The intestinal microbiome, probiotics and prebiotics in neurogastroenterology
Bibliographic record
Abstract
The brain-gut axis allows bidirectional communication between the central nervous system (CNS) and the enteric nervous system (ENS), linking emotional and cognitive centers of the brain with peripheral intestinal functions. Recent experimental work suggests that the gut microbiota have an impact on the brain-gut axis. A group of experts convened by the International Scientific Association for Probiotics and Prebiotics (ISAPP) discussed the role of gut bacteria on brain functions and the implications for probiotic and prebiotic science. The experts reviewed and discussed current available data on the role of gut microbiota on epithelial cell function, gastrointestinal motility, visceral sensitivity, perception and behavior. Data, mostly gathered from animal studies, suggest interactions of gut microbiota not only with the enteric nervous system but also with the central nervous system via neural, neuroendocrine, neuroimmune and humoral links. Microbial colonization impacts mammalian brain development in early life and subsequent adult behavior. These findings provide novel insights for improved understanding of the potential role of gut microbial communities on psychological disorders, most particularly in the field of psychological comorbidities associated with functional bowel disorders like irritable bowel syndrome (IBS) and should present new opportunity for interventions with pro- and prebiotics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".