Glycemic Response to Oat Bran Muffins Treated to Vary Molecular Weight of β‐Glucan
Bibliographic record
Abstract
ABSTRACT Oat bran muffins, containing 4 or 8 g of β‐glucan per two‐muffin serving, were prepared with or without β‐glucanase treatment to produce a range of β‐glucan molecular weights from 130,000 to just over 2 million. Following an overnight fast, the glycemic responses elicited by the untreated and treated muffins was measured in 10 healthy subjects and compared with a control whole wheat muffin. Taken all together, the 4‐g β‐glucan/serving muffins reduced blood glucose peak rise (PBGR) by 15 ± 6% compared with the control. The 8‐g β‐glucan/serving muffins had a significantly greater effect (44 ± 5% reduction compared with the control, P < 0.05). The efficacy of the muffins decreased as the molecular weight was reduced from a 45 ± 6% reduction in PBGR (P < 0.05) for the untreated muffins (averaged of both serving sizes) to 15 ± 6% (P < 0.05) for muffins with the lowest molecular weight. As the molecular weight was reduced from 2,200,000 to 400,000, the solubility of the β‐glucan increased from a mean of 44 to 57%, but as the molecular weight was further decreased to 120,000, solubility fell to 26%. There was a significant correlation (r2 = 0.729, P < 0.001) between the peak blood glucose and the product of the extractable β‐glucan content and the molecular weight of the β‐glucan extracted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".