Synergistic enhancement of collagenous protein synthesis by human gingival fibroblasts exposed to nifedipine and interleukin‐1‐beta <i>in vitro</i>
Bibliographic record
Abstract
Gingival overgrowth commonly occurs coincident to therapy with calcium channel blockers. The biologic mechanism for this condition is unknown; however, many clinicians suggest that poor oral hygiene may contribute to development of the overgrowth. This study tests the hypothesis that collagenous protein synthesis by gingival fibroblasts is synergistically enhanced when they are exposed to both nifedipine (N) and the pro-inflammatory cytokine, interleukin-1-beta, a cytokine expressed in inflamed gingiva. Human gingival fibroblasts were isolated from biopsies of normal gingiva and cells separated into two groups. Group 1 was exposed to media containing 0, 5, 50, or 500 pg/ml IL-1-beta, or 10(-7) M N for 7 days; Group 2 was exposed to those concentrations of IL-1-beta +10(-7) M N. [3H]-proline was added to the medium for the final 24 h. Cells and matrix were harvested and radioactivity determined by liquid scintillation analysis. Means (d.p.m./10(3) cells) were compared by factorial ANOVA and Scheffé comparisons. Collagenous protein synthesis was significantly reduced by 5 pg/ml IL-1-beta +10(-7) M N and enhanced by 500 pg/ml IL-1-beta +10(-7) M N as compared to N or IL-1-beta alone. Thus, patients may be more susceptible to gingival overgrowth coincident to nifedipine therapy as a result of the synergistic enhancement of connective tissue synthesis by these agents.
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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".