Does supplementing β-mannanase modulate the feed-induced immune response and gastrointestinal ecology in poultry and pigs? An appraisal
Bibliographic record
Abstract
The provision of adequate and balanced nutrients is critical for efficient and profitable animal protein production. However, non-nutritive components in feedstuffs can elicit responses that can negatively impact nutrient utilization efficiency. For example, dietary β-mannans are recognizable by cell surface mannose receptors are pivotal for diverse cellular functions. This review will evaluate the physiological implications of dietary native β-mannans, the utility of supplemental feed β-mannanase in hydrolyzing β-mannans, and subsequent metabolic responses. Dietary native β-mannans have been implicated in inadvertent stimulation of immune response through a phenomenon called the feed-induced immune response (FIIR), that has been associated with intestinal inflammation and depression in animal performance. Supplemental β-mannanase blunted the FIIR by hydrolyzing native β-mannans to smaller fragments with a reduced ability to stimulate the innate immune system as indicated by the modulation of oxidative stress, mucosal permeability, and blood concentration of acute phase proteins and immunoglobulins in broilers and piglet models. Moreover, β-mannanase hydrolysis of native β-mannans to mannooligosaccharides (MOS) impacted gastrointestinal microbial ecology. Indeed, β-mannanase-derived MOS reduced the concentration of pathogenic bacteria such as Escherichia coli and Salmonella and increased the production of short-chain fatty acids in gastrointestinal tracts of various animal models. Consequently, by hydrolyzing native β-mannans, supplemental β-mannanase may have nutritional, metabolic, and microbial ecology benefits. In summary, integrating multi-functional feed additives such as β-mannanase into feeding programs for monogastric animals will be critical for efficient and sustainable animal protein production in the context of evolving challenges such as the mandated elimination of use of antibiotics for growth promotion.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".