Correlation Between Fungal and Bacterial Populations in Periodontitis Through Targeted Sequencing: A Pilot Study
Bibliographic record
Abstract
Background and Objective: The oral microbiome plays an important role in oral health and disease, including periodontitis, which affects about 40% of the adult population in the United States. Bacterial pathogens have been well studied and documented in their relationship with periodontitis; however, the role of fungi in periodontitis is still unclear. The purpose of this study is to determine the relationship of specific fungal species with periodontal pathogenic bacteria in healthy, mild periodontitis, and severe periodontitis patients. Methods: In this study, human participants were recruited, and saliva samples were collected. Twelve participants representing periodontal health (n = 2), mild periodontitis (n = 3), and severe periodontitis (n = 7) were included. Salivary samples were sequenced for analysis of their mycobiome (ITS sequencing) and microbiome (16s RNA sequencing). Results: A total of 375 species of bacteria and 39 species of fungi were identified among all samples. Clustering was observed for bacteria in healthy and mild periodontitis, but more variability was observed in the severe periodontal disease group. Variability was observed for fungi among all samples and groups. Red complex bacteria were negatively correlated with Candida species for the disease groups, although the correlation was not statistically significant. A significant correlation was observed between red-complex bacteria in the severe periodontal disease group. Additionally, a significant correlation was observed among Candida species in all groups. Conclusions: This pilot study simultaneously processed saliva samples for microbiome and mycobiome sequencing and found a trend towards negative correlation between Candida species and red complex bacteria.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".