Influence of In-Feed Virginiamycin on the Systemic and Mucosal Antibody Response of Chickens
Bibliographic record
Abstract
Subtherapeutic and prophylactic doses of virginiamycin are capable of altering the intestinal microbiota as well as increasing several growth parameters in chickens. In spite of the fact that the microbiota plays a role in shaping the host's immune system, little information is available on the effects of in-feed antibiotics on the chicken immune system. The objective of this study was to examine the effects of an antibiotic, virginiamycin, on the development of antibody responses. Chickens were fed diets containing no antibiotics, along with either subtherapeutic (11 ppm) or prophylactic (22 ppm) doses of virginiamycin. Chickens were then immunized with keyhole limpet hemocyanin (KLH) and sheep red blood cells systemically, and with BSA and KLH orally. Although antibodies were detected against BSA in the intestinal contents of birds that were orally immunized, there was no difference among different treatment groups. Systemic IgG, and to a lesser extent IgM, antibody responses to KLH were greater (P < 0.05) in birds fed a diet containing 11 or 22 ppm of virginiamycin compared with control birds fed no antibiotic. No treatment effect was found in the sheep red blood cell-immunized birds. Results of the present study implicate virginiamycin in enhancing antibody responses to some antigens in chickens. Further studies are required to determine to what extent these effects on antibody response are mediated through changes in the composition of the microbiota.
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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".