Susceptibility to Nisin, Bactofencin, Pediocin and Reuterin of Multidrug Resistant Staphylococcus aureus, Streptococcus dysgalactiae and Streptococcus uberis Causing Bovine Mastitis
Bibliographic record
Abstract
Antibiotics are the most effective strategy to prevent and treat intramammary infections. However, their misuse has led to the dissemination of multidrug resistant bacteria (MDR) for both animals and humans. Efforts to develop new alternative strategies to control bacterial infections related to MDR are continuously on the rise. The objective of this study was to evaluate the antimicrobial activity of different bacteriocins and reuterin against MDR Staphylococcus and Streptococcus clinical isolates involved in bovine mastitis. A bacterial collection including S. aureus (n = 19), S. dysgalactiae (n = 17) and S. uberis (n = 19) was assembled for this study. Antibiotic resistance profiles were determined by the disk diffusion method. In addition, sensitivity to bacteriocins and reuterin was evaluated by determining minimum inhibitory concentrations (MIC). A total of 21 strains (37.5%) were MDR. MICs ranged from ≤1.0 μg/mL to ≥100 μg/mL for nisin and 2.0 to ≥250 μg/mL for bactofencin. Reuterin was active against all tested bacteria, and MICs vary between 70 and 560 μg/mL. Interestingly, 20 MDR strains were inhibited by bactofencin at a concentration of ≤250 μg/mL, while 14 were inhibited by nisin at an MIC of ≤100 μg/mL. Pediocin did not show an inhibitory effect.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".