Tissue-Engineered Human Vascular Media With a Functional Endothelin System
Bibliographic record
Abstract
BACKGROUND: Cardiovascular diseases remain a major cause of death and disability in the Western world. Among the various approaches adopted to counteract the morbidity associated with these diseases, surgical procedures and cardiac and vascular xenotransplantations or allotransplantations are routinely performed. The suitable vascular graft would be as close as possible to the native and healthy vessel composed exclusively of human components provided by the patient and would adapt to the donor's hemodynamics. We have developed such a tissue-engineered human blood vessel reconstructed with human cells. Because endothelin is the most potent vasopressor known to date, we were interested in investigating the functionality of the endothelinergic system in our reconstructed human blood vessel. METHODS AND RESULTS: Vasoconstriction studies were performed with nonselective and selective agonists and antagonists to demonstrate that ET(A) receptors were present and functional in tissue-engineered human vascular media constructed with the self-assembly method. Reverse-transcriptase polymerase chain reaction studies demonstrated that mRNA of the ET(A) but not the ET(B) receptor was present in these human tissue-engineered blood vessels. Furthermore, we demonstrated that the endothelin-converting enzyme, the main enzyme responsible for the formation of the biologically active endothelin peptides, was present and functional in these same bioengineered vascular media. CONCLUSIONS: Our results suggest that the media component of our tissue-engineered blood vessel has the potential of controlling vascular resistance via the presence of functional endothelin ET(A) receptors and endothelin-converting enzyme.
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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 | 0.000 |
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".