Gingival Epithelial Cell-Derived Microvesicles Activate Mineralization in Gingival Fibroblasts
Bibliographic record
Abstract
Abstract Soft tissue calcification occurs in many parts of the body, including the gingival tissue. Epithelial cell-derived MVs can control many functions in fibroblasts but their role in regulating mineralization has not been explored. We hypothesized that microvesicles (MVs) derived from gingival epithelial cells could regulate calcification of gingival fibroblast cultures in osteogenic environment. Human gingival fibroblasts (HGFs) were cultured in osteogenic differentiation medium with or without human gingival epithelial cell-derived MV stimulation. Mineralization of the cultures, localization of the MVs and mineral deposits in the HGF cultures were assessed. Gene expression changes associated with MV exposure were analyzed using gene expression profiling and real-time qPCR. Within a week of exposure, epithelial MVs stimulated robust mineralization of HGF cultures that was further enhanced by four weeks. The MVs taken up by the HGF's did not calcify themselves but induced intracellular accumulation of minerals. HGF gene expression profiling after short exposure to MVs demonstrated relative dominance of inflammation-related genes that showed increases in gene expression. In later cultures, OSX, BSP and MMPs were significantly upregulated by the MVs. These results suggest for the first time that epithelial cells maybe associated with the ectopic mineralization process often observed in the soft 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".