Efficacy of bacteriocin-based formula for reducing staphylococci, streptococci, and total bacterial counts on teat skin of dairy cows
Bibliographic record
Abstract
The use of teat dips is one of the most effective strategies to control mastitis by preventing new intramammary infections. Reducing bacterial load on teat skin helps control the spread of pathogens and spoilage and improves the quality of milk. The objective of this study was to evaluate the reduction of bacterial populations through the application of bacteriocin-based teat formulas. Teats of 12 Holstein cows received 2 different concentrations of bactofencin A, nisin, and reuterin alone or in combination, as well as iodine (positive control) and saline (negative control). Teat swabs were collected before and after application of teat formulas and analyzed for staphylococci, streptococci, and total bacteria counts. There were no differences for staphylococci, streptococci, and total bacterial counts for samples collected before application throughout the entire experiment. Reuterin-low and reuterin-high treatments reduced total bacterial count by 0.47 and 0.36 logs, respectively, whereas bactofencin A had no effect on any tested bacterial groups. Nisin-low treatment reduced staphylococci, streptococci, and total bacterial counts by 0.47, 0.30 and 0.50 logs, respectively. Nisin-high treatment resulted in 0.50, 0.50, and 0.47 log reduction for staphylococci, streptococci, and total bacterial counts. The bacteriocin consortium showed the highest reduction rates with 0.91, 0.54, and 0.90 log reductions obtained for staphylococci, streptococci, and total bacteria counts, respectively, for the low-concentration consortium. Similarly, the high-concentration consortium showed reduction rates with 0.95, 0.60, and 0.82 log reductions obtained for staphylococci, streptococci, and total bacteria counts, respectively. Thus, nisin and the bacteriocin consortium showed the most promise as a teat disinfectant by reducing staphylococci, streptococci, and total bacteria counts.
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) | 0.000 | 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 | 0.000 | 0.001 |
| 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".