Effects of essential oils on proteolytic, deaminative and methanogenic activities of mixed ruminal bacteria
Bibliographic record
Abstract
The objective of this study was to evaluate in vitro the effects of three essential oils (EO) [cinnamon leaf (250 mg L -1 ), garlic oil (100 and 250 mg L -1 ), and juniper berry oil (20 mg L -1 )] and two EO compounds (EOC) [anethol (20 mg L -1 ) and p-cymene (20 mg L -1 )] on proteolytic, deaminative and methanogenic activities of mixed ruminal bacteria. Concentrations of total VFA were similar (P > 0.05) among treatments. With the exception of cinnamon and garlic oils, which reduced (P < 0.05) the proportion of propionate, the other EO and EOC had no effect on the proportions of individual VFA, compared with the control. Proteolytic activity of ruminal bacteria was unaffected (P > 0.05) by treatments; however, bacterial deaminative activity and NH 3 concentration were increased (P < 0.05) by the addition of EO (except for cinnamon leaf oil and garlic oil at 250 mg L -1 ) and EOC. Except for anethol, methanogenic activity of ruminal bacteria was reduced (P < 0.05) by EO and EOC, which was reflected by a marked decrease in methane concentration. This study shows that at the concentrations evaluated, anethol, garlic oil (100 mg L -1 ), juniper berry oil, and p-cymene may not be beneficial to improve efficiency of N utilization in ruminants because they enhance deaminative activity, while cinnamon and garlic oil (250 mg L -1 ) could be good alternatives to antibiotics because they reduce methanogenic activity of ruminal bacteria. Key words: Essential oil, proteolysis, deamination, methanogenesis, in vitro
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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) | 0.000 | 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".