On communication between gut microbes and the brain
Bibliographic record
Abstract
PURPOSE OF REVIEW: Interest in the microbiota-gut-brain axis is increasing apace and what was, not so long ago, a hypothetical relationship is emerging as a potentially critical factor in the regulation of intestinal and mental health. Studies are now addressing the neural circuitry and mechanisms underlying the influence of gut bacteria on the central nervous system and behavior. RECENT FINDINGS: Gut bacteria influence development of the central nervous systems (CNS) and stress responses. In adult animals, the overall composition of the microbiota or exposure to specific bacterial strains can modulate neural function, peripherally and centrally. Gut bacteria can provide protection from the central effects of infection and inflammation as well as modulate normal behavioral responses. Behavioral effects described to date are largely related to stress and anxiety and an altered hypothalamus-pituitary-adrenal axis response is a common observation in many model systems. The vagus nerve has also emerged as an important means of communicating signals from gut microbes to the CNS. SUMMARY: Studies of microbiota-gut-brain communication are providing us with a deeper understanding of the relationship between the gut bacteria and their hosts while also suggesting the potential for microbial-based therapeutic strategies that may aid in the treatment of mood disorders.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".