Supplementation of a new combination of prebiotic and postbiotic shapes fecal microbiota of old dogs while influencing immune parameters
Bibliographic record
Abstract
Healthy senior dogs were subjected for 77 days to a dietary regime with (or without) prebiotic + postbiotic mixture composed by short-chain fructo-oligosaccharides and yeast fractions (scFOS+). Their fecal microbiota was studied and the links between the microbiota and immune parameters were investigated after Lyme vaccination. All along the study results showed a clear modulation of the microbiota with a higher relative abundance (RA) for Megamonas spp., Bacteroidaceae, Bacteroidetes plebeius, Clostridiales, Phascolarctobacterium, Succinivibrionaceae, Fusobacterium spp. in feces from scFOS + dogs. An amplicon sequence variants (ASVs) index (Enterobacteriaceae + Clostridium spiroforme vs. Fusobacterium + Megamonas) was developed and found significantly different between the groups with scFOS + dogs showing a lower index suggesting a possible modulation of the physico-chemical environment in the gut, favoring the growth of strict anaerobes producing short-chain fatty acids. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) analysis revealed stimulation of propionate and acetate production pathways, vitamin biosynthesis (vitamin B2, B5 and B9 precursors) and pathways related to tricarboxylic acid cycle in the scFOS + group. The RA of several ASVs from Bacteroidetes and Fusobacteria families were found moderately negatively correlated to IgA and IgG concentrations (P < 0.05). In particular, Megamonas and Phascolarctobacterium appeared as interest genera. In summary, scFOS + is a good candidate to support the health of elderly dogs through microbiota changes. Metabolomics or in vitro mechanistic experiments will be crucial to further understand the mechanisms at play.
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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".