Toward an <i>In Vitro</i> Vasculature: Differentiation of Mesenchymal Stromal Cells Within an Endothelial Cell-Seeded Modular Construct in a Microfluidic Flow Chamber
Bibliographic record
Abstract
An in vitro tissue construct amenable to perfusion was formed by randomly packing mesenchymal stromal cell (MSC)-embedded, endothelial cell (EC)-coated collagen cylinders (modules) into a microfluidic chamber. The interstices created by the random packing of the submillimeter-sized modules created EC-lined channels. Flow caused a greater than expected amount of contraction and remodeling in the modular constructs. Flow influenced the MSC to develop smooth muscle cell markers (smooth muscle actin-positive, desmin-positive, and von Willebrand factor-negative) and migrate toward the surface of the modules. When modules were coated with EC, the extent of MSC differentiation and migration increased, suggesting that the MSC were becoming smooth muscle cell- or pericyte-like in their location and phenotype. The MSC also proliferated, resulting in a substantial increase in the number of differentiated MSC. These effects were markedly less for static controls not experiencing flow. As the MSC migrated, they created new matrix that included the deposition of proteoglycans. Collectively, these results suggest that MSC-embedded modules may be useful for the formation of functional vasculature in tissue engineered constructs. Moreover, these flow-conditioned tissue engineered constructs may be of interest as three-dimensional cell-laden platforms for drug testing and biological assays.
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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".