β-Glucans and Resistant Starch Alter the Fermentation of Recalcitrant Fibers in Growing Pigs
Bibliographic record
Abstract
Interactions among dietary ingredients are often assumed non-existent when evaluating the nutritive value and health effects of dietary fiber. Specific fibers can distinctly affect digestive processes; therefore, digestibility and fermentability of the complete diet may depend on fiber types present. This study aimed to evaluate the effects of readily fermentable fibers (β-glucans and resistant starch) on the degradation of feed ingredients containing more persistent, recalcitrant, fibers. Six semi-synthetic diets with recalcitrant fibers from rapeseed meal (pectic polysaccharides, xyloglucans, and cellulose) or corn distillers dried grain with solubles (DDGS; (glucurono)arabinoxylans and cellulose) with or without inclusion of β-glucans (6%) or retrograded tapioca (40%) substituted for corn starch were formulated. Six ileal-cannulated pigs (BW 28±1.4 kg) were assigned to the diets according to a 6×6 Latin square. β-glucan-extract increased apparent total tract digestibility (ATTD) of non-glucosyl polysaccharides (accounting for ~40% of the fiber-fraction) from rapeseed meal (6%-units, P<0.001), but did not affect non-glucosyl polysaccharides from DDGS. Retrograded tapioca reduced ATTD of non-glucosyl polysaccharides from rapeseed meal and DDGS (>10%-units, P<0.001), indicating that the large amount of resistant starch entering the hindgut was preferentially degraded over recalcitrant fibers from rapeseed meal and DDGS, possibly related to reduced hindgut-retention time following the increased intestinal bulk. Fermentation of fiber sources was not only dependent on fiber characteristics, but also on the presence of other fibers in the diet. Hence, interactions in the gastrointestinal tract among fibrous feed ingredients should be considered when evaluating their nutritive value.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".