Effects of Flavonoid-rich Plant Extracts on <i>In vitro</i> Ruminal Methanogenesis, Microbial Populations and Fermentation Characteristics
Bibliographic record
Abstract
The objective of this study was to evaluate the in vitro effects of flavonoid-rich plant extracts (PE) on ruminal fermentation characteristics and methane emission by studying their effectiveness for methanogenesis in the rumen. A fistulated Holstein cow was used as a donor of rumen fluid. The PE (Punica granatum, Betula schmidtii, Ginkgo biloba, Camellia japonica, and Cudrania tricuspidata) known to have high concentrations of flavonoid were added to an in vitro fermentation incubated with rumen fluid. Total gas production and microbial growth with all PE was higher than that of the control at 24 h incubation, while the methane emission was significantly lower (p<0.05) than that of the control. The decrease in methane accumulation relative to the control was 47.6%, 39.6%, 46.7%, 47.9%, and 48.8% for Punica, Betula, Ginkgo, Camellia, and Cudrania treatments, respectively. Ciliate populations were reduced by more than 60% in flavonoid-rich PE treatments. The Fibrobacter succinogenes diversity in all added flavonoid-rich PE was shown to increase, while the Ruminoccocus albus and R. flavefaciens populations in all PE decreased as compared with the control. In particular, the F. succinogenes community with the addition of Birch extract increased to a greater extent than that of others. In conclusion, the results of this study showed that flavonoid-rich PE decreased ruminal methane emission without adversely affecting ruminal fermentation characteristics in vitro in 24 h incubation time, suggesting that the flavonoid-rich PE have potential possibility as bio-active regulator for ruminants.
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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) | 0.001 | 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".