Hemocompatibility of micropatterned biomaterial surfaces is dependent on topographical feature size
Bibliographic record
Abstract
Small-diameter synthetic vascular grafts that have improved hemocompatibility and patency remain an unmet clinical need due to thrombosis. A surface modification that has potential to attenuate these failure mechanisms while promoting an endothelial layer is the micropatterning of luminal surfaces. Anisotropic features have been shown to downregulate smooth muscle cell proliferation, direct endothelial migration, and attenuate platelet adhesion and activation. However, the effect of micropatterning feature size and orientation relative to whole blood flow has yet to be investigated within a systematic study. In this work, hemocompatibility of micropattern grating sizes of 2, 5, and 10 µm were investigated. The thrombogenicity of the micropattern surface modifications were characterized by quantifying FXIIa activity, fibrin formation, and static platelet adhesion in vitro. Additionally, dynamic platelet attachment and end-point fibrin formation were quantified using an established, flowing whole blood ex vivo non-human primate shunt model without antiplatelet or anticoagulant therapies. We observed a higher trend in platelet attachment and significantly increased fibrin formation for larger features. We then investigated the orientation of 2 µm gratings relative to whole blood flow and found no significant differences between the various orientations for platelet attachment, rate of linear platelet attachment, or end-point fibrin formation. MicroCT analysis of micropatterned grafts was utilized to quantify luminal patency. This work is a significant step in the development of novel synthetic biomaterials with improved understanding of hemocompatibility for use in cardiovascular applications.
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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) | 0.001 | 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".