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
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it