Identification of oral bacteria on titanium implant surfaces by 16S rDNA sequencing
Bibliographic record
Abstract
OBJECTIVE: To characterize the profile of microbial communities colonizing titanium implants with different surface treatments after exposure to the oral environment at the genus or higher taxonomic level. MATERIAL AND METHODS: Sixteen titanium disks, machined or sandblasted large-grit and acid-etched (SLA), were mounted on removable intraoral splints worn by four patients. After 24 h of intraoral exposure, biofilm samples were collected from disks and supra/subgingival teeth areas. The 16S rDNA genes from each sample were amplified, sequenced with the Miseq Illumina instrument and analyzed. RESULTS: A total of 29 genera and seven more inclusive taxa, representing the phyla Firmicutes, Proteobacteria, Fusobacteria, Bacteroidetes, Actinobacteria and candidate division TM7 were identified in both titanium surfaces and teeth. No differences were found in relation to the operational taxonomic units (OTUs) and microbial diversity, assessed by Chao 1 and Shannon indices, when comparing SLA and machined titanium surfaces. CONCLUSIONS: Machined and SLA surfaces are colonized by similar numbers of prokaryotic OTUs after 24 h of exposure to the oral environment. Higher complexity of the titanium surface topography in the initial phase of biofilm maturation does not seem to significantly influence the colonizing microbiota.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".