The Effects of Biomimetic Surface Topography on Vascular Cells: Implications for Vascular Conduits
Bibliographic record
Abstract
Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide and represent a pressing clinical need. Vascular occlusions are the predominant cause of CVD and necessitate surgical interventions such as bypass graft surgery to replace the damaged or obstructed blood vessel with a synthetic conduit. Synthetic small-diameter vascular grafts (sSDVGs) are desired to bypass blood vessels with an inner diameter <6 mm yet have limited use due to unacceptable patency rates. The incorporation of biophysical cues such as topography onto the sSDVG biointerface can be used to mimic the cellular microenvironment and improve outcomes. In this review, the utility of surface topography in sSDVG design is discussed. First, the primary challenges that sSDVGs face and the rationale for utilizing biomimetic topography are introduced. The current literature surrounding the effects of topographical cues on vascular cell behavior in vitro is reviewed, providing insight into which features are optimal for application in sSDVGs. The results of studies that have utilized topographically-enhanced sSDVGs in vivo are evaluated. Current challenges and barriers to clinical translation are discussed. Based on the wealth of evidence detailed here, substrate topography offers enormous potential to improve the outcome of sSDVGs and provide therapeutic solutions for CVDs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".