Analgesia and mouse strain influence neuromuscular plasticity in inflamed intestine
Bibliographic record
Abstract
BACKGROUND: Mouse models of inflammatory bowel disease (IBD) identify an impact on the enteric nervous system (ENS) but do not distinguish between Crohn's disease and ulcerative colitis phenotypes. In these models, analgesia is required, but its influence on different strains and disease outcomes is unknown. Therefore, changes to the ENS and intestinal smooth muscle were studied in trinitrobenzene sulfonic acid (TNBS) and dextran sodium sulfate (DSS) induced colitis to identify the effects of analgesia, and compared between two mouse strains. METHODS: Colitis was induced in CD1 or BALB/c mice receiving analgesia with either buprenorphine or tramadol. Euthanasia was on Day 8 (DSS) or Day 4 (TNBS). Outcomes were Disease Activity Index and cytokine assay, and quantitative histology and immunocytochemistry were used to evaluate effects of inflammation on neurons and smooth muscle. KEY RESULTS: In BALB/c mice, both models of colitis caused >2-fold increase in smooth muscle cell number. DSS caused axon proliferation without neuron loss while TNBS caused significant neuron loss and axonal damage. Buprenorphine (but not tramadol) was generally anti-inflammatory in both strains, but correlated with lethal outcomes to TNBS in BALB/c mice. CONCLUSIONS AND INFERENCES: Smooth muscle growth is common to both models of colitis. In contrast, ENS damage in TNBS is correlated with the severe response of a Crohn's disease-like phenotype, while DSS correlates with a milder, ulcerative colitis-like outcome in the deeper tissues. Analgesia with tramadol over buprenorphine is supported for mouse studies of IBD.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".