Predicted functional and taxonomic analysis of subgingival biofilm of Grade C periodontitis in young patients under maintenance therapy
Bibliographic record
Abstract
BACKGROUND: In Grade C periodontitis in young patients (PerioC-Y), the functional roles of the subgingival community after years of periodontal treatment are still underexplored. This study evaluated the taxonomic and predicted functional content of the subgingival microbiome of PerioC-Y patients under supportive periodontal therapy (SPT). METHODS: Clinical and microbiological data from subgingival biofilm were assessed from 10 PerioC-Y patients at two time points: at baseline and after 5.7 ± 1.3 years of SPT. This was compared with 15 patients without a history of periodontitis. The V1-V3 and V4-V5 regions of the 16S rRNA were sequenced using the Illumina Miseq. Microbial composition was evaluated by the core microbiome, and alpha- and beta-diversity. The microbiome functional content was predicted using Picrust2, and the gene differential abundance was analyzed with DESeq2. RESULTS: Clinical improvements were seen in PerioC-Y-SPT. Differences in β-diversity between PerioC-Y and health were observed (health x PerioC-Y-baseline, P = 0.02; health x PerioC-Y-SPT, P = 0.05). Moreover, although β-diversity did not statistically change between baseline and SPT in PerioC-Y, the microbial correlation evidenced increased Streptococcus and decreased Treponema network contributions during SPT. Based on predicted functional data, treatment induced a reduction in genes related to flagellar protein and signal transduction in PerioC-Y. However, compared with healthy individuals, some genes remained more highly abundant in PerioC-Y-SPT, such as quorum sensing and efflux pump transporters. CONCLUSION: Despite clinical improvements and a shift in taxonomic composition, the PerioC-Y patients' periodontal treatment was not enough to reach a similar microbiome to patients without disease experience. Some functional content in this biofilm remained altered in PerioC-Y regardless of disease control.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".