Microbiota of deciduous endodontic infections analysed by MDA and Checkerboard DNA-DNA hybridization
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it