Sex-Based Diverse Plaque Microbiota in Children with Severe Caries
Bibliographic record
Abstract
Severe early childhood caries (S-ECC) is a multifactorial disease that can lead to suffering and reduced oral health–related quality of life in young children. The bacterial and fungal composition of dental plaque and how children’s sex is associated with S-ECC are largely unknown. In this study, V4-16S rRNA and ITS1 rRNA gene amplicon sequencing was used to compare the plaque bacteriome and mycobiome of children <72 mo of age: 40 with S-ECC (15 males, 25 females) and 40 caries-free (19 males, 21 females). Health- and nutrition-related questionnaire data were also investigated. This study aimed to analyze potential sex-based differences in the supragingival plaque microbiota of young children with S-ECC and those caries-free. Behavioral and nutritional habit differences were observed between children with S-ECC and those caries-free and between male and female children. Overall, higher levels of Veillonella dispar, Streptococcus mutans, and other bacterial species were found in the S-ECC group as compared with caries-free controls ( P < 0.05). A significant difference in the abundance of Neisseria was observed between males and females with S-ECC ( P < .05). Fungal taxonomic analysis showed significantly higher levels of Candida dubliniensis in the plaque of children with S-ECC as compared with those caries-free ( P < 0.05), but no differences were observed with Candida albicans ( P > 0.05). Significant differences in the relative abundance of Mycosphaerella, Cyberlindnera, and Trichosporon fungal species were also observed between the caries-free and S-ECC groups ( P < 0.05). Machine learning analysis revealed the most important bacterial and fungal species for classifying S-ECC versus caries-free. Different patterns of crosstalk between microbial species were observed between male and female children. Our work demonstrates that plaque microbiota and sex may be important determinants for S-ECC and could be factors to consider for inclusion in caries risk assessment tools.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".