Induction of Fibrogenic Phenotype in Human Mesenchymal Stem Cells by Connective Tissue Growth Factor in a Hydrogel Model of Soft Connective Tissue
Bibliographic record
Abstract
Scar formation is the typical endpoint of wound healing in adult mammalian tissues. An overactive or prolonged fibrogenic response following injury leads to excessive deposition of fibrotic proteins that promote tissue contraction and scar formation. Although well-defined in the dermal tissue, the progression of fibrosis is less explored in other connective tissues, such as the vocal fold. To establish a physiologically relevant 3D model of loose connective tissue fibrosis, we have developed a synthetic extracellular matrix using hyaluronic acid (HA) and peptidic building blocks carrying complementary functional groups. The resultant network was cell adhesive and protease degradable, exhibiting viscoelastic properties similar to the human vocal fold. Human mesenchymal stem cells (hMSCs) were encapsulated in the HA matrix as single cells or multicellular aggregates and cultured in pro-fibrotic media containing connective tissue growth factor (CTGF) for up to 21 days. hMSCs treated with CTGF-supplemented media exhibited an increased expression of fibrogenic markers and ECM proteins associated with scarring. Incorporation of α-smooth muscle actin into F-actin stress fibers was also observed. Furthermore, CTGF treatment increased the migratory capacity of hMSCs as compared to the CTGF-free control groups, indicative of the development of a myofibroblast phenotype. Addition of an inhibitor of the mitogen-activated protein kinase (MAPK) pathway attenuated cellular expression of fibrotic markers and related ECM proteins. Overall, this study demonstrates that CTGF promotes the development of a fibrogenic phenotype in hMSCs encapsulated within an HA matrix and that the MAPK pathway is a potential target for future therapeutic endeavors towards limiting scar formation in loose connective tissues.
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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".