Efficacy of recombinant bovine epidermal growth factor in the treatment of experimental subclinical <i>Staphylococcus aureus</i> mastitis in a ewe model
Bibliographic record
Abstract
Staphylococcus aureus is the most common contagious mastitis pathogen of dairy cattle. Antimicrobial treatment of infected cattle results in variable cure rates. Epidermal growth factor (EGF) plays an important role in the modulation of host innate immune responses and the regulation of mammary epithelial regeneration, indicating that EGF may be useful as a treatment for mastitis. A pilot study was conducted to evaluate the efficacy of recombinant bovine EGF (rbEGF) for the treatment of S aureus intramammary infection (IMI) using an ovine model. Each ewe was experimentally infected with S aureus in both udder halves. One udder half of each ewe received one of two treatments: EGF (n=13) or pirlimycin (n=13). The contralateral udder half of each ewe received sterile saline as a control. The bacteriological cure rate following rbEGF was significantly lower (15 per cent) than that attained with pirlimycin hydrochloride (61 per cent) and did not differ from that following treatment with sterile saline. Cure rates following treatment with rbEGF were not significantly different to those following sterile saline. Given that EGF is associated with modulation of host immunity and wound healing, future studies into EGF should not focus on whether EGF increases cure rates of S aureus IMI.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".