Streptococcus salivarius inhibits immune activation by periodontal disease pathogens
Bibliographic record
Abstract
BACKGROUND: Periodontal disease represents a major health concern. The administration of beneficial microbes has been increasing in popularity over efforts to manipulate the microbes using antimicrobial agents. This study determined the ability of Streptococcus salivarius to inhibit IL-6 and IL-8 production by gingival fibroblasts when activated by periodontal pathogens and their effect on the salivary microbiome. METHODS: Primary human gingival fibroblasts were challenged with Porphyromonas gingivalis, Aggregatibacter actinomycetemcomitans and Fusobacterium nucleatum and a combination of all three. IL-6 and IL-8 cytokine release were measured. Using this same model, S. salivarius K12, M18 and different supernatant and whole-cell lysate fractions of S. salivarius K12 were administered to pathogen-induced fibroblasts. A patient study of healthy participants was also conducted to determine the effect S. salivarius K12 had on the native microbiome using 16S next generation sequence analysis. RESULTS: All pathogens tested induced a significant IL-6 and IL-8 response. S. salivarius K12 or M18, did not exhibit an increase in inflammatory cytokines. When either of the probiotic strains were co-administered with a pathogen, there were significant reductions in both IL-6 and IL-8 release. This effect was also observed when gingival fibroblasts were pre-treated with either S. salivarius K12 or M18 and then stimulated with the oral pathogens. Chewing gum containing S. salivarius K12 did not alter the salivary microbiome and did not increase inflammatory markers in the oral cavity. CONCLUSION: S. salivarius K12 and M18 prevented immune activation induced by periodontal disease pathogens. S. salivarius K12 did not alter the salivary microbiome or induce immune activation when administered as a chewing gum. These results warrant further study to determine if it may be an effective treatment in a model of periodontal 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".