Sex-specific post-inflammatory dysbiosis mediates chronic visceral pain in colitis
Bibliographic record
Abstract
BACKGROUND: Despite achieving endoscopic remission, over 20% of inflammatory bowel disease (IBD) patients experience chronic abdominal pain. Visceral pain and the microbiome exhibit sex-dependent interactions, while visceral pain in IBD shows a sex bias. Our aim was to evaluate whether post-inflammatory microbial perturbations contribute to visceral hypersensitivity in a sex-dependent manner. METHODS: Males, cycling females, ovariectomized, and sham-operated females were given dextran sodium sulfate to induce colitis and allowed to recover. Germ-free recipients received sex-appropriate and cross-sex fecal microbial transplants (FMT) from post-inflammatory donor mice. Visceral sensitivity was assessed by recording visceromotor responses to colorectal distention. The composition of the microbiota was evaluated via 16S rRNA gene V4 amplicon sequencing, while the metabolome was assessed using targeted (short chain fatty acids - SCFA) and semi-targeted mass spectrometry. RESULTS: Post-inflammatory cycling females developed visceral hyperalgesia when compared to males. This effect was reversed by ovariectomy. Both post-inflammatory males and females exhibited increased SCFA-producing species, but only males had elevated fecal SCFA content. FMT from post-inflammatory females transferred visceral hyperalgesia to both males and females, while FMT from post-inflammatory males could only transfer visceral hyperalgesia to males. CONCLUSIONS: Female sex, hormonal status as well as the gut microbiota play a role in pain modulation. Our data highlight the importance of considering biological sex in the evaluation of visceral pain.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".