Grape seed proanthocyanidin improves intestinal inflammation in canine through regulating gut microbiota and bile acid compositions
Bibliographic record
Abstract
Although certain progress has been made in treating canine inflammatory bowel disease (IBD), a large proportion of dogs have a poor prognosis and may develop resistance and side effects. Therefore, it is of great significance to prevent or alleviate canine IBD through nutritional intervention. Plant polyphenol can interact with intestinal bacteria and has important prospects in the intestinal health improvement. This study evaluated the effect of grape seed proanthocyanidin (GSP), a plant-derived natural polyphenol, on Labrador Retrievers with mild IBD. In Experiment 1 of this study, GSP alleviated persistent intestinal inflammation in canines by improving inflammatory indexes and reducing intestinal permeability. Moreover, GSP treatment increased the abundance of bacteria with potential anti-inflammatory properties and engaging bile acid metabolism, including Ruminococcaceae, Faecalibacterium, Ruminococcus_torques_group, and Lachnospiraceae_NK4A136_group. Notably, targeted metabolomic analysis identified elevated productions of fecal chenodeoxycholic acid and its microbial transformation product lithocholic acid, which might contribute to relieving canine intestinal inflammation. Further, in Experiment 2, fecal microbiota transplantation was used to determine whether gut microbiota is a potential mechanism for GSP efficacy. Dogs with mild IBD received the fecal microbiota from the group administered GSP and mirrored the improvement effects of GSP, which results verified that gut microbial alteration could be an underlying mechanism for GSP efficiency on canine IBD. Our findings highlight that the mechanism of the GSP function on canine IBD is mediated by altering gut microbial composition and improving bile acid metabolism. This study proposes a natural polyphenol-based dietary strategy for improving canine intestinal health.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".