Optimization of RUSITEC model adaptations for in vitro simulation of subacute ruminal acidosis and evaluation of Saccharomyces cerevisiae supplementation on microbial fermentation and stability
Bibliographic record
Abstract
There is a growing need for alternative research models to reduce or replace animal use in feed and feed additive testing. In vitro model development could provide value added data in the assessment of feed additives for microbial modulation in cattle where animal variation in intake can impact results. This study aimed to refine and evaluate the in vitro model of subacute ruminal acidosis (SARA) and assess how variations in buffer and feed additive supplementation could influence microbial fermentation and community composition. Using the rumen simulation technique (RUSITEC) with a SARA model, buffer dilution and feed additive supplementation were compared for their influence on microbial fermentation and community composition. Results showed that buffer dilution, combined with dietary changes, effectively induced SARA in vitro , with a significant impact on ruminal pH and fermentation parameters. Acetate production and pH were lower in reactors supplemented with Saccharomyces cerevisiae (AY), especially under low buffering conditions, suggesting AY’s potential to mitigate ruminal dysbiosis. Microbial protein and protozoa abundance also changed in response to dietary and buffer adjustments. The results of this study support the use of in vitro models for more controlled, reproducible testing of feed additives, with potential applications in improving nutritional interventions and rumen microbial stability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".