Unraveling the role of mechanical stimulation on smooth muscle cells: A comparative study between 2D and 3D models
Bibliographic record
Abstract
A thorough understanding of cell response to combined culture configuration and mechanical cues is of paramount importance in vascular tissue engineering applications. Herein, we investigated and compared the response of vascular smooth muscle cells (vSMCs) cultured in different culture environments (2D cell monolayers and 3D cellularized collagen-based gels) in combination with mechanical stimulation (7% uniaxial cyclic strain, 1 Hz) for 2 and 5 days. When cyclic strain was applied, two different responses, in terms of cell orientation and expression of contractile-phenotype proteins, were observed in 2D and 3D models. Specifically, in 2D configuration, cyclic strain caused ∼50% of cell population to align nearly perpendicular (80-90 degrees) to the strain direction, while not influencing the contractile-phenotype protein expression, as compared to the 2D static controls. Conversely, the application of uniaxial strain to 3D constructs induced a ∼60% cell alignment almost parallel (0-10 degrees) to the strain direction. Moreover, 3D mechanical stimulation applied for 5 days induced a twofold increase of SM α-actin level and a 14-fold increase of calponin expression as compared to 3D static controls. Altogether these findings provide a new insight into the potential to drive cell behavior by modulating the extracellular matrix and the biomechanical environment. Biotechnol. Bioeng. 2016;113: 2254-2263. © 2016 Wiley Periodicals, Inc.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".