Human endothelial colony-forming cells expanded with an improved protocol are a useful endothelial cell source for scaffold-based tissue engineering
Bibliographic record
Abstract
One of the major challenges in tissue engineering is to supply larger three-dimensional (3D) bioengineered tissue transplants with sufficient amounts of nutrients and oxygen and to allow metabolite removal. Consequently, artificial vascularization strategies of such transplants are desired. One strategy focuses on endothelial cells capable of initiating new vessel formation, which are settled on scaffolds commonly used in tissue engineering. A bottleneck in this strategy is to obtain sufficient amounts of endothelial cells, as they can be harvested only in small quantities directly from human tissues. Thus, protocols are required to expand appropriate cells in sufficient amounts without interfering with their capability to settle on scaffold materials and to initiate vessel formation. Here, we analysed whether umbilical cord blood (CB)-derived endothelial colony-forming cells (ECFCs) fulfil these requirements. In a first set of experiments, we showed that marginally expanded ECFCs settle and survive on different scaffold biomaterials. Next, we improved ECFC culture conditions and developed a protocol for ECFC expansion compatible with 'Good Manufacturing Practice' (GMP) standards. We replaced animal sera with human platelet lysates and used a novel type of tissue-culture ware. ECFCs cultured under the new conditions revealed significantly lower apoptosis and increased proliferation rates. Simultaneously, their viability was increased. Since extensively expanded ECFCs could still settle on scaffold biomaterials and were able to form tubular structures in Matrigel assays, we conclude that these ex vivo-expanded ECFCs are a novel, very potent cell source for scaffold-based tissue engineering.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".