Engineering of a Staphylokinase-based Fibrinolytic Agent with Antithrombotic Activity and Targeting Capability toward Thrombin-rich Fibrin and Plasma Clots
Bibliographic record
Abstract
Current clinically approved thrombolytic agents have significant drawbacks including reocclusion and bleeding complications. To address these problems, a staphylokinase-based thrombolytic agent equipped with antithrombotic activity from hirudin was engineered. Because the N termini for both staphylokinase and hirudin are required for their activities, a Y-shaped molecule is generated using engineered coiled-coil sequences as the heterodimerization domain. This agent, designated HE-SAKK, was produced and assembled from Bacillus subtilis via secretion using an optimized co-cultivation approach. After a simple in vitro treatment to reshuffle the disulfide bonds of hirudin, both staphylokinase and hirudin in HE-SAKK showed biological activities comparable with their parent molecules. This agent was capable of targeting thrombin-rich fibrin clots and inhibiting clot-bound thrombin activity. The time required for lysing 50% of fibrin clot in the absence or presence of fibrinogen was shortened 21 and 30%, respectively, with HE-SAKK in comparison with staphylokinase. In plasma clot studies, the HE-SAKK concentration required to achieve a comparable 50% clot lysis time was at least 12 times less than that of staphylokinase. Therefore, HE-SAKK is a promising thrombolytic agent with the capability to target thrombin-rich fibrin clots and to minimize clot reformation during fibrinolysis.
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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) | 0.000 | 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".