Characteristic levels of cytokines and bacteria cause increased gut motility and physical damage in the gastrointestinal system of indomethacin-treated rats
Bibliographic record
Abstract
Inflammatory bowel disease (IBD) presents with chronic intestinal inflammation, which can be replicated in animal model systems with indomethacin treatment. Minimal research has been done to understand the impact of indomethacin treatment and subsequent inflammation on the intestinal system, particularly the microbiome diversity, cytokine levels, and contractile activity. Indomethacin-treated rats were tested for the presence of certain gut bacteria and demonstrated significantly depleted Candidatus savagella and Lactobacillus populations in treated feces samples (P = 0.02 and 0.01, respectively). ELISA assays were run to visualize IL-1a, IL-1b, and IL-6 concentrations in serum samples of control and indomethacin-treated rats, and all three types of cytokines had a significantly increased presence in treated rats (P = 0.001). Contractile activity was measured using control and treated ileum tissues mounted in organ baths. The addition of acetylcholine at its EC50 caused a significant increase in contractility of the indomethacin-treated ileum compared to its control counterpart (P = 0.04). Together, these results suggest that indomethacin treatment increases cytokine levels, depletes Candidatus savagella and Lactobacillus populations in the feces, and increases contractility in the gut, which suggests a more distinct pattern for which to use in disease diagnosis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".