Refractory Periodontitis Population Characterized by a Hyperactive Oral Neutrophil Phenotype
Bibliographic record
Abstract
BACKGROUND: Neutrophils, in addition to being the primary protective component of the innate immune system, also contribute to periodontal destruction through production of reactive oxygen species (ROS), which cause damage to connective tissues and extracellular matrix after neutrophil activation. We have previously shown that hyperactive neutrophils are present in peripheral blood samples of patients diagnosed with refractory periodontitis. To test the hypothesis that oral neutrophil hyperactivity is related to periodontal disease severity, we used a flow cytometric approach to isolate and analyze oral neutrophil ROS (oROS) production in a refractory periodontal disease patient population. METHODS: Oral rinse samples and venous blood were obtained from 13 patients diagnosed with refractory periodontitis. After isolation of neutrophils from both samples, dihydrorhodamine 123 was used as a fluorescent probe for phorbol 12-myristate 13-acetate-mediated ROS production as assessed through flow cytometry. For each patient, oROS production levels were expressed as a percentage of their baseline to maximal peripheral blood neutrophil ROS production range. RESULTS: Two distinct groups of refractory patients were identified based on levels of phorbol 12-myristate 13-acetate-stimulated oROS production. The patient group with high oROS production had significantly more clinical attachment loss (AL) compared to the patient group with low oROS production. CONCLUSIONS: Our findings demonstrate that a group of refractory patients with increased clinical AL present a hyperactive oral neutrophil phenotype characterized by increased potential for ROS production. Identification of this exaggerated oral neutrophil phenotype could allow clinicians to identify which patients are more susceptible to rapid disease progression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".