Substrate Topography Modulate Human Vascular Smooth Muscle Cell Proliferation and Phenotype in Proinflammatory Condition
Bibliographic record
Abstract
The clinical efficacy of “off the shelf” synthetic small-diameter vascular grafts (sSDVG) remains limited due to rapid occlusion from thrombosis and intimal hyperplasia (IH), driven by dysregulated vascular smooth muscle cell (VSMC) behavior after vascular injury. While luminal topography has been used to improve endothelialization, understanding the VSMC responses to topographies under proinflammatory conditions such as platelet-derived growth factor-BB (PDGF-BB) exposure is critical for improving sSDVG patency. We hypothesized topographies could modulate VSMC behavior, even with PDGF-BB stimulation, and that the responses would vary by feature size and shape. An initial screening of 16 micropatterns on polydimethylsiloxane identified five patterns for analyses of VSMC proliferation, phenotype switching (desmin-vimentin), and expression of α-smooth muscle actin (α-SMA) under normal and PDGF-stimulated conditions. The 2 μm grating reduced proliferation by half (49% to 24 ± 2%), doubled contractile phenotype expression (0.8% to 1.6 ± 0.11%), and elevated α-SMA expression. When incorporated into fucoidan-modified poly(vinyl alcohol) (PVA) hydrogels, previously shown to promote endothelialization, the 2 μm grating retained its ability to suppress PDGF-induced proliferation while enhancing contractile phenotype and directional motility. A 4-week in vivo study in a rabbit carotid artery model showed no increase in IH at anastomoses with 2 μm grating compared to unpatterned and ePTFE controls. Mechanistic studies showed the 2 μm gratings enhanced focal adhesion (FA) maturation, increased phosphorylated myosin light chain kinase (pMLCK) expression, and promoted cytoplasmic YAP localization, suggesting the topographical modulation of FA signaling promoted cytoskeletal contractility and reduced proliferation. Conversely, unpatterned and 1.8 μm convex lens substrates induced nuclear YAP and reduced pMLCK, favoring proliferation. These findings highlight that substrate topography, beyond aiding endothelialization, could also modulate VSMC responses in a proinflammatory environment, offering a promising strategy for improving sSDVG design.
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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".