Culturomic and quantitative real‐time<scp>‐</scp>polymerase chain reaction analyses for early contamination of abutments with different surfaces: A randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: Rough and/or plasma-activated abutments seem to be able to increase soft tissue adhesion and stability; however, limited evidence is available about bacterial contamination differences. PURPOSE: The aim was to investigate the oral microbiota on four dental abutments with different surfaces by quantitative real-time polymerase chain reaction (qRT-PCR) and culturomic approach. METHODS: Forty patients needing a single implant rehabilitation were involved in the study. Forty healing abutments, especially designed for the study, were divided into four groups according to the surface topography (1. machined [MAC], 2. machined plasma of argon treated [plasmaMAC], 3. ultrathin threaded microsurface [UTM], 4. UTM plasma of argon treated [Plasma UTM]). Random assignment was performed according to predefined randomization tables. All patients underwent surgical intervention for implant and contextual healing abutment positioning. After 2 months of healing, a sterile cotton swab was used for microbiological sampling for culturomics, while sterile paper points inserted into the sulcus were used for qRT-PCR. RESULTS: At the end of the study, 36 patients completed all procedures and a total of 36 abutments (9 per group) were analyzed. qRT-PCR retrieved data for 23 bacterial species whereas culturomics revealed the presence of 74 different bacteria, most of them not routinely included into oral cavity microbiological kits of analysis or never found before in the oral microenvironment. No statistically significant differences emerged analyzing the four different surfaces (p = 0.053). On the contrary, higher total and specific bacterial counts were detected in the plasma-treated surfaces compared to the untreated ones (p = 0.021). CONCLUSIONS: Abutments with different topographies and surface treatments resulted contaminated by similar oral bacterial flora. Abutments with moderately rough surface were not associated with a greater bacterial adhesion compared to machined ones. Conversely, more bacteria were found around plasma-treated abutments. Furthermore, data reported suggested to include new species not previously sought in the routine analyses of the oral bacterial microflora.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".