Metabolites of the oral microbiome: important mediators of multikingdom interactions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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