Impact of Saccharomyces cerevisiae on the intestinal microbiota of dogs with antibiotic-induced dysbiosis
Bibliographic record
Abstract
Introduction The gut microbiota plays an important role in the health of dogs, but treatment with antibiotics causes marked dysbiosis. The objectives of this study were to evaluate the impact of yeast probiotic Saccharomyces cerevisiae supplementation on the fecal microbiota of dogs and its potential to prevent dysbiosis induced by antibiotics. Methods Twenty healthy adult dogs were divided into a control and a yeast probiotic group receiving 1g/kg of S. cerevisiae (Actisaf®, Phileo by Lesaffre, Marcq-en-Barœul, France) daily from D0 to D31. Both groups were given oral metronidazole from D11 to D17. Fecal swabs were collected on D0, 3, 11, 17, 20, 24, and 31 for microbiota analysis and blood on D0 and D24 for measurements of cytokines and cortisol. Results and discussion At D0, two distinct microbiota profiles comprised of dogs from both groups, control and probiotic, were identified. One profile had higher abundances of species related to stress and inflammation, and the other comprised species associated with good intestinal health. After three days of supplementation with yeast probiotic S. cerevisiae, all five dogs from the probiotic group having a stress-related microbiota (membership) shifted to a healthy microbiota. Metronidazole markedly changed the microbiota of both groups (p <0.001). Still, treated dogs had significantly different microbiota on D17 (end of antibiotics treatment). The dysbiosis was resolved in both groups by D24. TNF-α remarkably decreased from D0 to D24 (p = 0.002) in the probiotic group, which also had lower levels than controls on D24 (p = 0.040). There were no significant differences in the other measured cytokines. It was concluded that the use of yeast probiotic S. cerevisiae positively shifted the microbiota composition of healthy adult dogs carrying an abnormal microbial profile and that it has the potential to attenuate the dysbiosis caused by oral metronidazole.
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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 | 0.000 |
| 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".