Effects of β-Glucan from Three Different Sources on Glucolipid Metabolism in Mice with Metabolic Syndrome
Bibliographic record
Abstract
Glucan, a common dietary fiber, shows prebiotic potential for managing metabolic disorders, intestinal inflammation, and cardiovascular dysfunction. Although our earlier work found that glucans from various sources differ in molecular weight and linkage, how these variations affect their protective efficacy against metabolic syndrome (MetS) is still unknown. This study examined the effects of β-glucan from barley, highland barley, and oat bran on mice with high-fat diet-induced MetS. Various MetS-related indicators were analyzed, including overweight, dyslipidemia, hypertension, liver function, and host metabolites. Findings revealed that the differential effects of β-glucans primarily occurred in serum and hepatic lipid metabolism. HBBG was most effective at reducing LDL-C and hepatic lipid accumulation. Furthermore, changes in liver inflammation and lipid transport gene expression indicated that HBBG more effectively alleviated hepatic inflammation and regulated CCL2 and CD36 gene expression. These results highlight the source-dependent bioactivity of dietary β-glucans in improving metabolic disorders.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".