Moody microbes or fecal phrenology: what do we know about the microbiota-gut-brain axis?
Bibliographic record
Abstract
INTRODUCTION: The microbiota-gut-brain axis is a term that is commonly used and covers a broad set of functions and interactions between the gut microbiome, endocrine, immune and nervous systems and the brain. The field is not much more than a decade old and so large holes exist in our knowledge. DISCUSSION: At first sight it appears gut microbes are largely responsible for the development, maturation and adult function of the enteric nervous system as well as the blood brain barrier, microglia and many aspects of the central nervous system structure and function. Given the state of the art in this exploding field and the hopes, as well as the skepticism, which have been engendered by its popular appeal, we explore recent examples of evidence in rodents and data derived from studies in humans, which offer insights as to pathways involved. Communication between gut and brain depends on both humoral and nervous connections. Since these are bi-directional and occur through complex communication pathways, it is perhaps not surprising that while striking observations have been reported, they have often either not yet been reproduced or their replication by others has not been successful. CONCLUSIONS: We offer critical and cautionary commentary on the available evidence, and identify gaps in our knowledge that need to be filled so as to achieve translation, where possible, into beneficial application in the clinical setting.
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.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.015 | 0.026 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".