Metabolites of the oral microbiome: important mediators of multikingdom interactions
Bibliographic record
Abstract
The oral cavity hosts over 700 different microbial species that produce a rich reservoir of bioactive metabolites critical to oral health maintenance. Over the last two decades, new insights into the oral microbiome and its importance in health and disease have emerged mainly due to the discovery of new oral microbial species using next-generation sequencing. This advancement has revolutionized the documentation of unique microbial profiles associated with different niches and health/disease states within the oral cavity and the relation of the oral bacteria to systemic diseases. However, less work has been done to identify and characterize the unique oral microbial metabolites that play critical roles in maintaining equilibrium between the various oral microbial species and their human hosts. This article discusses the most significant microbial metabolites produced by these diverse communities of oral bacteria that can either foster health or contribute to disease. Finally, we shed light on how advances in genomics and genome mining can provide a high-throughput platform for discovering novel bioactive metabolites derived from the human oral microbiome to tackle emerging infectious and systemic diseases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".