Nifedipine and phenytoin induce matrix synthesis, but not proliferation, in intact human gingival connective tissue ex vivo
Bibliographic record
Abstract
Drug-induced gingival enlargement (DIGE) is a fibrotic condition that can be caused by the antihypertensive drug nifedipine and the anti-seizure drug phenytoin, but the molecular etiology of this type of fibrosis is not well understood and the role of confounding factors such as inflammation remains to be fully investigated. The aim of this study was to develop an ex vivo gingival explant system to allow investigation of the effects of nifedipine and phenytoin alone on human gingival tissue. Comparisons were made to the histology of human DIGE tissue retrieved from individuals with DIGE. Increased collagen, fibronectin, and proliferating fibroblasts were evident, but myofibroblasts were not detected in DIGE samples caused by nifedipine and phenytoin. In healthy gingiva cultured in nifedipine or phenytoin-containing media, the number of cells positive for p-SMAD2/3 increased, concomitant with increased CCN2 and periostin immunoreactivity compared to untreated explants. Collagen content assessed through hydroxyproline assays was significantly higher in tissues cultured with either drug compared to control tissues, which was confirmed histologically. Matrix fibronectin levels were also qualitatively greater in tissues treated with either drug. No significant differences in proliferating cells were observed between any of the conditions. Our study demonstrates that nifedipine and phenytoin activate canonical transforming growth factor-beta signaling, CCN2 and periostin expression, as well as increase collagen density, but do not influence cell proliferation or induce myofibroblast differentiation. We conclude that in the absence of confounding variables, nifedipine and phenytoin alter matrix homeostasis in gingival tissue explants ex vivo, and drug administration is a significant factor influencing ECM accumulation in gingival enlargement.
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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".