Controlling the Fibroblastic Differentiation of Mesenchymal Stem Cells Via the Combination of Fibrous Scaffolds and Connective Tissue Growth Factor
Bibliographic record
Abstract
Controlled differentiation of multi-potent mesenchymal stem cells (MSCs) into vocal fold-specific, fibroblast-like cells in vitro is an attractive strategy for vocal fold repair and regeneration. The goal of the current study was to define experimental parameters that can be used to control the initial fibroblastic differentiation of MSCs in vitro. To this end, connective tissue growth factor (CTGF) and micro-structured, fibrous scaffolds based on poly(glycerol sebacate) (PGS) and poly(ɛ-caprolactone) (PCL) were used to create a three-dimensional, connective tissue-like microenvironment. MSCs readily attached to and elongated along the microfibers, adopting a spindle-shaped morphology during the initial 3 days of preculture in an MSC maintenance medium. The cell-laden scaffolds were subsequently cultivated in a conditioned medium containing CTGF and ascorbic acids for up to 21 days. Cell morphology, proliferation, and differentiation were analyzed collectively by quantitative PCR analyses, and biochemical and immunocytochemical assays. F-actin staining showed that MSCs maintained their fibroblastic morphology during the 3 weeks of culture. The addition of CTGF to the constructs resulted in an enhanced cell proliferation, elevated expression of fibroblast-specific protein-1, and decreased expression of mesenchymal surface epitopes without markedly triggering chondrogenesis, osteogenesis, adipogenesis, or apoptosis. At the mRNA level, CTGF supplement resulted in a decreased expression of collagen I and tissue inhibitor of metalloproteinase 1, but an increased expression of decorin and hyaluronic acid synthesase 3. At the protein level, collagen I, collagen III, sulfated glycosaminoglycan, and elastin productivity was higher in the conditioned PGS-PCL culture than in the normal culture. These findings collectively demonstrate that the fibrous mesh, when combined with defined biochemical cues, is capable of fostering MSC fibroblastic differentiation in vitro.
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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 | 0.000 | 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) | 0.000 | 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".