Distinct Oral Neutrophil Subsets Define Health and Periodontal Disease States
Bibliographic record
Abstract
Neutrophils exit the vasculature and swarm to sites of inflammation and infection. However, these cells are abundant in the healthy, inflammation-free human oral environment, suggesting a unique immune surveillance role within the periodontium. We hypothesize that neutrophils in the healthy oral cavity occur in an intermediary parainflammatory state that allows them to interact with and contain the oral microflora without eliciting a marked inflammatory response. Based on a high-throughput screen of neutrophil CD (cluster of differentiation) marker expression and a thorough literature review, we developed multicolor flow cytometry panels to determine the surface marker signatures of oral neutrophil subsets in periodontal health and disease. We define here 3 distinct neutrophil subsets: resting/naive circulatory neutrophils, parainflammatory neutrophils found in the healthy oral cavity, and proinflammatory neutrophils found in the oral cavity during chronic periodontal disease. Furthermore, parainflammatory neutrophils manifest as 2 distinct subpopulations-based on size, granularity, and expression of specific CD markers-and exhibit intermediate levels of activation as compared with the proinflammatory oral neutrophils. These intermediately activated parainflammatory populations occur in equal proportions in the healthy oral cavity, with a shift to one highly activated proinflammatory neutrophil population in chronic periodontal disease. This work is the first to identify and characterize oral parainflammatory neutrophils that interact with commensal biofilms without inducing an inflammatory response, thereby demonstrating that not all neutrophils trafficking through periodontal tissues are fully activated. In addition to establishing possible diagnostic and treatment monitoring biomarkers, this oral neutrophil phenotype model builds on existing literature suggesting that the healthy periodontium may be in a parainflammatory state.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".