Probiotics Stimulate Production of Natural Antibodies in Chickens
Bibliographic record
Abstract
Commensal bacteria in the intestine play an important role in the development of immune response. These bacteria interact with cells of the gut-associated lymphoid tissues (GALT). Among cells of the GALT, B-1 cells are of note. These cells are involved in the production of natural antibodies. In the present study, we determined whether manipulation of the intestinal microbiota by administration of probiotics, which we had previously shown to enhance specific systemic antibody response, could affect the development of natural antibodies in the intestines and sera of chickens. Our findings demonstrate that when 1-day-old chicks were treated with probiotics, serum and intestinal antibodies reactive to tetanus toxoid (TT) and Clostridium perfringens alpha-toxin in addition to intestinal immunoglobulin A (IgA) reactive to bovine serum albumin (BSA) were increased in unimmunized chickens. Moreover, IgG antibodies reactive to TT were increased in the intestines of probiotic-treated chickens compared to those of untreated controls. In serum, IgG and IgM reactive to TT and alpha-toxin were increased in probiotic-treated, unimmunized chickens compared to levels in untreated controls. However, no significant difference in serum levels of IgM or IgG response to BSA was observed. These results are suggestive of the induction of natural antibodies in probiotic-treated, unimmunized chickens. Elucidating the role of these antibodies in maintenance of the chicken immune system homeostasis and immune response to pathogens requires further investigation.
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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".