<i>Treponema denticola</i> lipooligosaccharide activates gingival fibroblasts and upregulates inflammatory mediator production
Bibliographic record
Abstract
In response to bacterial challenges, fibroblasts, a major constituent of gingival connective tissue, can produce immunoregulatory cytokines and proteolytic enzymes that may contribute to tissue destruction and the progression of periodontitis, a chronic inflammatory disease affecting tooth-supporting tissues, including alveolar bone. The spirochete Treponema denticola is a major etiological agent of periodontitis and can invade oral tissues. The aim of the present study was to investigate the inflammatory response of gingival fibroblasts to T. denticola lipooligosaccharide (LOS). T. denticola LOS induced significant production of various inflammatory mediators by fibroblasts, including interleukin-6, interleukin-8, monocyte chemoattractant protein 1, nitric oxide, and prostaglandin E(2). In addition, the secretion of matrix metalloproteinase 3, an enzyme active on basement membrane components, was also significantly increased. The response of fibroblasts was dose-dependent and much stronger following a 24 h stimulation period. The expression and/or phosphorylation state of several signaling proteins, including Fos, MKK1, MKK2, MKK3/6, NF-kappaB p50, and NF-kappaB p65, was enhanced following stimulation of fibroblasts with T. denticola LOS. In summary, T. denticola LOS induced an inflammatory response in gingival fibroblasts and may thus contribute to the immunopathogenesis of periodontitis and the progression of the disease.
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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".