Gingival and dermal fibroblasts produce interleukin‐1β converting enzyme and interleukin‐1β but not interleukin‐18 even after stimulation with lipopolysaccharide
Bibliographic record
Abstract
Epithelial cells play a critical role in periodontal disease through the secretion of pro-inflammatory cytokines such as interleukin-1 beta (IL-1 beta) and interleukin-18 (IL-18). However, the role played by fibroblasts is still unclear. The rationale of this study was to throw light on the role of gingival fibroblasts in periodontal disease. We thus investigated the expression of IL-1 beta, IL-18, and ICE mRNA and the secretion of the corresponding proteins by human normal gingival fibroblasts before and after stimulation with lipopolysaccharide (LPS) from Porphyromonas gingivalis and Escherichia coli. IL-1 beta, IL-18, and ICE mRNA expression was evaluated by RT-PCR. Proteins were analyzed by Western blot and ELISA. We demonstrated that gingival fibroblasts expressed ICE mRNA. Basal expression of ICE was modulated following cell stimulation with lipopolysaccharide (5 mug/ml). However, gingival fibroblasts expressed low levels of IL-1 beta mRNA. The expression was potentiated by LPS. The expression of IL-1 beta mRNA was followed by the secretion of IL-1 beta but not IL-18 protein. Our study suggests that fibroblasts may be involved in the defense against infections via an IL-1 beta-mediated but not an IL-18-mediated mechanism.
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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) | 0.001 | 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) | 0.003 | 0.001 |
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".