Activation of Transcription Factor <i>GAX</i> and Concomitant Downregulation of IL-1β and ERK1/2 Modulate Vascular Smooth Muscle Cell Phenotype in 3D Fibrous Scaffolds
Bibliographic record
Abstract
Since vascular smooth muscle cells (VSMCs) display phenotypic plasticity in response to changing environmental cues, understanding the molecular mechanisms underlying the phenotypic modulation mediated by a three-dimensional (3D) scaffold is important to engineer functional vasculature. Following cell seeding into 3D scaffolds, the synthetic phenotype is desired to enable cells to expand rapidly and produce and assemble extracellular matrix components, but must revert to a quiescent contractile phenotype after tissue fabrication to impart the contractile properties found in native blood vessels. This study shows that 3D electrospun fibrous scaffolds regulate human coronary artery smooth muscle cells (HCASMCs) toward a more synthetic phenotype characterized by reduced contractile markers, such as smooth muscle alpha-actin and calponin. The reduction in contractile markers expression was mediated by endogenously expressed proinflammatory cytokine interleukin-1β (IL-1β). 3D topography transiently induces concomitant upregulation of IL-1β and MAPK ERK1/2 through nuclear factor-κB-dependent signaling pathway. An early burst of expression of IL-1β is essential for suppression of the homeobox transcription factor Gax and related cyclin-dependent kinase inhibitor p21(Cip1), which are key regulators for cells exiting from cell cycle. Our findings provide new insights for understanding signaling mechanisms of HCASMCs in electrospun 3D fibrous scaffolds, which have considerable value for application in vascular tissue engineering.
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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".