Somatic Cell Count in Milk of Selenium‐supplemented Dairy Cows after an Intramammary Challenge with <i>Staphylococcus aureus</i>
Bibliographic record
Abstract
The objective of this study was to evaluate the effect of selenium (Se) supplementation on milk somatic cell count (SCC) in dairy cows. Twelve multiparous Holstein-Friesian cows were fed a diet containing a suboptimal Se concentration (<0.05 ppm, dry basis) starting 2 months before calving. Supplemented cows (n=6) received a single s.c. injection of barium selenate (1 ml/50 kg BW) 45 days prior to calving, whereas control group was kept unsupplemented. Twenty weeks after calving, two mammary quarters (right side) of each cow were challenged with 205,000 cfu/ml of Staphylococcus aureus (strain Newbould 305). Blood was collected bi-weekly until day 150 of lactation for the analysis of blood glutathione peroxidase (GPx1; EC 1.11.1.9) activity. To re-isolate the challenging pathogen and to evaluate SCC, aseptic milk samples were collected daily starting on the day of challenge, and finishing 7 days after inoculation. Unsupplemented cows had a lower activity of GPx1 through the experiment (P<0.001). Natural log SCC (lnSCC) was higher in unsupplemented than Se-supplemented cows (P=0.04), showing evidence of significance after 5 days. Selenium supplementation of dairy cows fed a diet containing a suboptimal Se concentration, resulted in higher blood activity of GPx1, and lower mean lnSCC after an intramammary challenge with Staph. aureus.
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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) | 0.001 | 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".