FOXP3+ T Cells—An Emerging Evidence in Periodontitis Therapeutics
Bibliographic record
Abstract
OBJECTIVE: To review interaction of FOXP3+ regulatory T cells with Th17 cells in determining the progression of periodontitis. MATERIAL AND METHODOLOGY: Literature review pertaining to FOXP3+ regulatory T cells, Th17 cells, and periodontitis was analyzed. Descriptive summary is presented. RESULTS: FOXP3+ regulatory T cells (Tregs) play an essential role in maintaining immune homeostasis and modulating inflammatory responses. The balance between Tregs and pro-inflammatory Th17 cells is crucial in determining the progression of periodontitis, a chronic immune-mediated inflammatory disease. While Tregs are responsible for suppressing excessive immune activation and preventing tissue destruction, an imbalance favoring Th17 cells leads to increased osteoclastic activity and alveolar bone loss through IL-17 and RANKL signaling. The inflammatory microenvironment in periodontitis compromises FOXP3+ Treg stability and function, thereby allowing unregulated immune responses that exacerbate periodontal tissue breakdown. Recent studies suggest that strategies aimed at enhancing Treg-mediated immune regulation, such as IL-2 supplementation, all-trans retinoic acid (ATRA), IL-33 administration, and CCL22-mediated recruitment, could mitigate periodontal inflammation and preserve alveolar bone integrity. Furthermore, systemic conditions like diabetes and obesity play a significant role in disrupting Treg function by promoting a pro-inflammatory environment, impairing immune regulation, and exacerbating immune dysregulation. This dysfunction weakens the protective role of Tregs, leading to an intensified inflammatory response that accelerates periodontal tissue destruction and alveolar bone loss. CONCLUSION: Understanding the mechanisms governing FOXP3+ Treg stability and their interaction with pathogenic Th17 responses is essential for developing targeted immunomodulatory therapies. Future research should focus not only on selectively expanding Tregs but also on translational strategies such as adoptive Treg transfer and IL-17 inhibition, while carefully balancing efficacy and the risk of systemic immunosuppression.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".