Enzyme supplements in broiler chicken diets: <i>in vitro</i> and <i>in vivo</i> effects on bacterial growth
Bibliographic record
Abstract
Abstract Increasing the growth performance of broiler chickens by supplementing their diets with exogenous enzymes can also contribute to positive changes in gut health. In this respect the growth of various bacteria normally associated with the gastrointestinal tract of poultry was assessed in vitro using a medium containing arabinoxylan, β‐glucan, guar gum and raffinose and their corresponding enzymes. Overall, enzymes releasing the largest amounts of free sugars yielded the largest increase in bacterial numbers. Accordingly, β‐glucan and raffinose treated with their respective enzymes promoted the largest number of bacterial types, reaching a minimum of 1.0 log10 population within 6 h at 40 °C. A broiler chicken growth trial was also conducted using wheat‐, barley‐ and corn‐based diets with and without enzyme and probiotic addition. Escherichia coli, coliforms, enterococci and aerobic and anaerobic sporeformers were monitored for growth in both the caecum and ileum. Enzyme supplementation reduced E. coli levels in the caecum of broilers fed wheat‐ or corn‐based diets. A further reduction in E. coli numbers was observed in broilers fed the same diets supplemented with a combination of enzyme and probiotic. Enzyme supplementation had much less of an effect on microbial populations in the ileum. Inclusion of probiotics reduced E. coli levels in the caecum and ileum but only in broilers fed wheat‐ and corn‐based diets. Anaerobic spore levels in the ileum increased in all diets containing probiotic. Overall, inclusion of enzymes or probiotics exhibited mixed effects on gut bacteria, depending on the nature of the carbohydrate source and enzyme. Copyright © 2007 Society of Chemical Industry
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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 | 0.000 |
| 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 | 0.001 | 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".