Influence of drinking water quality on immune responses to viral vaccines in layer chickens
Bibliographic record
Abstract
Drinking water quality is a critical factor in poultry production, and suboptimal drinking water can negatively influence immune functions leading to reduced vaccine efficacy. In the field study, we assessed drinking water quality on Alberta layer farms and evaluated its impact on vaccine-induced immune responses. Chemical, physical and microbiological analyses were performed on 26 water samples collected from Alberta layer farms and serological response for poultry viral vaccines were evaluated at the same time. The water hardness, pH, bicarbonates, and dissolved sodium exceeded acceptable limits in 34 %, 50 %, 46 %, and 27 % of the farms, respectively. Farm-level data revealed no significant direct correlation between water quality scores and vaccine induced serological response. For the controlled experiment, specific pathogen-free White Leghorn chicks were assigned to 4 groups: tap water control (TW-control), field water control (FW-control), tap water vaccinated (TW-vaccinated), and field water (FW-vaccinated), two of the groups were vaccinated against infectious bronchitis while maintaining unvaccinated controls and all 4 groups were maintained on their respective water sources for ∼8 weeks. Controlled experiments showed that vaccinated birds receiving FW had 600 units lower mean antibody titers compared to those given TW, which is statistically not significant. The vaccinated FW group also showed reduced CD4⁺ and CD8⁺ T-cell populations in the spleen and lungs, along with altered IFN-γ and significantly increased IL-10 transcription. In conclusion, although the field data showed a lack of correlation between water quality and vaccine induced serological response, the control experiment revealed that poor water quality might influence the infectious bronchitis vaccine effectiveness in layers.
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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".