Characterization of microbial intolerances and ruminal dysbiosis towards different dietary carbohydrate sources using an in vitro model
Bibliographic record
Abstract
AIM: This study aimed to characterize the critical points for determining the development of dysbiosis associated with feed intolerances and ruminal acidosis. METHODS AND RESULTS: A metabologenomics approach was used to characterize dynamic microbial and metabolomics shifts using the rumen simulation technique (RUSITEC) by feeding native cornstarch (ST), chemically modified cornstarch (CMS), or sucrose (SU). SU and CMS elicited the most drastic changes as rapidly as 4 h after feeding. This was accompanied by a swift accumulation of d-lactate, and the decline of benzoic and malonic acid. A consistent increase in Bifidobacterium and Lactobacillus as well as a decrease in fibrolytic bacteria was observed for both CMS and ST after 24 h, indicating intolerances within the fibre degrading populations. However, an increase in Lactobacillus was already evident in SU after 8 h. An inverse relationship between Fibrobacter and Bifidobacterium was observed in ST. In fact, Fibrobacter was positively correlated with several short-chain fatty acids, while Lactobacillus was positively correlated with lactic acid, hexoses, hexose-phosphates, pentose phosphate pathway (PENTOSE-P-PWY), and heterolactic fermentation (P122-PWY). CONCLUSIONS: The feeding of sucrose and modified starches, followed by native cornstarch, had a strong disruptive effect in the ruminal microbial community. Feed intolerances were shown to develop at different rates based on the availability of glucose for ruminal microorganisms. SIGNIFICANCE AND IMPACT OF THE STUDY: These results can be used to establish patterns of early dysbiosis (biomarkers) and develop strategies for preventing undesirable shifts in the ruminal microbial ecosystem.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".