The role of azurocidin and its implications in periodontal and peri-implant disease: A systematic review
Bibliographic record
Abstract
OBJECTIVES: This systematic review aimed to explore the role of Azurocidin (Azu) in the pathogenesis of periodontal and peri-implant disease and its potential use as an inflammatory biomarker. MATERIALS AND METHODS: Four electronic databases were used for study identification: PubMed, Google Scholar, ScienceDirect, and Scopus from Oct 10, 1991 to Jul 15, 2024. Study selection and data extraction were performed in a blinded and independent manner. The Joanna Briggs Institute (JBI) tool was used to assess the quality of cross-sectional articles, and the Newcastle-Ottawa scale was used to assess cohort studies. RESULTS: Out of 222 identified articles, nine studies met the inclusion criteria. These studies included 462 participants: 156 with healthy teeth and implants and 306 with periodontal conditions such as gingivitis, periodontitis, apical periodontitis, peri-implant mucositis, and peri-implantitis. A total of 1313 samples were analyzed (163 saliva, 118 PICF, 1003 GCF, 11 gingival tissue, and 18 infected root canals). ELISA was the most common method for azurocidin analysis (66.6 %), followed by LC-MS/MS (33.3 %), nLC-MS/MS (11.1 %), and Western Blot (11.1 %). Azu levels were consistently elevated in individuals with periodontitis compared to periodontally healthy subjects. CONCLUSIONS: Azu may contribute to the inflammatory processes in periodontal and peri-implant diseases. Although elevated levels are observed in periodontitis, its diagnostic value remains unclear due to limited and heterogeneous data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".