Formation of Cartilage and Synovial Tissue by Human Gingival Stem Cells
Bibliographic record
Abstract
Human gingival stem cells (HGSCs) can be easily isolated and manipulated in culture to investigate their multipotency. Osteogenic differentiation of bone-marrow-derived mesenchymal stem/stromal cells has been well documented. HGSCs derive from neural crests, however, and their differentiation capacity has not been fully established. The aim of the present report was to investigate whether HGSCs can be induced to differentiate to osteoblasts and chondrocytes. HGSCs were cultured either in a classical monolayer culture or in three-dimensional floating micromass pellet cultures in specific differentiation media. HGSC differentiation to osteogenic and chondrogenic lineages was determined by protein and gene expression analyses, and also by specific staining of cells and tissue pellets. HGSCs cultured in osteogenic differentiation medium showed induction of Runx2, alkaline phosphatase (ALPL), and osterix expression, and subsequently formed mineralized nodules consistent with osteogenic differentiation. Interestingly, HGSC micromass cultures maintained in chondrogenic differentiation medium showed SOX9-dependent differentiation to both chondrocyte and synoviocyte lineages. Chondrocytes at different stages of differentiation were identified by gene expression profiles and by histochemical and immunohistochemical staining. In 3-week-old cultures, peripheral cells in the micromass cultures organized in layers of cuboidal cells with villous structures facing the medium. These cells were strongly positive for cadherin-11, a marker of synoviocytes. In summary, the findings indicate that HGSCs have the capacity to differentiate to osteogenic, chondrogenic, and synoviocyte lineages. Therefore, HGSCs could serve as an alternative source for stem cell therapies in regenerative medicine for patients with cartilage and joint destructions, such as observed in rheumatoid arthritis.
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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) | 0.003 | 0.001 |
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".