A human tissue‐engineered vascular media: a new model for pharmacological studies of contractile responses
Bibliographic record
Abstract
Our method for producing tissue-engineered blood vessels based exclusively on the use of human cells, i.e., without artificial scaffolding, has previously been described (1). In this report, a tissue-engineered vascular media (TEVM) was specifically produced for pharmacological studies from cultured human vascular smooth muscle cells (VSMC). The VSMC displayed a differentiated phenotype as demonstrated by the re-expression of VSMC-specific markers and actual tissue contraction in response to physiological stimuli. Because of their physiological shape and mechanical strength, rings of human TEVM could be mounted on force transducers in organ baths to perform standard pharmacological experiments. Concentration-response curves to vasoconstrictor agonists (histamine, bradykinin, ATP, and UTP) were established, with or without selective antagonists, allowing pharmacological characterization of receptors (H1, B2, and P2Y1, and pyrimidinoceptors). Sustained agonist-induced contractions were associated with transient increases in cytosolic Ca2+ concentration, suggesting sensitization of the contractile machinery to Ca2+. ATP caused both Ca2+ entry and Ca2+ release from a ryanodine- and caffeine-sensitive store. Increased cyclic AMP or cyclic GMP levels caused relaxation. This human TEVM displays many of functional characters of the normal vessel from which the cells were originally isolated, including contractile/relaxation responses, cyclic nucleotide sensitivity, and Ca2+ handling mechanisms comparable to those of the normal vessel from which the cells were originally isolated. These results demonstrate the potential of this human model as a versatile new tool for pharmacological research.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".