ln-vitro models for the culture of previously uncultured oral bacteria
Bibliographic record
Abstract
Around half of oral bacteria have yet to be cultured, and their role in disease is therefore unknown. It is hypothesised that bacteria in biofilms have become dependent on growing in multi-species communities. TM7 phylum has no cultured representatives and some oral TM7 phylotypes have been associated with oral diseases such as periodontitis. The aims of this study were therefore to evaluate the ability of two model culture systems: Cooked Meat Medium and the Calgary Biofilm Device (CBD), to support the growth of mixed oral bacterial communities including uncultured bacteria, and to attempt to culture representatives of the TM7 division. The Cooked Meat Medium was used to establish a mixed bacterial community from 3 endodontic samples and their composition was analysed by Sanger sequencing and 454 pyrosequencing. A diverse bacterial community closely related to the original endodontic samples was maintained up to 480 days and included some uncultured bacteria present in the original samples. A mixed oral biofilm was established on the CBD from saliva. The effect of the presence of mucin and glucose in the growth media on community composition was evaluated, but no significant differences were seen. The effect of using propidium monoazide to remove extracellular DNA was assessed and was found to significantly affect the perceived composition of the biofilms. Uncultured taxa detected in culture included representatives of deep branches of Bacteroidetes and Clostridiales, and TM7 and SR1 phyla. TM7 members were detected in both models with specific PCR primers, but their proportion never exceeded 1 %. In an attempt to isolate TM7 division representatives a saliva microcosm was grown on agar. TM7 representatives were detected by colony hybridization and specific PCR and subcultured, producing enrichment. Two simple co-cultures of TM7 HOT352 / HOT353 with Slackia exigua or Atopobium parvulum were obtained, but were not maintained.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".