Saffron Pre-Treatment Promotes Reduction in Tissue Inflammatory Profiles and Alters Microbiome Composition in Experimental Colitis Mice
Bibliographic record
Abstract
Inflammatory bowel disease (IBD) is a chronic inflammatory condition of the gastrointestinal tract with an incompletely understood pathogenesis. Long-standing colitis is associated with increased risk of colon cancer. Despite the availability of various anti-inflammatory and immunomodulatory drugs, many patients fail to respond to pharmacologic therapy and some experience drug-induced adverse events. Dietary supplements, particularly saffron (Crocus sativus), have recently gained an appreciable attention in alleviating some symptoms of digestive diseases. In our study, we investigated whether saffron may have a prophylactic effect in a murine colitis model. Saffron pre-treatment improved the gross and histopathological characteristics of the colonic mucosa in murine experimental colitis. Treatment with saffron showed a significant amelioration of colitis when compared to the vehicle-treated mice group. Saffron treatment significantly decreased secretion of serotonin and pro-inflammatory cytokines, such as TNF-α, IL-1β, and IL-6, in the colon tissues by suppressing the nuclear translocation of NF-κB. The gut microbiome analysis revealed distinct clusters in the saffron-treated and untreated mice in dextran sulfate sodium (DSS)-induced colitis by visualization of the Bray–Curtis diversity by principal coordinates analysis (PCoA). Furthermore, we observed that, at the operational taxonomic unit (OTU) level, Cyanobacteria were depleted, while short-chain fatty acids (SCFAs), such as isobutyric acid, acetic acid, and propionic acid, were increased in saffron-treated mice. Our data suggest that pre-treatment with saffron inhibits DSS-induced pro-inflammatory cytokine secretion, modulates gut microbiota composition, prevents the depletion of SCFAs, and reduces the susceptibility to colitis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".