Enhancing compliance and extracellular matrix properties of tissue-engineered vascular grafts through pulsatile bioreactor culture
Bibliographic record
Abstract
Biofabrication techniques represent a promising avenue for the production of small diameter vascular grafts. However, while current tissue-engineered vascular grafts (TEVGs) fulfil certain functional requirements of native blood vessels, most exhibit very poor mechanical compliance, directly reducing patency in vivo. Here, highly compliant TEVGs were cultured in a dynamic pulsatile bioreactor which ensured enhanced compliance, using biomimetic melt electrowritten (MEW) tubular scaffolds as substrates for tissue growth. Through 6-week in vitro culture, we investigated differences in extracellular matrix (ECM) production and mechanical performance of TEVGs cultured with placental mesenchymal stem cells (MSCs) and smooth muscle cells (SMCs) in static and dynamic conditions. Pulsatile stimulation successfully maintained the high compliance (12.4 ± 0.8 % per 100 mmHg) of our biomimetic scaffolds, substantially greater than existing small diameter grafts. Dynamic TEVGs demonstrated physiologically relevant burst pressure (1125 ± 212 mmHg) and suture pull-out force (3.0 ± 0.4 N), while also accumulating greater ECM components than static TEVGs. To assess off-the-shelf suitability, grafts were decellularized and lyophilised to produce d-TEVGs, which exhibited negligible loss of mechanics or ECM integrity. Finally, rehydrated d-TEVGs were seeded with endothelial cells in vitro, with an intimal endothelial lining forming after 7 days. These findings demonstrate the production of TEVGs with specifically engineered mechanical compliance which has been maintained by dynamic in vitro culture, supporting continued work toward biofabrication of the next generation of vascular grafts.
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".