Effective Treatment of Staphylococcus aureus Intramammary Infection in a Murine Model Using the Bacteriophage Cocktail StaphLyse™
Bibliographic record
Abstract
Staphylococcus aureus causes intramammary infections (IMIs), which are refractory to antibiotic treatment and frequently result in chronic mastitis. IMIs are the leading cause of conventional antibiotic use in dairy farms. Phage therapy represents an alternative to antibiotics to help better manage mastitis in cows, reducing the global spread of resistance. A mouse mastitis model of S. aureus IMI was used to study the efficacy of a new cocktail of five lytic S. aureus-specific phages (StaphLyse™), administered either via the intramammary (IMAM) route or intravenously (IV). The StaphLyse™ phage cocktail was stable in milk for up to one day at 37 °C and up to one week at 4 °C. The phage cocktail was bactericidal in vitro against S. aureus in a dose-dependent manner. A single IMAM injection of this cocktail given 8 h after infection reduced the bacterial load in the mammary glands of lactating mice infected with S. aureus, and as expected, a two-dose regimen was more effective. Prophylactic use (4 h pre-challenge) of the phage cocktail was also effective, reducing S. aureus levels by 4 log10 CFU per gram of mammary gland. These results suggest that phage therapy may be a viable alternative to traditional antibiotics for the control of S. aureus IMIs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| 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".