Distinct Effects of Bovicin HC5 and Virginiamycin on in vitro Ruminal Fermentation and Microbial Community Composition
Bibliographic record
Abstract
Antibiotics are used as feed additives for cattle to alter rumen fermentation and increase weight gain. However, this practice can potentially lead to the presence of antibiotic residues in milk and meat and the selection of multiresistant bacteria. Bacteriocins have been suggested as an alternative to antibiotics used in animal production. This work aimed to evaluate the in vitro effects of bovicin HC5 and virginiamycin on ruminal fermentation and on microbial community composition. Ruminal fluid was collected from fistulated cows fed corn silage and incubated with Trypticase (15 g L-1). Cultures treated with bovicin HC5 or virginiamycin decreased (P < 0.05) ammonia accumulation by 47.46% and 66.17%, respectively. Bovicin HC5 and virginiamycin also decreased (P < 0.05) the concentration of organic acids and gas production, but the effects were somewhat distinct. Molecular fingerprinting of the microbial community using PCR-DGGE revealed that community structure varied between treatments and were distinct from the controls. These results demonstrate that bovicin HC5 and virginiamycin have distinct effects on ruminal fermentation and modify differently the microbial community composition. These results also expand the knowledge about the effects of antibiotics and bacteriocins on bacterial and archaeal communities involved in protein metabolism in the rumen.
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) | 0.000 | 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".