Interleukin-6 in quarter milk as a further prediction marker for bovine subclinical mastitis
Bibliographic record
Abstract
Subclinical mastitis (SM) is a major concern in the dairy industry because it causes economic losses and can lead to clinical mastitis. The mechanisms of the onset and progression of SM are not fully understood, and a new procedure for the detection and appropriate prediction of SM leading to clinical mastitis is necessary for dairy cow management. Inflammatory cytokines such as interleukin (IL)-6 are known to be present in inflamed mammary glands at the onset of mastitis, and IL-6 concentrations correlate with the levels of inflammation. In this study, the detection of IL-6 was examined for the evaluation for the future prediction of SM in 77 quarter milk samples from 20 cows. IL-6 concentrations in quarter milk were measured by sandwich ELISA, and the data were compared with milk somatic cell count (SCC) levels to diagnose SM. Average IL-6 concentration was significantly higher in SM quarter milk (207·0 ± 441·6 pg/ml) than in healthy control quarter milk (12·6 ± 33·4 pg/ml, P<0·01). Results of the cross-tabulation table show that SM prediction accuracy based on IL-6 concentration was almost equal or superior to SM prediction accuracy based on SCC. The detection of IL-6 in milk indicated SM earlier than did the detection of elevated SCC. Thus, the detection of IL-6 in milk could be a future prediction marker for SM.
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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 | 0.005 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".