Mechanical Loading Modulates the Differentiation State of Vascular Smooth Muscle Cells
Bibliographic record
Abstract
The cause underlying the onset of stenosis after vascular reconstruction is not well understood. In the present study, we evaluated the effect of mechanical unloading on the differentiation state of human vascular smooth muscle cells (hVSMCs) using a tissue-engineered vascular media (TEVM). hVSMCs cultured in a mechanically loaded three-dimensional environment, known as a living tissue sheet, had a higher differentiated state than cells grown on plastic. When the living tissue sheet was detached from its support, the release of the residual stress resulted in a mechanical unloading and cells within the extracellular matrix (ECM) dedifferentiated as shown by downregulation of differentiation markers. The relaxed living tissue sheet can be rolled onto a tubular mandrel to form a TEVM. The rolling procedure resulted in the reintroduction of a mechanical load leading to a cohesive compacted tissue. During this period, cells gradually redifferentiated and aligned circumferentially to the tubular support. Our results suggest that differentiation of hVSMCs can be driven by mechanical loading and may occur simultaneously in the absence of other cell types. The extrapolation of our results to the clinical context suggests the hypothesis that hVSMCs may adopt a proliferative phenotype resulting from the mechanical unloading of explanted blood vessels during vascular reconstruction. Therefore, we propose that this mechanical unloading may play an important role in the onset of vascular graft stenosis.
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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".