Influence of the Periodontal Status on the Initial‐Biofilm Formation on Titanium Surfaces
Bibliographic record
Abstract
BACKGROUND: Dental implants will be exposed to a complex ecosystem once they are placed in the oral cavity. The bacterial colonization and biofilm formation on these devices will depend not only on the physicochemical surface implant properties but also on the periodontal health conditions of the patients, as these devices are exposed. PURPOSE: The aim of this study was to correlate the subgingival microbial profile with the composition of initial biofilm formed on different microstructured titanium (Ti) surfaces. MATERIALS AND METHODS: Ten periodontitis and 10 periodontally healthy subjects were included in this study. The subjects wore a removable acrylic device with four different fixed Ti surfaces for 48 hours. Microbial samples of subgingival plaque and the biofilm formed on each Ti surface were individually analyzed by the checkerboard DNA-DNA hybridization technique. RESULTS: Despite the roughness or hydrophilicity of the Ti surfaces, a characteristic pattern of bacterial adhesion was observed on each of the study groups. However, significant differences in the proportion of the species that colonized the Ti surfaces were found between the periodontitis and periodontally healthy groups. Treponema denticola, Neisseria mucosa, Eikenella corrodens, and Tannerella forsythia were detected in higher proportions on the Ti disks placed in the periodontitis subjects, while significant higher proportions of Capnocytophaga sputigena, Fusobacterium periodonticum, Prevotella melaninogenica, and Streptococcus mitis were detected on the Ti disks placed in the periodontally healthy group. CONCLUSIONS: The results obtained in this study shows that the composition and the proportion of the species that initially colonize Ti surfaces are highly influenced by the periodontal status more than the surface characteristics of the Ti implant.
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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 | 0.000 | 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) | 0.002 | 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".