The longitudinal effect of a multi‐strain probiotic on the intestinal bacterial microbiota of neonatal foals
Bibliographic record
Abstract
REASONS FOR PERFORMING THE STUDY: The microbiota plays a key role in health and disease. Probiotics are a potential way to therapeutically modify the intestinal microbiota and prevent disease. OBJECTIVES: The aim of this study was to investigate the effects of probiotics on the bacterial microbiota of foals during and after administration. STUDY DESIGN: Randomised placebo controlled field trial. METHODS: cfu/g) or placebo once daily for 3 weeks. A total of 3 faecal samples were collected from each foal at 2-week intervals and assessed via metagenomic sequencing. The Wilcoxon test was used to compare data between treatment groups. RESULTS: There were no changes on the phylum, order or class level between treatment groups at any age (all P>0.05) but some significant changes in relative abundance of families. Probiotic administration did not result in an increased relative abundance of lactobacilli or bifidobacteria at any age (Lactobacillus: P = 0.9, P = 0.1 and P = 0.2, Bifidobacterium: P = 0.3, P = 0.6 and P = 0.1 for Weeks 2, 4 and 6, respectively). Lactobacillus was enriched in the probiotic group at Week 6 on LEfSe analysis (linear discriminant analysis score 0.34, P = 0 .02). There was no effect on alpha diversity (all P>0.2) or community structure when parsimony and unifrac analysis were applied (all P>0.6). CONCLUSIONS: There were limited effects of probiotic treatment on the bacterial microbiota of foals. The studied probiotic based on lactobacilli and bifidobacteria has a limited potential for therapeutic modification of the gastrointestinal microbiota.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".