Animal models of visceral pain and the role of the microbiome
Bibliographic record
Abstract
Visceral pain refers to pain arising from the internal organs and is distinctly different from the expression and mechanisms of somatic pain. Diseases and disorders with increased visceral pain are associated with significantly reduced quality of life and incur large financial costs due to medical visits and lost work productivity. In spite of the notable burden of illness associated with those disorders involving increased visceral pain, and some knowledge regarding etiology, few successful therapeutics have emerged, and thus increased attention to animal models of visceral hypersensitivity is warranted in order to elucidate new treatment opportunities. Altered microbiota-gut-brain (MGB) axis communication is central to the comorbid gastrointestinal/psychiatric diseases of which increased visceral (intestinal) sensitivity is a hallmark. This has led to a particular focus on intestinal microbiome disruption and its potential role in the etiology of heightened visceral pain. Here we provide a review of studies examining models of heightened visceral pain due to altered bidirectional communication of the MGB axis, many of which are conducted on a background of stress exposure. We discuss work in which the intestinal microbiota has either been directly manipulated (as with germ-free, antibiotic, and fecal microbial transplantation studies) or indirectly affected through early life or adult stress, inflammation, and infection. Animal models of visceral pain alterations with accompanying changes to the intestinal microbiome have the highest face and construct validity to the human condition and are the focus of the current review.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".