Advancing tissue-engineered vascular grafts via their endothelialization and mechanical conditioning
Bibliographic record
Abstract
Tissue engineering has garnered significant attention for its potential to address the predominant modes of failure of small diameter vascular prostheses, namely mid-graft thrombosis and anastomotic intimal hyperplasia. In this review, we described two main features underpinning the promise of tissue-engineered vascular grafts: the incorporation of an antithrombogenic endothelium, and the generation of a structurally and biomechanically mimetic extracellular matrix. From the early attempts at the in-vitro endothelialization of vascular prostheses in the 1970s through to the ongoing clinical trials of fully tissue-engineered vascular grafts, the historical advancements and unresolved challenges that characterize the current state-of-the-art are summarized in a manner that establishes a guide for the development of an effective vascular prosthesis for small diameter arterial reconstruction. The importance of endothelial cell purity and their arterial specification for the prevention of both diffuse neointimal hyperplasia and the accelerated development of atherosclerotic lesions is delineated. Additionally, the need for an extracellular matrix that recapitulates both the composition and structure of native elastic arteries to facilitate the protracted stability and patency of an engineered vasoactive conduit is described. Finally, the capacity of alternative sources of cells and mechanical conditioning to overcome these technical barriers to the clinical translation of an effective small diameter vascular prosthesis is discussed. In conclusion, this review provides an overview of the historical development of tissue-engineered vascular grafts, highlighting specific areas warranting further research, and commentating on the outlook of a clinically feasible and therapeutically efficacious vascular prosthesis for small diameter arterial reconstruction.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".