High molecular weight barley β‐glucan supports bacterial populations beneficial for gut health (647.45)
Bibliographic record
Abstract
Effects of short‐term consumption of barley β‐glucan (BBG) on gut microbiota were investigated using a controlled, single‐blinded, randomized crossover study consisting of four phases of 35 d intervention, each separated by 28d washout intervals. Subjects (n=22) received wheat and rice based control diet (CD), barley diet (BD) with 5g/d low molecular weight (MW) BBG, BD with 3g/d high MW BBG, or BD with 3g low MW BBG. DNA was extracted from feces taken at the end of each phase and the V4 region of bacterial 16S rRNA genes amplified and subjected to illumina sequencing. Data were analyzed using MIXED and GLIMMIX procedures of SAS, PLS‐DA of SIMCA and PERMANOVA of PRIMER. β‐Diversity of microbiota was affected by treatments (P = 0.02) with the most significant difference observed between 3g/d high MW compared to CD. At the phylum level, 3g/d high MW decreased (P < 0.001) Firmicutes (77.7% vs. 88.8%) and increased Bacteroidetes (18.2% vs. 10.0%) compared to CD. Among the core genera, 3 g high MW increased Bactroides (11.3% vs. 6.7%; P = 0.01) compared to CD. Other taxa which were positively associated with 3g high MW BBG included Prevotella , Akkermansia, Rikenellaceae and Lachnospiraceae. Data suggest that consumption of 3g/d high MW BBG supports particular bacterial populations that are beneficial to gut health.
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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) | 0.001 | 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".