Intravascular Targeting of a New Anticoagulant Heparin Compound
Bibliographic record
Abstract
Since most thrombotic reactions occur on the vessel wall, interaction of anticoagulants with vascular components is critical. Heparin (H) is the primary drug for treatment and prevention of thrombosis. To improve H's efficacy and bioavailability, a covalent complex of H and its biological target, antithrombin (AT), was developed. While H has a short, variable intravenous half-life leading to unpredictable anticoagulation, clearance of covalent ATH complex is slower. H's variable anticoagulant effect arises from interactions with plasma and vessel wall proteins. ATH has increased bioavailability due to lower plasma protein and endothelial binding relative to H. Pharmacodynamic studies demonstrate that the AT moiety can regulate ATH binding to target tissues. For example, blood vessel binding is enhanced using ATH containing recombinant AT with oligomannose structures that can interact with endothelial mannose receptor lectins. Furthermore, recent work has shown that inhibition of factor VIIa/tissue factor complex by ATH is significantly faster than AT + H. Similarly, thrombin bound to endothelial thrombomodulin is inhibited more efficiently by ATH than by AT + H, which might improve regulation of thrombin generation. Overall, linking H to AT may prevent unwanted protein interactions and allow vessel wall sites to be targeted. This review examines ATH biodistribution.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".