Submucosal Microbiome Profiles in Paired and Unpaired Samples From Healthy and Peri‐Implantitis Dental Implants
Bibliographic record
Abstract
BACKGROUND: This cross-sectional study aimed to compare the composition of the submucosal microbiome of peri-implantitis with paired and unpaired healthy implant samples. METHODS: We evaluated submucosal plaque samples obtained in 39 cases, including 13 cases of peri-implantitis, 13 cases involving healthy implants from the same patient (paired samples), and 13 cases involving healthy implants from different individuals (unpaired samples). The patients were evaluated using next-generation genomic sequencing (Illumina) based on 16S rRNA gene amplification. The sequences were grouped according to the amplicon sequence variant (ASV) to define the taxonomic categories. Alpha diversity was analyzed using Shannon's and Simpson's indices, while beta diversity was evaluated using principal coordinate analysis, analysis of similarities, and permutational multivariate variance analysis. Additionally, UniFrac distances were evaluated using Quantitative Insights into Microbial Ecology 2. Finally, we evaluated between-group differences in the taxonomic components. RESULTS: There were no significant between-group differences in alpha diversity. The average bacterial ratios of Filifactor alocis, Porphyromona endodontalis, Tannerella forsythia, Treponema denticola, Peptostreptococcaceae [Eubacterium nodatum], Desulfobulbus sp. HTM 041, and Mogibacterium timidum significantly differed between peri-implantitis samples and unpaired samples from the healthy implants (p < 0.05). However, there were few differences in the microbiota between peri-implantitis samples and those paired with healthy implants in the same patient. CONCLUSIONS: Future studies comparing the microbiome compositions using sequencing techniques between healthy implants and implants with peri-implantitis should focus on retrieving samples from the same patient, especially in individuals with a history of periodontitis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".