Novel rinse assay for the quantification of oral neutrophils and the monitoring of chronic periodontal disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The aim of this study was to develop a single, rapid, noninvasive oral rinse assay to enable the accurate quantification of oral neutrophils. Products released by neutrophils are partly responsible for the destruction observed in periodontitis. Quantification of oral neutrophil levels is important for understanding their role in periodontal diseases. Previous studies have relied on time-consuming serial rinses and cumbersome counting techniques for the collection and quantification of oral neutrophils. MATERIAL AND METHODS: Patients with chronic periodontal disease provided rinse samples before and after phase I periodontal treatment. Cells in the rinse samples were stained with acridine orange, and neutrophil counts were carried out using a fluorescence microscope and a hemocytometer. RESULTS: This assay allowed us to detect a significant difference in pretreatment oral neutrophil counts between periodontal disease and healthy control groups (p < 0.001). Patients who responded favorably to phase I therapy demonstrated a 43% reduction in oral neutrophil counts compared with their pretreatment levels (p = 0.019). Patients who did not respond to phase I periodontal treatment showed no significant difference in oral neutrophil levels (p = 0.39). CONCLUSION: Oral neutrophil levels, as determined by a rapid oral rinse, reflect the severity of periodontal disease and treatment response. A single, rapid, oral rinse assay is an effective means of collecting and quantifying oral neutrophil levels and may serve as an excellent research tool for further study of the role of neutrophils in periodontal diseases.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".