The Interaction of Implant Luting Cements and Oral Bacteria Linked to Peri‐Implant Disease: An In Vitro Analysis of Planktonic and Biofilm Growth – A Preliminary Study
Bibliographic record
Abstract
BACKGROUND: There is little consensus on the most appropriate cement to use when restoring a cement-retained, implant-supported restoration. One consideration should be the interaction of pathogenic oral bacteria with restorative cements. PURPOSE: To determine how oral bacteria associated with peri-implant disease grow in the presence of implant cements. MATERIALS AND METHODS: Five test cements with varying composition (zinc oxide-eugenol [TBO], eugenol-free zinc oxide [TBNE], zinc orthophosphate [FL], and two resin cements [PIC and ML]) were used to fabricate specimen disks. The disks were submerged in bacterial suspensions of either Aggregatibacter actinomycetemcomitans, Fusobacterium nucleatum, or Porphyromonas gingivalis. Planktonic bacterial growth within the test media was measured by determining the optical density of the cultures (OD600 ). Positive controls (media and bacteria without cement disks) and negative controls (media alone) were similarly evaluated. The mean and standard deviations (SD) were calculated for planktonic growth from three separate experiments. ANOVA statistical analysis with post hoc Tukey tests was performed where differences existed (p < .05). Selected cement disks (TBO and ML) were further examined for bacterial biofilm growth. Surface bacteria were removed and grown on agar media, and colony-forming units (CFUs) were quantified. RESULTS: Planktonic growth for both A. actinomycetemcomitans and P. gingivalis was significantly inhibited (p < .05) when grown in the presence of cement disks consisting of TBNE, PIC, FL, and TBO. In contrast, neither of these bacteria displayed growth inhibition in the presence of ML cement disks. F. nucleatum growth was also significantly inhibited by PIC, FL. and TBO (p < .05), but not by ML and TBNE cement disks. CFU counts for the biofilm study for TBO gave minimal and, in some instances, no bacterial adherence and growth, in contrast to ML, which supported substantially greater bacterial biofilm growth. CONCLUSION: Cements display differing abilities to inhibit both planktonic and biofilm bacterial growth. Cements with the ability to reduce planktonic or biofilm growth of the test bacteria may be advantageous in reducing peri-implant disease. Understanding the microbial growth-inhibiting characteristics of different cement types should be considered important in the selection criteria.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".