Modulation of Antibody-Mediated Immune Response by Probiotics in Chickens
Bibliographic record
Abstract
ABSTRACT Probiotic bacteria, including Lactobacillus acidophilus and Bifidobacterium bifidum , have been shown to enhance antibody responses in mammals. The objective of this study was to examine the effects of a probiotic product containing the above bacteria in addition to Streptococcus faecalis on the induction of the chicken antibody response to various antigens, both systemically and in the gut. The birds received probiotics via oral gavage and subsequently were immunized with sheep red blood cells (SRBC) and bovine serum albumin (BSA) to evaluate antibody responses in serum or with tetanus toxoid (TT) to measure the mucosal antibody response in gut contents. Control groups received phosphate-buffered saline. Overall, BSA and SRBC induced a detectable antibody response as early as week 1 postimmunization (p.i.), which lasted until week 3 p.i. Probiotic-treated birds had significantly ( P ≤ 0.001) more serum antibody (predominantly immunoglobulin M [IgM]) to SRBC than the birds that were not treated with probiotics. However, treatment with probiotics did not enhance the serum IgM and IgG antibody responses to BSA. Immunization with TT resulted in the presence of specific IgA and IgG antibody responses in the gut. Again, treatment with probiotics did not change the level or duration of the antibody response in the gut. In conclusion, probiotics enhance the systemic antibody response to some antigens in chickens, but it remains to be seen whether probiotics have an effect on the generation of the mucosal antibody response.
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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) | 0.000 | 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".