Nifedipine Induces Periostin Expression in Gingival Fibroblasts through TGF-beta
Bibliographic record
Abstract
Gingival enlargement is a fibrotic condition that can arise from systemic administration of the dihydropyridine calcium channel blocker nifedipine. Periostin, a transforming growth factor-beta (TGF-β)-inducible matricellular protein, has been associated with fibrosis in numerous tissues, but its expression has never been examined in nifedipine-influenced gingival enlargement (NIGE). The objective of this study was to assess if periostin up-regulation is associated with NIGE and whether nifedipine induces periostin expression in gingival fibroblasts. In NIGE tissue (n = 6), periostin is overexpressed in the gingival connective tissue compared with healthy control tissue (n = 6). The transcription factor p-SMAD2/3, which is associated with canonical TGF-β signaling, localizes to the nuclei in both HGFs and oral epithelial cells in NIGE tissues, but not in control healthy tissue. In vitro culture of HGFs with 30 and 100 ng/mL of nifedipine significantly increased periostin mRNA and protein levels, which correlated with increased levels of active TGF-β and increased phosphorylation and nuclear localization of SMAD3. Blocking of canonical TGF-β signaling through inhibition of the TGF-β receptor I with SB431542 significantly reduced nifedipine-induced SMAD3 phosphorylation and periostin expression. Our results demonstrate that nifedipine up-regulates periostin in HGFs in a TGF-β-dependent manner.
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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".