Sulfonated <scp>PEDOT</scp> ‐Modified Decellularized Arteries as Electroactive Scaffolds for Vascular Tissue Engineering
Bibliographic record
Abstract
Electroactive biomaterials present new opportunities for "smart" vascular grafts capable of supporting tissue integration while enabling electrical stimulation, sensing, or real-time modulation of the vascular environment. In this study, a conductive vascular conduit was engineered by incorporating sulfonated poly(3,4-ethylenedioxythiophene) (S'PEDOT) into extracellular matrix (ECM)-based scaffolds. Initial screening in collagen sponges identified S'PEDOT concentrations that supported biocompatibility with primary endothelial and smooth muscle cells while minimizing platelet adhesion. This strategy was then applied to decellularized rat aortas, which were functionalized with S'PEDOT and evaluated for electrical conductivity, tensile mechanics, and structural integrity. The modified grafts retained native architecture and mechanical compliance while exhibiting significantly enhanced conductivity compared to unmodified controls. In vivo biocompatibility was assessed by subcutaneous implantation in rats, followed by histological and immunohistochemical analyses. The S'PEDOT-modified grafts elicited minimal inflammatory response and preserved tissue architecture. These findings demonstrate a promising approach for integrating conductive polymers into natural scaffolds to develop electroactive vascular grafts, supporting future applications in multifunctional and responsive vascular 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) | 0.000 | 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".