Fucoidan and topography modification improved in situ endothelialization on acellular synthetic vascular grafts
Bibliographic record
Abstract
Thrombogenesis remains the primary failure of synthetic vascular grafts. Endothelial coverage is crucial to provide an antithrombogenic surface. However, most synthetic materials do not support cell adhesion, and transanastomotic endothelial migration is limited. Here, a surface modification strategy using fucoidan and topography was developed to enable fast in situ endothelialization of polyvinyl alcohol, which is not endothelial cell-adhesive. Among three different immobilization approaches compared, conjugation of aminated-fucoidan promoted endothelial monolayer formation while minimizing thrombogenicity in both in vitro platelet rich plasma testing and ex vivo non-human primate shunt assay. Screening of six topographical patterns showed that 2 μm gratings increased endothelial cell migration without inducing inflammation responses of endothelial cells. Mechanistic studies demonstrated that fucoidan could attract fibronectin, enabling integrin binding and focal adhesion formation and activating focal adhesion kinase (FAK) signaling, and 2 μm gratings further enhanced FAK-mediated cell migration. In a clinically relevant rabbit carotid artery end-to-side anastomosis model, 60% in situ endothelialization was observed throughout the entire lumen of 1.7 mm inner diameter modified grafts, compared to 0% of unmodified graft, and the four-week graft patency also increased. This work presents a promising strategy to stimulate in situ endothelialization on synthetic materials for improving long-term performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".