Tissue Engineering a Small Diameter Vessel Substitute: Engineering Constructs with Select Biomaterials and Cells
Bibliographic record
Abstract
Cardiovascular disease (CVD) is a leading cause of death and hospitalization worldwide. The need for small caliber vessels ( < 6mm) to treat CVD patients has grown; however the availability of autologous vessels in cardiac and peripheral bypass candidates is limited. The search for an alternative vessel source is widespread with both natural and synthetic tissue engineered materials being investigated as scaffolds. Despite decades of exhaustive studies with decellularized extracellular matrices (ECM) and synthetic graft materials, the field remains in search of a commercially viable biomaterial construct substitute. While the previous materials have been assessed by evaluating their compatibility with fibroblasts, smooth muscle cells and endothelial cells, current materials are being conceived based on their interactions with stem cells, progenitor cells and monocytes, as the latter may hold the key to repair and regeneration. The graft's ability to recruit and maintain these cells has become a major research focus. The successful tissue engineering of a small caliber vessel graft requires the use of optimal material chemistry and biological function to promote cell recruitment into the graft while maintaining each functional phenotype during vessel tissue maturation. The discussion of these significant research challenges constitutes the focus of this review.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.002 |
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".