Characterization of Intestinal Microbiota and Response to Dietary Virginiamycin Supplementation in the Broiler Chicken
Bibliographic record
Abstract
The inclusion of antibiotic growth promoters, such as virginiamycin, at subtherapeutic levels in poultry feeds has a positive effect on health and growth characteristics, possibly due to beneficial effects on the host gastrointestinal microbiota. To improve our understanding of the chicken gastrointestinal microbiota and the effect of virginiamycin on its composition, we characterized the bacteria found in five different gastrointestinal tract locations (duodenal loop, mid-jejunum, proximal ileum, ileocecal junction, and cecum) in 47-day-old chickens that were fed diets excluding or including virginiamycin throughout the production cycle. Ten libraries (five gastrointestinal tract locations from two groups of birds) of approximately 555-bp chaperonin 60 PCR products were prepared, and 10,932 cloned sequences were analyzed. A total of 370 distinct cpn60 sequences were identified, which ranged in frequency of recovery from 1 to 2,872. The small intestinal libraries were dominated by sequences from the Lactobacillales (90% of sequences), while the cecum libraries were more diverse and included members of the Clostridiales (68%), Lactobacillales (25%), and Bacteroidetes (6%). To assess the effects of virginiamycin on the gastrointestinal microbiota, 15 bacterial targets were enumerated using quantitative, real-time PCR. Virginiamycin was associated with increased abundance of many of the targets in the proximal gastrointestinal tract (duodenal loop to proximal ileum), with fewer targets affected in the distal regions (ileocecal junction and cecum). These findings provide improved profiling of the composition of the chicken intestinal microbiota and indicate that microbial responses to virginiamycin are most significant in the proximal small intestine.
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 | 0.000 | 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".