Evaluation of antimicrobial compounds to inhibit growth of select Gram-positive pathogenic or antimicrobial resistant bacteria in air-exposed silage
Bibliographic record
Abstract
Spoiled silages can harbor pathogenic and antimicrobial-resistant microbes. The potential of some antimicrobial additives to inhibit certain pathogenic and antimicrobial-resistant bacteria in air-exposed silage was measured using pure and mixed bacterial cultures. With pure cultures, laurate and monolaurin (5 mg·mL−1) caused decreases (P < 0.05) of 4 to >7 log10 colony-forming units (CFU)·mL−1 in Listeria monocytogenes and Enterococcus faecalis compared to controls. Ten-fold higher amounts of these inhibitors were needed to equivalently decrease staphylococci. 2-Nitropropanol (1 mg·mL−1) decreased (P < 0.05) E. faecalis and L. monocytogenes 2.9–3.8 and 2.4–7.2 log10 CFU·mL−1 after 6 and 24 h incubations, respectively. In air-exposed whole-plant corn silage the inhibitors caused decreases, although not necessarily significant, of 0.7–2.2 log10 CFU·mL−1 in L. monocytogenes, staphylococci and culturable aerobes after 24 h incubation, with modest yet significant (P < 0.05) inhibition (<0.1–0.3 log10 CFU·mL−1) of yeasts and molds. Tests for carry-over effects against ruminal microbes revealed laurate, monolaurin, and 2-nitropropanol inhibited methanogenesis by >50% (P < 0.05) after 24 h incubation and inhibited L. monocytogenes and enterococci. The antimicrobial activities exhibited by these compounds may yield opportunities to optimize their use to rescue spoiled silages.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".