Clonal Analysis of the Microbiota of Severe Early Childhood Caries
Bibliographic record
Abstract
BACKGROUND/AIMS: Severe early childhood caries is a microbial infection that severely compromises the dentition of young children. The aim of this study was to characterize the microbiota of severe early childhood caries. METHODS: Dental plaque samples from 2- to 6-year-old children were analyzed using 16S rRNA gene cloning and sequencing, and by specific PCR amplification for Streptococcus mutans and Bifidobacteriaceae species. RESULTS: Children with severe caries (n = 39) had more dental plaque and gingival inflammation than caries-free children (n = 41). Analysis of phylotypes from operational taxonomic unit analysis of 16S rRNA clonal metalibraries from severe caries and caries-free children indicated that while libraries differed significantly (p < 0.0001), there was increased diversity than detected in this clonal analysis. Using the Human Oral Microbiome Database, 139 different taxa were identified. Within the limits of this study, caries-associated taxa included Granulicatella elegans (p < 0.01) and Veillonella sp. HOT-780 (p < 0.01). The species associated with caries-free children included Capnocytophaga gingivalis (p < 0.01), Abiotrophia defectiva (p < 0.01), Lachnospiraceae sp. HOT-100 (p < 0.05), Streptococcus sanguinis (p < 0.05) and Streptococcus cristatus (p < 0.05). By specific PCR, S. mutans (p < 0.005) and Bifidobacteriaceae spp. (p < 0.0001) were significantly associated with severe caries. CONCLUSION: Clonal analysis of 80 children identified a diverse microbiota that differed between severe caries and caries-free children, but the association of S. mutans with caries was from specific PCR analysis, not from clonal analysis, of samples.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".