Effects of extracts of <i>Humulus lupulus</i> (hops) and <i>Yucca schidigera</i> applied alone or in combination with monensin on rumen fermentation and microbial populations <i>in vitro</i>
Bibliographic record
Abstract
BACKGROUND: β-Acids in hops (Humulus lupulus) and saponins in yucca (Yucca schidigera) have been found to possess antimicrobial properties similar to that of monensin and could be an alternative to in-feed antibiotics. The effects of monensin (MON) and ethanol extracts of hops (HE) and Y. schidigera (YE) alone and in combination with MON were assessed on ruminal microbial composition and fermentation in vitro of a barley-based diet. RESULTS: All treatments decreased (P < 0.05) CH4 production (per unit of dry matter), microbial protein (mg), and NH3 -N accumulation. All treatments reduced (P < 0.01) the acetate:propionate (A:P) ratio and molar proportions of butyrate, but increased (P < 0.01) those of propionate, whereas those of acetate decreased (P < 0.001) with addition of MON (10 µg mL(-1)) and combined with HE or YE. Methane produced per unit of true digested dry matter decreased (P < 0.001) with all treatments except YE. Monensin reduced (P < 0.001) proportions of 16S rRNA copies of Ruminococcus flavefaciens, but increased (P < 0.01) those of Selenomonas ruminantium. Hops extract alone or combined with MON reduced (P < 0.01) proportions of R. flavefaciens but combined with MON tended (P < 0.1) to increase those of S. ruminantium. Yucca extract combined with MON increased (P < 0.01) the proportions of R. flavefaciens and S. ruminantium. All treatments except MON (2.5 µg mL(-1)) reduced (P < 0.01) the relative abundance of methanogens. CONCLUSION: Hops extract and YE altered rumen microbes and fermentation in a manner similar to MON with many responses being additive when applied in combination.
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) | 0.001 | 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".