Microbiology and Cytokine Levels Around Healthy Dental Implants and Teeth
Bibliographic record
Abstract
BACKGROUND: Elicitation of the relationship of periodontopathogens and pro-inflammatory cytokines to bone resorption and formation is significant to a growing body of research known as osteoimmunology. It is essential that clinically healthy peri-implant and periodontal sites are studied to contribute comparison data for investigations that are addressing diseased sites. PURPOSE: The purpose of this study was to describe levels of selected pro-inflammatory cytokines in clinically healthy peri-implant and periodontal sites, and to examine whether cytokine levels may be related to specific bacterial/viral pathogens. MATERIALS AND METHODS: Eleven subjects (mean age 56.2 +/- 10) participated in the study. Subgingival microbial samples were cultured for periodontopathic bacteria. Gingival crevicular fluid samples were analyzed by nested polymerase chain reaction for Cytomegalovirus (HCMV) and were tested for the quantification of Interleukin (IL)-8, IL-1beta, IL-6, IL-10, Tumor Necrosis Factor (TNF)-alpha, and IL-12p70 using flow cytometry (FACS). Findings for microbiota composition and cytokine levels were compared between implants and teeth (chi square, Kruskall-Wallis, Mann-Whitney; p < or = .05). RESULTS: Both the frequency (%) and levels (%) of periodontopathic bacteria were higher around teeth than implants. The concentration (picogram per milliliter) of cytokines was more prominent around implants than teeth, reaching nearly twofold differences in some instances. Cytokine levels were higher when the sites analyzed were positive for any bacteria tested. HCMV was not detected. CONCLUSIONS: Pro-inflammatory cytokine production was unrelated to heavy bacterial challenge. Nevertheless, when periodontopathic bacteria were detected by culture, cytokine levels were increased around both implants and teeth. Studies are needed to investigate the pro-inflammatory cytokines (especially IL-1beta and TNF-alpha) produced in spite of minimal bacterial accumulation.
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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 | 0.000 | 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 | 0.001 | 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".