Keratinocyte growth factor (KGF)‐1 and ‐2 protein and gene expression in human gingival fibroblasts
Bibliographic record
Abstract
The onset and progression of periodontal disease is associated with significant changes in the epithelial component of the attachment complex. From the early to the advanced stages of periodontal disease increased epithelial cell proliferation, migration and invasion into the surrounding connective tissue takes place. Concomitantly there is a significant increase in proinflammatory cytokine expression in periodontal tissue and quantitative and qualitative changes in the subgingival microflora, including an increase in gram-negative microorganisms. One of the most significant virulence factors of these bacteria is lipopolysaccharide (LPS) connected to the outer membrane. Two important growth factors controlling epithelial behavior are Keratinocyte Growth Factor-1 (KGF-1) and -2 (KGF-2). Connective tissue cells express these growth factors, but only epithelial cells respond to them. We studied the effect of proinflammatory cytokines and LPS on gingival fibroblast expression of KGF-1 and KGF-2 in vitro. Gingival fibroblasts were found to express KGF-1 and -2 in culture but only KGF-1 protein and gene expression was stimulated by serum, in a concentration-dependent manner by proinflammatory cytokines IL-1alpha, IL-1beta, TNF-alpha and IL-6 and LPS isolated from Porphyromonas gingivalis and Escherichia coli. The local increase in proinflammatory cytokine expression and the accumulation of LPS in disease sites may therefore stimulate gingival fibroblast expression of KGF-1. We hypothesize that this local increase in KGF-1 expression may, via a paracrine mechanism, stimulate local epithelial cell proliferation, migration and invasion during the onset and progression of periodontitis.
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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".