Evaluation of the prebiotic potential of polyphenol-rich plant extracts in a mouse model of colitis
Bibliographic record
Abstract
Graphical Abstract Abstract Objective: Intestinal inflammation is linked to gut dysbiosis and disrupted host–microbe interactions. This study investigated four plant extracts rich in polyphenol – pomegranate fruit peel, lemon balm leaves, cinnamon bark, and grape pomace – for their potential prebiotic activities and protective effects in a mouse model of mild intestinal inflammation. Methods: Mice received extracts orally for 4 weeks prior to colitis induction with dextran sulfate sodium. Results: All four extracts reduced colonic inflammation markers, and only grape pomace delayed disease onset. Extracts modified gut microbiota composition during colitis, differentially influencing the abundance of beneficial taxa, pathobionts, hydrogen sulfide producers, and mucus-associated species, such as Akkermansia muciniphila. Metabolomic analysis revealed extract-specific alterations in fecal metabolite profiles, particularly regarding levels of ethanol, glycine and amino acids. Colonic gene transcript profiling showed that grape pomace had the most beneficial effects on the expression of genes involved in epithelial barrier function, microbial sensing, inflammation signaling and innate immunity during colitis. Histological and imaging data further confirmed that grape pomace preserved tight-junction integrity and limited bacterial translocation during colitis. Conclusion: The findings demonstrate that the four polyphenol-rich extracts prevent colitis-induced intestinal damage, likely through modulation of gut microbiota, fecal metabolites and barrier function. Of them, grape pomace extract induced the most promising effect against colitis, combining improved clinical symptoms and restoration of host–microbiome homeostasis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".