Neuromicrobiology, an emerging neurometabolic facet of the gut microbiome?
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 concept of the gut microbiome is emerging as a metabolic interactome influenced by diet, xenobiotics, genetics, and other environmental factors that affect the host's absorption of nutrients, metabolism, and immune system. Beyond nutrient digestion and production, the gut microbiome also functions as personalized polypharmacy, where bioactive metabolites that our microbes excrete or conjugate may reach systemic circulation and impact all organs, including the brain. Appreciable evidence shows that gut microbiota produce diverse neuroactive metabolites, particularly neurotransmitters (and their precursors), stimulating the local nervous system (i.e., enteric and vagus nerves) and affecting brain function and cognition. Several studies have demonstrated correlations between the gut microbiome and the central nervous system sparking an exciting new research field, neuromicrobiology. Microbiome-targeted interventions are seen as promising adjunctive treatments (pre-, pro-, post-, and synbiotics), but the mechanisms underlying host-microbiome interactions have yet to be established, thus preventing informed evidence-based therapeutic applications. In this paper, we review the current state of knowledge for each of the major classes of microbial neuroactive metabolites, emphasizing their biological effects on the microbiome, gut environment, and brain. Also, we discuss the biosynthesis, absorption, and transport of gut microbiota-derived neuroactive metabolites to the brain and their implication in mental disorders.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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