Transcriptomes in Healthy and Diseased Gingival Tissues
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Bibliographic record
Abstract
BACKGROUND: Clinical and radiographic measures are gold standards for diagnosing periodontitis but offer little information regarding the pathogenesis of the disease. We hypothesized that a comparison of gene expression signatures between healthy and diseased gingival tissues would provide novel insights in the pathobiology of periodontitis and would inform the design of future studies. METHODS: Ninety systemically healthy non-smokers with moderate to advanced periodontitis (63 with chronic periodontitis and 27 with aggressive periodontitis) each contributed at least two diseased interproximal papillae (with bleeding on probing [BOP], probing depth [PD] > or =4 mm, and attachment loss [AL] > or =3 mm) and a healthy papilla, if available (no BOP, PD < or =4 mm, and AL < or =2 mm). RNA was extracted, amplified, reverse-transcribed, labeled, and hybridized with whole genome microarrays. Differential expression was assayed in 247 individual tissue samples (183 from diseased sites and 64 from healthy sites) using a standard mixed-effects linear model approach, with patient effects considered random with a normal distribution and gingival tissue status considered a two-level fixed effect. Gene ontology analysis classified the expression patterns into biologically relevant categories. RESULTS: Transcriptome analysis revealed that 12,744 probe sets were differentially expressed after adjusting for multiple comparisons (P <9.15 x 10(7)). Of those, 5,295 were upregulated and 7,449 were downregulated in disease compared to health. Gene ontology analysis identified 61 differentially expressed groups (adjusted P <0.05), including apoptosis, antimicrobial humoral response, antigen presentation, regulation of metabolic processes, signal transduction, and angiogenesis. CONCLUSION: Gingival tissue transcriptomes provide a valuable scientific tool for further hypothesis-driven studies of the pathobiology of periodontitis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it