Detection and comparison of neutrophil extracellular traps in tissue samples of peri‐implantitis, periodontitis, and healthy patients: A pilot study
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to detect and compare the tissular expression of neutrophil extracellular traps (NETs) in peri-implant and periodontal samples of patients with peri-implantitis, periodontitis, and controls. MATERIALS AND METHODS: An observational study was performed on patients with peri-implantitis, periodontitis, and controls. Peri-implant and/or periodontal clinical examinations were performed on each participant. Tissue samples were collected during tooth/implant extraction for clinical reasons. Electron microscopy analysis, Picro-Sirius red staining, immunohistochemical (CD15), and immunofluorescence (citrullinated H3 and myeloperoxidase) techniques were performed to detect NET-related structures and the degree of connective tissue destruction, between the study groups. RESULTS: Sixty-four patients were included in the study: 28 peri-implantitis, 26 periodontitis, and 10 controls, with a total of 51 implants, 26 periodontal teeth, and 10 control teeth. Neutrophil release of nuclear content was observed in transmission electron microscopy. Immunohistochemical analysis showed a greater CD15 expression in both peri-implantitis and periodontitis compared to controls (p < 0.001), and peri-implantitis presented lower levels of connective tissue and collagen compared to both periodontitis (p = 0.044; p < 0.001) and controls (p < 0.001). Immunofluorescence showed greater citH3 expression in peri-implantitis than the one found in both periodontitis (p = 0.003) and controls (p = 0.048). CONCLUSIONS: A greater presence and involvement of neutrophils, as well as a greater connective tissue destruction were observed in cases of peri-implantitis. A higher expression of NET-related markers was found in mucosal samples of peri-implantitis compared to periodontitis and controls.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".