Review: Effect of probiotics on gastrointestinal function: evidence from animal models
Bibliographic record
Abstract
The digestive tract works through a complex net of integrative functions. At the level of the gut, this integration occurs between the immune, neuromotor and endocrine systems, the intestinal barrier and gut luminal contents. Gastrointestinal function is controlled and coordinated by the central nervous system to ensure effective motility, secretion, absorption and mucosal immunity. Thus, it is clear that the gut keeps a tightly regulated equilibrium between luminal stimuli, epithelium, immunity and neurotransmission in order to maintain homeostasis. It follows that perturbations of any of these systems may lead to gut dysfunction. While we acknowledge that the gut-brain axis is crucial in determining coordinated gut function, in this review we will focus on peripheral mechanisms that influence gastrointestinal physiology and pathophysiology. We will discuss the general hypothesis that the intestinal content is crucial in determining what we consider normal gastrointestinal physiology, and consequently that alteration in luminal content by dietary, antibiotic or probiotic manipulation can result in changes in gut function. This article focuses on lessons learned from animal models of gut dysfunction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.004 | 0.002 |
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".