Differential Regulation of Extracellular Matrix Components Using Different Vitamin C Derivatives in Mono- and Coculture Systems
Bibliographic record
Abstract
Vascular tissue engineering strategies using cell-seeded scaffolds require uniformly distributed vascular cells and sufficient extracellular matrix (ECM) production. However, acquiring sufficient ECM deposition on synthetic biomaterial scaffolds during the in vitro culture period prior to tissue implantation still remains challenging for vascular constructs. Two forms of vitamin C derivatives, ascorbic acid (AA) and sodium ascorbate (SA), are commonly supplemented in cell culture to promote ECM accumulation. However, the literature often refers to AA and SA interchangeably, and their differential effects on cell growth and ECM molecule (glycosaminoglycan, collagen, elastin) accumulation have never been reported when used in monoculture or coculture systems developed with synthetic three-dimensional (3D) scaffolds. In this study, it was found that 200 μM AA stimulated an increase in cell number, whereas SA (50, 100, and 200 μM) supported more calponin expression (immunostaining) and higher ECM accumulation from vascular smooth muscle cells (VSMCs) after 1 week in the degradable polar hydrophobic ionic polyurethane scaffold. The influence of AA and SA on ECM deposition was also studied in VSMC-monocyte cocultures to replicate some aspects of a wound healing environment in vitro and compared to their effects in respective VSMC monocultures after 4 weeks. Although 100 μM SA promoted ECM deposition in coculture, the condition of 100 μM AA + 100 μM SA was more effective toward enhancing ECM accumulation in VSMC monoculture after 4 weeks. The results demonstrated that AA and SA are not interchangeable, and the different effects of AA and/or SA on ECM deposition were both culture system (co- vs monoculture) and culture period (1 vs 4 week) dependent. This study provides further insight into practical vascular tissue engineering strategies when using 3D synthetic biomaterial-based constructs.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".