Metronidazole enhances killing of Porphyromonas gingivalis by human PMNs
Bibliographic record
Abstract
Background and objectives Periodontitis affects the supporting structures of the teeth as a result of the interactions between the subgingival biofilm and the host immune system. Periodontal therapy in severe forms of periodontitis often utilizes antimicrobial agents with some potential to improve host defense responses. In the present study, we investigated the in vitro effect of metronidazole (MTZ) at concentrations achievable in the periodontal pocket on PMN activation and PMN mediated killing of Porphyromonas gingivalis. Materials and methods Flow cytometry based assays were used to measure the impact of MTZ on PMN degranulation, neutrophil extracellular trap (NET) formation and myeloperoxidase (MPO) release and phagocytosis in response to the keystone oral pathogen P. gingivalis. Functional assays for PMN mediated killing of P. gingivalis and reactive oxygen species (ROS) production in PMN were also carried out. Results We demonstrate that PMNs pretreated with MTZ (2 μg/ml or 50 μg/ml) displayed enhanced killing of P. gingivalis compared to untreated PMNs. At concentrations achieved physiologically in the periodontal pocket, MTZ induced PMN surface expression of two activation markers (CD66 and CD63). MTZ did not alter P. gingivalis-induced NETosis, but suppressed P. gingivalis-induced ROS production and phagocytosis. Conclusion MTZ displays a positive interaction with PMNs to potentiate PMN mediated killing of P. gingivalis and may therefore contribute to its beneficial effects in the treatment of periodontitis initiated by P. gingivalis infections including those refractory to conventional treatment.
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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) | 0.002 | 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".