Effect of covalent antithrombin-heparin on activated protein C inactivation by protein C inhibitor
Bibliographic record
Abstract
Protein C in its activated form (APC) limits thrombin generation. Protein C inhibitor (PCI) readily neutralizes APC. Heparin accelerates this reaction, which may complicate anticoagulant treatment in patients with varying APC generation potential. A potent anticoagulant conjugate of antithrombin and heparin (ATH) was prepared, and its effect on APC+PCI reactions was tested. Second order rate constants for APC+PCI reactions were measured by discontinuous rate experiments in the presence of heparin or ATH. Similarly, low molecular weight fractions of heparin (LMWH) and ATH (LMWATH) were tested, as was high molecular weight ATH (HMWATH). Mechanisms of heparin or ATH binding to APC or PCI were assessed using electrophoresis. While heparin gave a higher maximal APC inhibition rate compared to ATH, peak inhibition rate was achieved at comparatively lower ATH concentrations. Since LMWH was ineffective at enhancing APC inhibition by PCI, unfractionated heparin likely acts by bridging APC and PCI. Unlike heparin, ATH may conformationally activate either APC or PCI since LMWATH significantly catalyses APC inhibition. Binding studies showed that ATH readily associates with APC. Thus, although a small fraction of ATH efficiently catalyses APC inhibition by PCI, complete ATH preparations induce a decreased maximal rate of APC-PCI formation compared to unfractionated heparin.
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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 | 0.001 |
| 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) | 0.002 | 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".