Phenytoin activates Smad3 phosphorylation and periostin expression in drug-induced gingival enlargement.
Bibliographic record
Abstract
Drug-induced gingival enlargement (DIGE) is a fibrotic condition associated with systemic administration of the anti-epileptic drug, phenytoin. We have previously demonstrated that periostin, which is transforming growth factor-beta (TGF-β) inducible gene, is upregulated in various fibrotic conditions including gingival enlargement associated with nifedipine. The objective of this study was to assess periostin expression in phenytoin-induced gingival enlargement (PIGE) tissues and to investigate the mechanisms underlying periostin expression. Human PIGE tissues were assessed using Masson's trichrome, with cell infiltration and changes in extracellular matrix composition characterized through labeling with antibodies to periostin, phospho-SMAD 3, TGF-β, as well as the macrophage markers CD68 and RM3/1. Using human gingival fibroblasts (HGFs) in vitro we examined the pathways through which phenytoin acts on fibroblasts. In PIGE tissues, which demonstrate altered collagen organization and increased inflammatory cell infiltration, periostin protein was increased compared with healthy tissues. p-SMAD2/3, the transcription factor associated with canonical TGF-β signaling, is localized to the nuclei in both gingival fibroblasts and oral epithelial cells in PIGE tissues, but not in healthy tissue. In vitro culture of HGFs with 15 and 30 μg/ml of phenytoin increased periostin protein levels, which correlated with p-SMAD3 phosphorylation. Inhibition of canonical TGF-β signaling with SB431542 significantly reduced phenytoin induction of SMAD3 phosphorylation and periostin expression in HGFs. Analysis of PIGE tissues showed a subset of CD68 stained macrophages were TGF-β positive and that RM1/3 regenerative macrophages were present in the tissues. Our results demonstrate that phenytoin 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".