Use of checkerboard DNA–DNA hybridization to evaluate the internal contamination of dental implants and comparison of bacterial leakage with cast or pre‐machined abutments
Bibliographic record
Abstract
AIMS: To evaluate the checkerboard DNA-DNA hybridization method for detection and quantitation of bacteria from the internal parts of dental implants and to compare bacterial leakage from implants connected either to cast or to pre-machined abutments. MATERIALS AND METHODS: Nine plastic abutments cast in a Ni-Cr alloy and nine pre-machined Co-Cr alloy abutments with plastic sleeves cast in Ni-Cr were connected to Branemark-compatible implants. A group of nine implants was used as control. The implants were inoculated with 3 microl of a solution containing 10(8) cells/ml of Streptococcus sobrinus. Bacterial samples were immediately collected from the control implants while assemblies were completely immersed in 5 ml of sterile Tripty Soy Broth (TSB) medium. After 14 days of anaerobic incubation, occurrence of leakage at the implant-abutment interface was evaluated by assessing contamination of the TSB medium. Internal contamination of the implants was evaluated with the checkerboard DNA-DNA hybridization method. RESULTS: DNA-DNA hybridization was sensitive enough to detect and quantify the microorganism from the internal parts of the implants. No differences in leakage and in internal contamination were found between cast and pre-machined abutments. Bacterial scores in the control group were significantly higher than in the other groups (P<0.05). CONCLUSION: Bacterial leakage through the implant-abutment interface does not significantly differ when cast or pre-machined abutments are used. The checkerboard DNA-DNA hybridization technique is suitable for the evaluation of the internal contamination of dental implants although further studies are necessary to validate the use of computational methods for the improvement of the test accuracy.
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 | 0.002 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".