Biomarkers and Bacteria Around Implants and Natural Teeth in the Same Individuals
Bibliographic record
Abstract
BACKGROUND: This cross-sectional study assesses cytokine levels in peri-implant crevicular fluid (PICF)/gingival crevicular fluid (GCF) and a selection of subgingival/submucosal plaque bacteria from clinically healthy or diseased sites in the same individuals. METHODS: Samples from 97 implants/teeth (58 implants [19 healthy, 20 mucositis, 19 peri-implantitis] and 39 natural teeth [19 healthy, 12 gingivitis, eight periodontitis] in 15 systemically healthy patients were investigated by immunoassay and real-time polymerase chain reaction. Samples were obtained first, with probing depth, clinical attachment level, bleeding on probing, plaque index scores, and keratinized tissue width then recorded. Data were analyzed by Wilcoxon, Mann-Whitney U, and permutation tests on dependent, independent, and mixed dependent and independent samples and Spearman correlation. RESULTS: Interleukin (IL)-1β levels were significantly higher in PICF samples of healthy implants than in GCF samples of healthy teeth (P = 0.003), and soluble receptor activator of nuclear factor-κB ligand (sRANKL) concentrations were significantly higher in the gingivitis than the mucositis group (P = 0.004). Biomarker levels were similar in peri-implantitis and periodontitis groups (P >0.05). Actinomyces naeslundi and Streptococcus oralis levels were significantly higher in the healthy implant group than in healthy teeth (P <0.05). Prevotella intermedia and Treponema denticola (Td) levels were lower in the mucositis group than the gingivitis group (P <0.05). Prevotella oralis and S. oralis levels were significantly higher in the periodontitis group (P <0.05), and Td levels were significantly higher in the peri-implantitis group (P <0.05). CONCLUSION: There were many similarities but, crucially, some differences in biomarker levels (IL-1β and sRANKL) and bacterial species between peri-implant and periodontal sites in the same individuals, suggesting similar pathogenic mechanisms.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".