The Role of NrF2 in the Regulation of Periodontal Health and Disease
Bibliographic record
Abstract
Immune-related disease tolerance is an important defense strategy that facilitates the maintenance of health in organs and tissues that are commonly colonized by bacteria. Immune tolerance to dysbiotic, tooth-borne biofilms is a poorly understood yet clinically relevant concept in the immunopathological mechanisms that are involved in the pathogenesis of periodontitis, particularly those related to neutrophil and macrophage responses. In periodontal health, neutrophils and macrophages respond to the formation of pathogenic bacterial biofilms by the production of bactericidal reactive oxygen species (ROS). However, when released in excess, ROS cause tissue damage and exacerbate inflammation. To counter these destructive responses, many cell types, including neutrophils and macrophages, launch a dedicated antioxidant system that limits the cell and tissue-damaging effects of ROS. The expression of antioxidants is primarily regulated by genetic response elements in their promoters. Here we consider the roles of nuclear factor erythroid 2-related factor (NrF2), a transcription factor, and other key regulators of antioxidants. The concept of disease tolerance, neutrophil and macrophage-generated oxidative stress, and their relationship to the pathogenesis of periodontitis is reviewed. We focus on the regulation of NrF2 and recent evidence suggesting that NrF2 plays a central role in host protection against tissue destruction in 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".