway, such as polyphosphate (polyP) and extracellular nucleic acids. The latter strategy could reduce the use of anticoagulants, potentially decreasing bleeding events. However, previously described cationic inhibitors of polyP and extracellular nucleic acids exhibit both nonspecific binding and adverse effects on blood clotting that limit their use. Indeed, the polycation used to counteract heparin-associated bleeding in surgical settings, protamine, exhibits adverse effects. To address these clinical shortcomings, we developed a synthetic polycation, Universal Heparin Reversal Agent (UHRA), which is nontoxic and can neutralize the anticoagulant activity of heparins and the prothrombotic activity of polyP. Sharply contrasting protamine, we show that UHRA does not interact with fibrinogen, affect fibrin polymerization during clot formation, or abrogate plasma clotting. Using scanning electron microscopy, confocal microscopy, and clot lysis assays, we confirm that UHRA does not incorporate into clots, and that clots are stable with normal fibrin morphology. Conversely, protamine binds to the fibrin clot, which could explain how protamine instigates clot lysis and increases bleeding after surgery. Finally, studies in mice reveal that UHRA reverses heparin anticoagulant activity without the lung injury seen with protamine. The data presented here illustrate that UHRA could be safely used as an antidote during adverse therapeutic modulation of hemostasis.
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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) | 0.000 | 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".