Assessing Probiotic Efficacy: Short-Term Impact on Canine Gut Microbiota Using an In Vitro Colonic Fermentation Model
Bibliographic record
Abstract
In dogs, gut microbiome dysbiosis is associated with several health conditions, including gastrointestinal disease. Probiotic supplementation can support a balanced gut microbiome. This study assessed the impact of a probiotic containing a mixture of Lacticaseibacillus casei, Limosilactobacillus fermentum, Levilactobacillus brevis, and Enterococcus faecium on the gut microbiota of six dogs using short-term colonic simulations. Two groups were included, i.e., blank versus supplementation with the test product, and incubated for 48 h. Probiotic-supplemented reactors had significantly greater fermentative activity compared with the blank, as shown by lower pH levels and higher gas pressure after 6 h, 24 h, and 48 h of incubation (p < 0.05 for all). Saccharolytic fermentation also increased, with a significantly higher level of acetate at 24 h and propionate at 6 h, 24 h, and 48 h with the test product versus blank (p < 0.05 for all). There was no significant effect of the test product on alpha-diversity, but beta-diversity analysis revealed a clear separation in the microbial community composition between the test product and blank. Eight bacterial taxa were enriched with test product supplementation, including the probiotic test strains as well as Megamonas and Bacteroides species. This study, using in vitro short-term colon simulations with six canine donors, provides insights into the probiotic characteristics of the test product.
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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 | 0.000 | 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 | 0.000 |
| 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".