Bacterial leakage along the implant–abutment interface: culture and DNA Checkerboard hybridization analyses
Bibliographic record
Abstract
OBJECTIVE: Bacterial species have been found harboring the internal surface of dental implants as consequence of their failed connections. The aim of the present study was to compare the detection frequency of bacterial leakage from human saliva through the implant-abutment interface, under non-loading conditions, using either DNA Checkerboard or culture method. MATERIALS AND METHODS: Thirty dental implants with hexagonal platforms were connected to pre-machined abutments according to the manufacturers' specifications. The assemblies were individually incubated in human saliva under anaerobic conditions for 7 days at 37°C. Afterward, contents from the inner parts of the implants were collected and evaluated with either DNA Checkerboard (s = 15) or culture (n = 15). Subsequently, identification and quantitation of bacterial species from saliva and implants were carried out for the group evaluated with the DNA Checkerboard method. RESULTS: Both DNA Checkerboard and culture showed positive signals of bacterial leakage in 6 of the 15 evaluated samples. Capnocytophaga gingivalis and Streptococcus mutans were the most frequently detected species harboring the internal surface of the implants followed by Veillonella parvula. CONCLUSION: Occurrence of bacterial leakage along the implant-abutment interface is comparably detected with both DNA Checkerboard hybridization and conventional culture methods.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".