Potential To Reduce <i>Escherichia coli</i> Shedding in Cattle Feces by Using Sainfoin ( <i>Onobrychis viciifolia</i> ) Forage, Tested In Vitro and In Vivo
Bibliographic record
Abstract
There is a growing concern about the presence of pathogens in cattle manure and its implications on human and environmental health. The phytochemical-rich forage sainfoin (Onobrychis viciifolia) and purified phenolics (trans-cinnamic acid, p-coumaric acid, and ferulic acid) were evaluated for their ability to reduce the viability of pathogenic Escherichia coli strains, including E. coli O157:H7. MICs were determined using purified phenolics and acetone extracts of sainfoin and alfalfa (Medicago sativa), a non-tannin-containing legume. Ground sainfoin or pure phenolics were mixed with fresh cattle feces and inoculated with a ciprofloxacin-resistant strain of E. coli, O157:H7, to assess its viability at -20 degrees C, 5 degrees C, or 37 degrees C over 14 days. Forty steers were fed either a sainfoin (hay or silage) or alfalfa (hay or silage) diet over a 9-week period. In the in vitro study, the MICs for coumaric (1.2 mg/ml) and cinnamic (1.4 mg/ml) acids were 10- to 20-fold lower than the MICs for sainfoin and alfalfa extracts. In the inoculated feces, the -20 degrees C treatment had death rates which were at least twice as high as those of the 5 degrees C treatment, irrespective of the additive used. Sainfoin was less effective than coumaric acid in reducing E. coli O157:H7 Cip(r) in the inoculated feces. During the animal trial, fecal E. coli numbers declined marginally in the presence of sainfoin (silage and hay) and alfalfa silage but not in the presence of hay, indicating the presence of other phenolics in alfalfa. In conclusion, phenolic-containing forages can be used as a means of minimally reducing E. coli shedding in cattle without affecting animal production.
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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) | 0.000 | 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) | 0.000 | 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".