Dietary calcium phosphate content and oat β-glucan influence gastrointestinal microbiota, butyrate-producing bacteria and butyrate fermentation in weaned pigs
Bibliographic record
Abstract
This study aimed to evaluate the effects of oat β-glucan in combination with low- and high-dietary calcium phosphate (CaP) content on gastrointestinal bacterial microbiota, prevalence of butyrate-production pathway genes and fermentation end-products in 32 weaned pigs allocated to four diets: a cornstarch-casein-based diet with low [65% of the calcium (Ca) and phosphorous (P) requirement] and high CaP content (125% and 115% of the Ca and P requirement, respectively); and low and high CaP diets supplemented with 8.95% of oat β-glucan concentrate. Pigs were slaughtered after 14 days, and digesta were collected for quantitative PCR analysis, and quantification of short-chain fatty acids and lactate. The high CaP content reduced gastric lactate and streptococci and propionate in the large intestine. Oat β-glucan distinctly raised gastric bacterial numbers, and colonic lactobacilli and bifidobacteria. Although not reflected by gene copies of butyrate-production pathway genes, oat β-glucan also increased gastric, caecal and colonic butyrate concentrations, which may be favourable for intestinal development in weaned pigs. Thus, a high CaP content negatively affected the intestinal abundance of certain fermentation end-products, whereas oat β-glucan generally enhanced bacterial numbers and activity. The results emphasize the importance of the stomach for bacterial metabolism of oat β-glucan in weaned pigs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".