Dietary Fiber and Intestinal Health of Monogastric Animals
Bibliographic record
Abstract
Animal performance, feed efficiency, and overall health are heavily dependent on gut health. Changes in animal production systems and feed regulations away from the use of antibiotic growth promoters (AGP) have necessitated the identification of strategies to optimize gut health in novel and effective ways. Among alternatives to AGP, the inclusion of dietary fibers (DF) in monogastric diets has been attempted with some success. Alternative feedstuffs and coproducts are typically rich in fiber and can be used in the diets to reduce feed costs and optimize gut health. DF are naturally occurring compounds with a diverse composition and are present in all plant-based feedstuffs. DF stimulate the growth of health-promoting gut bacteria, are fermented in the distal small intestine and large intestine to short-chain fatty acids and have beneficial effects on the immune system. Maternal DF supplementation is one novel strategy suggested to have a beneficial programming effect on the microbial and immune development of their offspring. One mechanism by which DF improves gut health is through maintenance of an anaerobic intestinal environment that subsequently prevents facultative anaerobic pathogens from flourishing. Studies with pigs and poultry have shown that fermentation characteristics and their beneficial effects on gut health vary widely based on type, form, and the physico-chemical properties of the DF. Therefore, it is important to have information on the different types of DF and their role in optimizing gut health. This review will provide information and updates on different types of DF used in monogastric nutrition and its contribution to gut health including microbiology, fermentation characteristics, and innate and adaptive immune responses.
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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 | 0.001 |
| 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".