<i>Porphyromonas gingivalis</i>-induced inflammatory mediator profile in an<i>ex vivo</i>human whole blood model
Bibliographic record
Abstract
Periodontitis is characterized by an accumulation of inflammatory cells in periodontal tissue and subgingival sites. Leukocytes play a major role in the host response to Porphyromonas gingivalis, a major aetiological agent of chronic periodontitis. Secretion of high levels of inflammatory mediators, including cytokines and prostaglandins, by leucocytes is believed to contribute to periodontal tissue destruction. The aim of this study was to investigate the inflammatory response of an ex vivo whole blood model to P. gingivalis stimulation. The production of interleukin-1 beta (IL-1beta), IL-4, IL-5, IL-6, IL-8, IL-10, IL-12p70, IL-13, tumour necrosis factor alpha (TNF-alpha), interferon gamma (IFN-gamma), IFN-gamma-inducible protein 10 (IP-10), monocyte chemoattractant protein-1 (MCP-1), Regulated on Activation Normal T cell Expressed and Secreted (RANTES) and prostaglandin E2 (PGE2) were quantified by enzyme-linked immunosorbent assays. P. gingivalis induced the secretion of the pro-inflammatory cytokines IL-1beta, TNF-alpha, IL-6 and IFN-gamma, the chemokines IL-8, RANTES and MCP-1 and the inflammatory mediator PGE2 in an ex vivo human whole blood model. The secretion levels were dependent on the strain and the infectious dose used. While the mediator profiles were comparable between six healthy subjects, a high interindividual variability in the levels of secreted mediators was observed. This study supports the view that P. gingivalis, by inducing high levels of inflammatory mediators from a mixed leucocyte population, can contribute to the 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".