Keratinocyte growth factor‐1 expression in healthy and diseased human periodontal tissues
Bibliographic record
Abstract
OBJECTIVES: Keratinocyte growth factor-1 (KGF-1) is up-regulated in chronic inflammation and specifically stimulates epithelial cell proliferation by signaling through the epithelial-specific keratinocyte growth factor receptor (KGFR). We examined KGF-1 and KGFR protein and gene expression in healthy and diseased periodontal tissues. METHODS: Tissues were collected from patients with periodontal health or disease, immediately frozen and stained for KGF-1 and KGFR protein expression. Laser capture microdissection of epithelial and connective tissue cells with reverse transcription-polymerase chain reaction (RT-PCR) examined KGF-1 and KGFR gene expression profiles and enzymatic digestion with heparitinase, chondroitinase ABC or pre-treatment with suramin examined epithelial surface molecule interactions with KGF-1. RESULTS: In tissues collected from healthy patients, KGF-1 protein localized to areas of junctional and basal oral epithelial cells and was significantly increased in periodontal pocket epithelium (p<0.01) and in the oral epithelium (p<0.05) of disease-associated tissues. KGFR localized to the junctional and the parabasal cells of oral epithelium, with the relative staining intensity being increased in disease-associated pocket epithelium (p<0.05). Laser capture microdissection with RT-PCR confirmed KGF-1 and KGFR were specifically expressed by connective tissue and epithelium, respectively. KGF-1 localization to epithelial cells was largely eliminated by suramin pre-treatment, indicating interaction with the KGFR. CONCLUSIONS: KGF-1 and KGFR proteins are expressed in healthy periodontal tissues but significantly increased in diseased periodontal tissues. We hypothesize up-regulation of KGF-1 and KGFR protein associated with disease regulates epithelial cell behavior associated with onset and progression of periodontal pocket formation.
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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 | 0.001 | 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".