Impact of probiotic <i>Lactobacillus</i> sp. on autochthonous lactobacilli in weaned piglets
Bibliographic record
Abstract
AIMS: This study aimed to determine whether host-adapted lactobacilli exhibit superior survival during intestinal transit relative to nomadic and free-living organisms, and to characterize the impact of probiotic lactobacilli on autochthonous lactobacilli. METHODS AND RESULTS: Mixed cultures of Lactobacillus casei K9-1 and Lactobacillus fermentum K9-2, or reutericyclin producing Lactobacillus reuteri and its isogenic mutant were fed to piglets as freeze-dried culture, or as part of fermented feed. Lactobacilli in digesta and faecal samples were quantified by strain-specific quantitative PCR (qPCR), high-resolution-melting curve qPCR, and high-throughput sequencing of 16S rRNA gene sequence tags. The abundance of the host adapted L. reuteri in digesta and faeces was higher (P < 0·05) when compared to L. casei or L. fermentum. Feed fermentation or chemical acidification of feed reduced (P < 0·05) cell counts of Lactobacillus salivarius in colonic digesta. The reutericyclin producing L. reuteri TMW1.656 transiently reduced (P < 0·05) the faecal abundance of lactobacilli. However, the overall impact of probiotic intervention on autochthonous lactobacilli was minor. CONCLUSIONS: The vertebrate host-adapted L. reuteri survives better during intestinal transit of piglets compared to L. casei and L. fermentum. SIGNIFICANCE AND IMPACT OF THE STUDY: Ecology and lifestyle of Lactobacillus strains may be suitable criteria for selection of probiotic strains for use in swine production.
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 | 0.000 | 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 | 0.001 | 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".