Negative influence of suboptimal quality of drinking water on avian coronavirus pathogenesis and immune response: A Controlled Study
Bibliographic record
Abstract
This study investigated the impact of poor drinking water quality on infectious bronchitis virus (IBV) pathogenesis. Drinking water samples from Alberta layer farms were assessed based on physical, chemical, and microbiological properties. The highest-scoring field water (FW), which is suboptimal with higher pH, hardness and bicarbonate concentration was selected, transported in clean containers, and used in this control experiment. Forty-eight specific pathogen free White Leghorn chicks were divided into four groups: Tap water non-infected (TW-control), field water non-infected (FW-control), tap water infected (TW-infected), and field water infected (FW-infected). They were maintained on their respective water types for 7 weeks. The IBV genome load was significantly higher in the lungs of the FW-infected when compared to TW-infected group at 4 days post-infection (dpi). The histopathological lesion scores in the trachea and lungs were higher in the FW-infected birds when compared to the uninfected controls at observed time points. However, the histopathological lesion scores in the trachea and lungs of the TW-infected birds were not different when compared to that of FW-infected group. In the lungs, the CD4 + and CD8 + T cell populations were significantly higher in the TW-infected group at observed time points when compared to uninfected controls. However, the CD4 + and CD8 + T cell populations in lungs of the FW-infected birds were not different when compared to that of TW-infected group. In the spleen, CD4 + and CD8 + T cell populations were significantly higher in TW-infected and FW-infected birds when compared to uninfected controls depending on the observed time point and we did not observe differences in CD4 + and CD8 + T cell populations in spleen between TW-infected and FW-infected birds. These findings suggest that sub-optimal drinking water can exacerbate IBV infection by weakening immune responses and increasing disease severity. Further studies are necessary to observe the effect of suboptimal water quality on the development of vaccine-mediated immune response. Understanding these interactions is key for improving water management strategies for maintaining poultry health and productivity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".