Microbiota of deciduous endodontic infections analysed by MDA and Checkerboard DNA-DNA hybridization
Bibliographic record
Abstract
AIMS: To evaluate the microbiota of endodontic infections in deciduous teeth by Checkerboard DNA-DNA hybridization after uniform amplification of DNA in samples by multiple displacement amplification (MDA). METHODOLOGY: Forty samples from the root canal system of deciduous teeth exhibiting pulp necrosis with or without radiographically detectable periradicular/interradicular bone resorption were collected and 32 were analysed, with three individuals contributing two samples; these were MDA-amplified and analysed by Checkerboard DNA-DNA hybridization for levels of 83 bacterial taxa. Two outcome measures were used: the percentage of teeth colonized by each species and the mean proportion of each bacterial taxon present across all samples. RESULTS: The mean amount of DNA in the samples prior to amplification was 5.2 (±4.7) ng and 6.1 (±2.3) μg after MDA. The mean number of species detected per sample was 19 (±4) (range: 3-66) to the nearest whole number. The most prevalent taxa were Prevotella intermedia (96.9%), Neisseria mucosa (65.6%), Prevotella nigrescens (56.2%) and Tannerella forsythia (56.2%). Aggregatibacter (Haemophilus) aphrophilus and Helicobacter pylori were not detected. P. intermedia (10%), Prevotella tannerae (7%) and Prevotella nigrescens (4.3%) presented the highest mean proportions of the target species averaged across the positive samples. CONCLUSION: Root canals of infected deciduous teeth had a diverse bacterial population. Prevotella sp. were commonly found with P. intermedia, Prevotella tannerae and Prevotella nigrescens amongst the most prominent species detected.
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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 | 0.001 | 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 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".