Evaluation of bacterial diversity in the gut of piglets supplemented with probiotics using ribosomal intergenic spacer analysis
Bibliographic record
Abstract
In this study, ribosomal intergenic spacer analysis (RISA) was used to monitor changes of intestinal microbiota of piglets treated at birth with or without Pediococcus acidilactici (PA) and/or Saccharomyces cerevisiae ssp. boulardii (SCB) and weaned piglets fed with control diet supplemented with PA and/or SCB or antibiotic. During lactation, probiotics were given orally to piglets three times a week. At weaning (day 21), probiotics and the antibiotic, tiamulin, were added to the diet. Fifteen piglets per treatment were slaughtered at day 18 and day 24. The tracking of each probiotic from colonic samples was done using PCR primers specific for PA targeting the 16S rRNA gene or a specific culture medium for enumeration of SCB. The results showed the presence of probiotics in colonic samples of piglets supplemented with probiotics. Dendograms (UPGMA and Ward’s method), and non-metric multidimensional scaling analysis showed that the variability of RISA profiles in colonic microflora between individual piglets within a treatment was too high to obtain a grouping based on probiotic supplementation. Based on the relative frequency of internal transcribed spacers from RISA profiles (indicator species analysis) and diversity indices (Shannon, richness and evenness), both PA and the antibiotic treatments reduced the bacterial diversity in the gut of piglets after weaning compared with preweaning, while diversity was slightly increased postweaning compared with preweaning in control without antibiotic and SCB groups. In conclusion, all dietary additives differently affected postweaning microbial community composition; however, both antibiotic and PA reduced postweaning microbial diversity suggesting a possible benefit of PA supplementation during the postweaning transition period. Key words: Intestinal flora, piglet, PCR, probiotic, RISA profile, weaning
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 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".