Thrombosis‐Responsive Thrombolytic Coating Based on Thrombin‐Degradable Tissue Plasminogen Activator (t‐PA) Nanocapsules
Bibliographic record
Abstract
Abstract Surface modification with bioactive agents capable of combating thrombosis is a widely used strategy for developing antithrombotic biomaterials. However, exposure of the blood to the antithrombotic agent on the material surface may cause hemostatic disorders under normal conditions. Ideally an implanted biomaterial should respond appropriately on demand to a specific change in the physiologic environment, as happens in the body itself. In the present study, a thrombosis‐responsive surface coating with the ability to lyse fibrin as it forms is reported. The coating consists of nanocapsules (NCs) in which the fibrinolysis activator t‐PA is encapsulated in a thrombin‐degradable hydrogel shell. The t‐PA NCs are attached to several materials covalently through a polydopamine adhesive layer. The resulting surfaces are treated with the antifouling agent glutathione (GSH) to prevent further interactions with blood/plasma components. The t‐PA NCs/GSH‐coated surface is stable and remain inert in normal plasma environment while releasing t‐PA and promoting fibrinolysis when thrombin is present. The fibrinolytic activity increases with increasing thrombin concentration, and therefore presumably with the extent of thrombosis. This work constitutes the first report of an antithrombotic coating whose function is triggered and regulated, respectively, by the appearance of thrombin and the extent of coagulation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".