Tissue-Engineered Human Vascular Media Produced <i>in Vitro</i> by the Self-Assembly Approach Present Functional Properties Similar to Those of Their Native Blood Vessels
Bibliographic record
Abstract
We have developed a tissue-engineering approach for the production of a completely biological blood vessel from cultured human cells. In the present study, we took advantage of this tissue-engineering method to demonstrate that it can be used to reproduce the subtle differences in the expression of receptors present on the media of native human blood vessels. Indeed, a small percentage (3 of 18) of native human umbilical cord veins (HUCVs) responded to endothelin, the most powerful vasopressor agent known to date, via both endothelin A (ET(A)) and endothelin B (ET(B)) receptor activation. In contrast, most HUCVs tested responded to ET via ET(A) receptor activation only. Tissue-engineered vascular media (TEVM) were next reconstructed by using vascular smooth muscle cells (VSMCs) isolated and cultured from HUCVs expressing both ET(A) and ET(B) receptors to determine the functional integrity of our TEVM model. The reconstructed TEVM presents an endothelin response similar to that of respective HUCVs from which VSMCs were isolated. Reverse transcriptase polymerase chain reaction on TEVM reconstructed in vitro correlated these vasocontractile profiles by showing the presence of messenger RNA for both ET(A) and ET(B) receptors. Taken together with recently published results on TEVM expressing only ET(A) receptor, these results show that our reconstructed TEVM present a similar ET response profile as the blood vessel from which the VSMCs were isolated and cultured. These findings indicate that subtle differences, such as receptor expression, are preserved in the reconstructed tissue. Therefore, our TEVM offers a valuable human in vitro model with which to study the functionality of human blood vessels, such as their vasoactive response, or to perform pharmacologic studies.
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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) | 0.000 | 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) | 0.001 | 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".