In Vitro Osteogenic Induction Of Human Gingival Fibroblasts For Bone Regeneration
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Periodontitis is an inflammatory disease causing bone loss, and is a primary cause of tooth loss. Gingival fibroblasts are readily available with minimal donor site morbidity and may be ideal for tissue engineering efforts in regenerating lost alveolar bone. Dexamethasone (Dex) is commonly employed for in vitro osteogenic induction of a variety of cells, but its effect on human gingival fibroblasts (HGF) is still controversial. Therefore, the aim of our study was to investigate the osteogenic differentiation of HGF following Dex treatment. METHODS: Cultured HGFs were exposed to osteogenic medium containing a wide range of Dex concentrations (0.01-10 µM). The osteogenic phenotype was assessed based on changes in alkaline phosphatase (ALP) activity, the mRNA expression of selected extracellular matrix proteins critical for mineralization and the extent of extracellular mineralization (Von Kossa staining and Ca-content). RESULTS: All assays showed a consistent and maximal osteogenic effect of Dex on HGF at 0.1 and 0.5 µM (weeks 3 and 4), as evidenced by significant osteopontin and osteocalcin expression and mineralization. Longer cultures (week 4) also yielded positive osteogenic effect of Dex at 0.01 µM. Moreover, ALP activity was significantly stimulated at 0.1 and 0.5 µM Dex initially after one week, but ALP was subsequently reduced under Dex. Higher Dex concentrations caused down regulation of osteogenic effects observed at the optimal (0.1-0.5 µM) concentrations. CONCLUSION: Under appropriate osteogenic conditioning, Dex treated HGFs could be a potential source of cells for cell-based therapy for periodontal bone regeneration.
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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) | 0.002 | 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".