Response of periodontitis and healthy patients in a <i>Porphyromonas gingivalis</i>‐stimulated whole‐blood model
Bibliographic record
Abstract
AIM: To investigate the inflammatory responses of periodontitis patients and healthy patients in a whole-blood model stimulated with Porphyromonas gingivalis. METHODS: Whole blood collected from 17 periodontitis patients and six healthy patients was stimulated with Porphyromonas gingivalis cells. The secretion of cytokines and matrix metalloproteinases was quantified by enzyme-linked immunosorbent assay. An analysis of covariance with the ancova model was used to evaluate the significance of differences in secreted host molecules by whole blood from the periodontitis and healthy groups. RESULTS: Porphyromonas gingivalis induced the secretion of interleukin-1β, interleukin-6, interleukin-8, tumor necrosis factor-α, monocyte chemoattractant protein-1, interferon inducible protein-10 by whole blood from patients in the periodontitis and healthy groups. Matrix metalloproteinase-8 and -9 levels secreted by whole blood also increased following stimulation. No significant differences (P < 0.05) in the amounts of secreted host molecules were observed between periodontitis and healthy patients. CONCLUSION: This study suggests that Porphyromonas gingivalis can provoke an inflammatory response and promote the progression of periodontitis by inducing the secretion of high levels of cytokines and matrix metalloproteinases by a mixed leukocyte population. However, the whole-blood model did not reveal any significant differences in the inflammatory response between periodontitis patients (n = 17) and periodontally-healthy patients (n = 6).
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".