Use of procalcitonin, neopterin, haptoglobin, serum amyloid A and proinflammatory cytokines in diagnosis and prognosis of bovine respiratory disease in feedlot calves under field conditions
Bibliographic record
Abstract
Bovine respiratory diseases (BRD) have long been considered a serious problem that causes major economic losses in feedlot calves (FC). This study aimed to determine the diagnostic and prognostic effect of selected biological markers including, procalcitonin (PCT), neopterin (NP), proinflammatory cytokines (IL-1β, IL-8, TNF-α, IF-γ), haptoglobin (HP) and serum amyloid A (SAA) on FC with BRD under field conditions. Sixty-nine FC that were identified to be infected with Mannheimia haemolytica and Histophilus somni and had different clinical respiratory signs (diseased group) were selected for this study. In addition, 20 healthy FC have been selected as a control group. We have detected higher serum levels of PCT, NP, HP, SAA, IL-1β, IL-8, TNF-α and IF-γ in diseased FC group compared with the control group. All tested markers revealed a high level of discrimination between BRD infected FC and healthy ones (AUC > 0.90). Moreover, the obtained data showed a high degree of prognostic accuracy for PCT, NP, IL-8, HP, IF-γ and IL-1β in predicting treatment response of FC with BRD at the selected thresholds (AUC = 0.99, 0.99, 0.97, 0.93, 0.88 and 0.82, respectively). Significant inhibition was observed for the selected biochemical markers in treated FC 7 days post-treatment. In conclusion, this study showed that BRD in FC was associated with significant alterations in serum APPs, proinflammatory cytokines, PCT and NPT levels. Furthermore, it demonstrated that these serum biomarkers are much higher in FC with BRD compared to recovered ones. Our data suggest that the measurement of PCT, NPT, APPs and cytokines together with the clinical examination may be a useful diagnostic and prognostic tool for assessment of FC naturally infected with M. haemolytica and H. somni.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".