Nonstarch polysaccharide-degrading enzymes alter the microbial community and the fermentation patterns of barley cultivars and wheat products in an in vitro model of the porcine gastrointestinal tract
Bibliographic record
Abstract
An in vitro experiment was carried out to assess how nonstarch polysaccharide (NSP)-degrading enzymes influence the fermentation of dietary fiber in the pig large intestine. Seven wheat and barley products and cultivars with differing carbohydrate fractions were hydrolyzed using pepsin and pancreatin in the presence or not of NSP-degrading enzymes (xylanase and β-glucanase) and the filter retentate was subsequently fermented with sow fecal bacteria. Dry matter, starch, crude protein and β-glucan digestibilities during hydrolysis were measured. Fermentation kinetics of the hydrolyzed ingredients were modelled. Short-chain fatty acids (SCFA) production and molar ratio were compared after 12, 24 and 72 h. Microbial communities were analyzed after 72 h of fermentation using terminal restriction fragment length polymorphism. The results showed an increase of nutrient digestibility (P<0.001), whereas fermentability and SCFA production decreased (P<0.001) with addition of the enzyme. SCFA and bacterial community profiles also indicated a shift from propionate to acetate and an increase in cellulolytic Ruminococcus- and xylanolytic Clostridium-like bacteria. This is explained by the increase in slowly fermentable insoluble carbohydrate and the lower proportion of rapidly fermentable β-glucan and starch in the retentate when grains were incubated with NSP-degrading enzymes. Shifts were also different for the four barley varieties investigated, showing that the efficiency of the enzymes depends on the structure of the carbohydrate fractions in cereal products and cultivars.
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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 | 0.000 |
| 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 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".