The role of added feed enzymes in promoting gut health in swine and poultry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it