The effect of staphylococcal mastitis including resistant strains on serum procalcitonin, neopterin, acute phase response and stress biomarkers in Holstein dairy cows
Bibliographic record
Abstract
Staphylococcal mastitis (SM) is a frequent disease in the dairy cattle that is costly to treat. This study aimed to investigate the alterations in the levels of procalcitonin (PCT), neopterin (NPT), haptoglobin (HP), serum amyloid A (SAA), proinflammatory cytokines (IL-1β, IL-8, TNF-α, IF-γ) and oxidative stress (OS) biomarkers in Holstein dairy cows with SM under field conditions. In addition, we also evaluated the role of examined biomarkers in disease pathogenesis and their use as diagnostic biomarkers for the disease in dairy cows. Fifty-three dairy cows with SM, including those with infections caused by Staphylococcus aureus (n = 42) and methicillin resistant S. aureus (MRSA) (n = 11) were selected for this study. In addition, 20 healthy dairy cows were enrolled as a control group. Higher serum levels of PCT, NP, IL-1β, IL-8, TNF-α, IF-γ, HP and SAA and a state of OS was detected in SM group in comparison with the controls. Moreover, the levels of all examined biomarkers in mastitic cows with S. aureus when compared with those infected with MRSA was not significantly different. All examined biomarkers demonstrated a significant degree of discrimination between SM cows and healthy controls (the area under the curve (AUC) ranged from 83.6 for SAA to 100 for PCT). Our study showed that SM in dairy cows was associated with substantial changes in serum PCT, NPT, Acute phase proteins (APPs), proinflammatory cytokines, and OS levels. This study demonstrates that clinical examination in tandem with quantification of PCT, NPT, APPs and cytokines, OS biomarkers could be a useful assessment tool for SM in dairy cows.
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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".