Genetic Profiling of the Oral Microbiota Associated with Severe Early-Childhood Caries
Bibliographic record
Abstract
The determination of the composition of the microbial community in the oral cavity is usually based on cultivation methods; however, nearly half of the bacteria in the saliva and the dental plaque are not cultivable. In this study, we evaluated the difference in oral microbial diversity between children with severe early-childhood caries (S-ECC) and caries-free (CF) controls by means of a cultivation-independent approach called denaturing gradient gel electrophoresis (DGGE). Pooled dental plaque samples were collected from 20 children aged 2 to 8 years. Total microbial genomic DNA was isolated from those subjects, and a portion of the 16S rRNA gene locus was PCR amplified by using universal primers. We observed that the mean species richness of the bacterial population was greater in the CF children (n = 12) (42 +/- 3.7) than in the S-ECC children (n = 8) (35 +/- 4.3); the difference was statistically significant (P = 0.005). The overall diversity of plaque samples as measured by the Shannon index was 3.5 for the S-ECC group and 3.7 for the CF group (P = 0.004). Differences in DGGE profiles were distinguished on the basis of a cluster analysis. Sequence analysis of excised DGGE bands consisted of 2.7 phylotypes, on average. After adjusting for the number of observed bands, we estimated that the S-ECC group exhibited 94.5 total phylotypes and that the CF group exhibited 113.4. These results suggest that the microbial diversity and complexity of the microbial biota in dental plaque are significantly less in S-ECC children than in CF children.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".