Plasma cytokine and chemokine levels during natural outbreaks of bovine respiratory disease in young bulls on feedlots
Bibliographic record
Abstract
Bovine Respiratory Disease (BRD) is a leading cause of morbidity and mortality in young cattle upon feedlot arrival. The immune response plays a dual role in infection control and lung tissue damage, but few studies have assessed cytokine levels during natural BRD outbreaks. Advances in multiplexed assays now allow for broader cytokine and chemokine profiling in cattle. In this nested case–control study, 184 young bulls from nine French fattening farms were clinically assessed and underwent thoracic ultrasonography (TUS) weekly during the first month on feed. BRD cases ( n = 98) and matched healthy controls ( n = 86) were selected based on clinical signs. Fifteen cytokines and chemokines were quantified in plasma using a bovine-specific bead-based multiplex assay, on the day an animal was first detected as sick and in its matched control sampled on the same day. BRD-associated pathogens were assessed using qPCR on nasal swabs and paired serology. The link between cytokines and clinical, microbiological, and preconditioning (vaccination and preventive antibiotic treatment) variables was investigated using the Wilcoxon-Mann–Whitney test, mixed-effects linear regression models, and multivariate clustering. Cytokine and chemokine levels did not distinguish clinically sick from healthy animals. No specific cytokine profile was associated with infection by a given pathogen. However, IL-17A and IFN- γ concentrations were positively associated with treatment relapse and disease severity, suggesting that they may have prognostic potential. Cluster analysis revealed three subgroups with distinct cytokine patterns and health outcomes, in association with preconditioning variables, highlighting the critical role of these interventions in shaping the immune response during BRD outbreaks. This study is the first to report the measurement of such a wide range of cytokines during spontaneous BRD episodes in young bulls. While not diagnostic when considered individually, cytokine profiles may hold prognostic value and could be integrated into multimodal risk stratification tools, in combination with clinical, microbiological data, and TUS results, to improve BRD management in the field. Preconditioning practices, such as vaccination or preventive antibiotic administration, significantly influence early immune responses and should further be investigated to refine prevention strategies and individualize health monitoring protocols at feedlot entry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".