Aggregation‐Disruption‐Induced Multi‐Scale Mediating Strategy for Anticoagulation in Blood‐Contacting Devices
Bibliographic record
Abstract
Minimally invasive blood-contacting interventional devices are increasingly used to treat cardiovascular diseases. However, the risk of device-related thrombosis remains a significant concern, particularly the formation of cycling thrombi, which pose life-threatening risks. To better understand the interactions between these devices and blood, the initial stages of coagulation contact activation on extrinsic surfaces are investigated. Direct force measurements reveals that activated contact factors stimulate the intrinsic coagulation pathway and promote surface crosslinking of fibrin. Furthermore, fibrin aggregation is disrupted by surface-grafted inhibitors, as confirmed by ex vivo coagulation tests. An engineered serum protein with zwitterion grafts to resist the deposition of biological species such as fibrin, platelets, and red blood cells is also developed. Simultaneously, a protease inhibitor-based coacervate is incorporated into the coating to inhibit the intrinsic pathway effectively. The loaded coacervate can be released and reloaded through modulation of catechol-amine interactions, facilitating material regeneration. The strategy offers a novel multi-scale mediation strategy that simultaneously inhibits nanoscale coagulation factors and resists microscale thrombus aggregation, providing a long-term solution for anticoagulation in blood-contacting devices.
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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".