Microbiome of titanium and zirconia dental implants abutments
Bibliographic record
Abstract
OBJECTIVES: This study employed culture-independent molecular techniques to extend the characterization of the microbial diversity of biofilm associated with either titanium or zirconia implant-abutments, including not-yet-cultivated bacteria species, and to identify and quantify species recovered from peri-implantar/periodontal sulci, supragingival biofilm and the internal parts of implants. Probing depth, clinical attachment level, bleeding on probing, and marginal bone level were also evaluated over time and correlated with biofilm formation. METHODS: Twenty healthy participants were analyzed. DNA-Checkerboard and 16S-rDNA-Pyrosequencing were used to quantify and determine species identity. RESULTS: 161 bacterial taxa representing 12 different phylotypes were found, of which 25% were non-cultivable. Species common to all sites belonged to genera Fusobacterium, Prevotella, Actinomyces, Porphyromonas, Veillonella and Streptococcus. While some species were subject-specific and detected in most sites, other species were site-specific. Moderate to higher levels of unclassified species were found colonizing titanium-related sites. Pathogenic and non-pathogenic species were detected colonizing oral sites in both materials. Titanium-related sites presented the highest total microbial count and higher counts of pathogenic species. CONCLUSIONS: Our results revealed differences regarding microbial diversity and microorganisms counts in oral biofilm associated with titanium or zirconia. The obtained data suggests a possible relation between microbiological findings and clinical outcomes. SIGNIFICANCE: Next-generation methods of detection have provided new insights on complex microbiota colonizing different sites of oral cavity. The present study demonstrates relevant differences in the communities and microbial counts colonizing different tested substrates with consequent significant differences in the clinical-outcomes, suggesting a probably different mechanism for specific bacterial adhesion.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".