The role of added feed enzymes in promoting gut health in swine and poultry
Bibliographic record
Abstract
The value of added feed enzymes (FE) in promoting growth and efficiency of nutrient utilisation is well recognised in single-stomached animal production. However, the effects of FE on the microbiome of the gastrointestinal tract (GIT) are largely unrecognised. A critical role in host nutrition, health, performance and quality of the products produced is played by the intestinal microbiota. FE can make an impact on GIT microbial ecology by reducing undigested substrates and anti-nutritive factors and producing oligosaccharides in situ from dietary NSP with potential prebiotic effects. Investigations with molecular microbiology techniques have demonstrated FE-mediated responses on energy utilisation in broiler chickens that were associated with certain clusters of GIT bacteria. Furthermore, investigations using specific enteric pathogen challenge models have demonstrated the efficacy of FE in modulating gut health. Because FE probably change the substrate characteristics along the GIT, subsequent microbiota responses will vary according to the populations present at the time of administration and their reaction to such changes. Therefore, the microbiota responses to FE administration, rather than being absolute, are a continuum or a population of responses. However, recognition that FE can make an impact on the gut microbiota and thus gut health will probably stimulate development of FE capable of modulating gut microbiota to the benefit of host health under specific production conditions. The present review brings to light opportunities and challenges for the role of major FE (carbohydrases and phytase) on the gut health of poultry and swine species with a specific focus on the impact on GIT microbiota.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".