Biomarkers for Prediction of Bovine Respiratory Disease Outcome
Bibliographic record
Abstract
Fatal bovine respiratory disease (BRD) is frequently the result of a primary viral and a secondary bacterial respiratory infection. In cattle, BRD causes more than half of feedlot deaths and has a major impact on financial losses in the cattle industry in North America. It is, therefore, very important to understand the mechanism of this complex disease process as well to predict and identify BRD susceptible cattle to enhance treatment efficacy. We recently established the value of using combinatorial omics approaches to identify candidate biomarkers associated with stress responses, a factor that can increase the severity of BRD. The objective of the present investigation was to experimentally recreate fatal BRD and to use a combinatorial analysis of proteomic, metabonomic, and elemental profiles in serum samples to determine if multimethod analysis of these biomarkers could predict disease outcome. The proteomic studies revealed that changes in the serum proteome were significant on day 4 postviral infection when compared to preinfection (day 0) serum samples. Proteomic studies identified a group of acute phase proteins (haptoglobin and apolipoprotein AI), which could be linked to a primary viral respiratory infection, but there was no significant association observed with fatal BRD. In contrast, metabonomic and elemental analyses identified candidate biomarkers for viral infection (glucose, LDL, valine, phosphorous, and iron) and disease outcome (lactate, glucose, iron). While multivariate analysis of proteomic and metabolite profiles did not discriminate between animals that survived or died postsynergic viral-bacterial infection by analyzing preinfection (day 0) serum samples, analysis of serum elemental profiles prior to infection was, however, predictive of BRD outcome. Furthermore, discriminant analyses of all three methodologies used to profile serum (collected on day 4 postviral but prior to bacterial infection) revealed differential trends between animals that survived or died following synergic viral-bacterial infection. Thus, a combinatorial approach using proteomic, metabonomic, and elemental analyses of serum samples revealed that multimethod analysis could discriminate between the complex biological responses to secondary bacterial respiratory infection and predict disease outcome.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".